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digitaldataera · 4 years ago

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Learn About Different Tools Used in Data Science

Data Science is a very broad spectrum and all its domains need data handling in unique way which get many analysts and data scientists into confusion. If you want to be pro-active in finding the solution to these issues, then you must be quick in making decision in choosing the right tools for your business as it will have a long-term impact.

This article will help you have a clear idea while choosing the best tool as per your requirements.

Let's start with the tools which helps in reporting and doing all types of analysis of data analytic and getting over to dashboarding. Some of the most common tools used in reporting and business intelligence (BI) are as follows:

- Excel: In this you get wide range of options which includes Pivot table and charts, with which you can do the analysis more quickly and easily.

- Tableau: This is one of the most popular visualization tools which is also capable of handling large amounts of data. This tool provides an easy way to calculate functions and parameters, along-with a very neat way to present it in a story interface.

- PowerBI: Microsoft offers this tool in its Business Intelligence (BI) Space, which helps in integrations of Microsoft technologies.

- QlikView: This is also a very popular tool because it’s easy to learn and is also a very intuitive tool. With this, one can integrate and merge, search, visualize and analyse all the sources of data very easily.

- Microstrategy: This BI tool also supports dashboards, key data analytics tasks like other tools and automated distributions as well.

Apart from all these tools, there is one more which you cannot exclude from this tool's list, and that tool is

- Google Analytics: With google analytics, you can easily track all your digital efforts and what role they are playing. This will help in improvising your strategy.

Now let's get to the part where most of the data scientists deal with. The following predictive analytics and machine learning tools will help you solve forecasting, statistical modelling, neural networks and deep learning.

- R: It is very commonly used language in data science. You can access its libraries and packages as they are easily available. R has also a very strong community which will you if you got with something.

- Python: This is also one of the most common language for data science, or you can also say that this is one the most used language for data science. It is an open-source language which makes it favourite among data scientists. It has gained a good place because of its ease and flexibility.

- Spark: After becoming open source, it has become one of the largest communities in the world of data. It holds its place in data analytics as it offers features of flexibility, computational power, speed, etc.

- Julia: This is a new and emerging language which is very similar to Python along-with some extra features.

- Jupyter Notebooks: This is an open-source web application widely used in Python for coding. It is mainly used in Python, but it also supports R, Julia etc.

Apart from all these widely used tools, there are some other tools of the same category that are recognized as industry leaders.

- SAS

- SPSS

- MATLAB

Now let's discuss about the data science tools for Big Data. But to truly understand the basic principles of big data, we will categorize the tools by 3 V's of big data:

· Volume

· Variety

· Velocity

Firstly, let's list the tools as per the volume of the data.

Following tools are used if data range from 1GB to 10GB approx.:

- Microsoft Excel: Excel is most popular tool for handling data, but which are in small amounts. It has limitations of handling up to 16,380 columns at a time. This is not a good choice when you have big data in hand to deal with.

- Microsoft Access: This is also another tool from Microsoft in which you handle databases up to 2 Gb, but beyond that it will not be able to handle.

- SQL: It has been the primary database solution from last few decades. It is a good option and is most popular data management system but, it still has some drawbacks and become difficult to handle when database continues to grow.

- Hadoop: If your data accounts for more than 10Gb then Hadoop is the tool for you. It is an open-source framework that manages data processing for big data. It will help you build a machine learning project from starting.

- Hive: It has a SQL-like interface built on Hadoop. It helps in query the data which has been stored in various databases.

Secondly, let's discuss about the tools for handling Variety

In Variety, different types of data are considered. In all, data are categorized as Structured and Unstructured data.

Structured data are those with specified field names like the employee details of a company or a school database or the bank account details.

Unstructured data are those type of data which do not follow any trend or pattern. They are not stored in a structured format. For example, the customer feedbacks, image feed, video fee, emails etc.

It becomes really a difficult task while handling these types of data. Two most common databases used in managing these data are SQL and NoSQL.

SQL has been a dominant market leader from a long time. But with the emergence of NoSQL, it has gained a lot of attention and many users have started adopting NoSQL because of its ability to scale and handle dynamic data.

Thirdly, there are tools for handling velocity.

It basically means the velocity at which the data is captured. Data could be both real time and non-real time.

A lot of major businesses are based on real-time data. For example, Stock trading, CCTV surveillance, GPS etc.

Other options include the sensors which are used in cars. Many tech companies have launched the self-driven cars and there are many high-tech prototypes in cue to be launched. Now these sensors need to be in real-time and very quick to dynamically collect and process data. The data could be regarding the lane, it could be regarding the GPS location, it could be regarding the distance from other vehicles, etc. All these data need to be collected and processed at the same time.

So, for these types of data following tools are helping in managing them:

- Apache Kafka: This is an open-source tool by Apache and is quick. One good feature of this tool is that this is fault-tolerant because of which this is used in production in many organisations.

- Apache Storm: This is another tool from Apache which can used with most of the programming language. It is considered very fast and good option for high data velocity as it can process up to 1 Million tuples/second.

- Apache Flink: This tool from Apache is also used to process real-time data. Some of its advantages are fault-tolerance, high performance and memory management.

- Amazon Kinesis: This tool from Amazon is a very powerful option for organizations which provides a lot of options, but it comes with a cost.

We have discussed about almost all the popular tools available in the market. But it’s always advisable to contact some data science consulting services to better understand the requirements and which tool will be best suitable for you.

Look for the best data science consulting company which would best suit in your requirements list.

#data science #data science consulting services #best data science consulting company #data science consultant

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matlabhwexperts-blog · 7 years ago

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Vehicle Network In Matlab Assignment Help

https://www.matlabhomeworkexperts.com/vehicle-network-in-matlab.php

Vehicle Network is one of the fields which can be considered as highly specialized because it deals with the problem specific statements. Vehicle Network Toolbox provides connectivity to CAN devices from MATLAB and Simulink using industry-standard CAN database files. We at MatlabHomeworkExperts have a highly qualified pool of Vehicle Network experts. Our tutors are highly qualified and experienced at solving various college level MATLAB Vehicle Network assignments, university level MATLAB Vehicle Network projects. The Vehicle Network experts and Vehicle Network tutors associated with us are highly qualified and proficient in all the domains. Our Vehicle Network solvers and Vehicle Network experts provide high quality solution so that students can fetch highest grades in their academics. Our experts can solve Vehicle Network assignments within few hours as well. We at Matlab Homework Experts provide you with details of all the topics mentioned below. Along with these major topics, our online Vehicle Network experts provide solutions to all the sub topics studied under Vehicle Network.

CAN Bus Communication from MATLAB and Simulink

CAN Channel Message Filtering

CAN Message Reception Callback Functions

Create and Use J1939 Parameter Groups

Event Triggered CAN Message Transmission

Log and Replay CAN Messages

Manage CAN Message Data in a GUI

Parse Raw CAN Messages and Data

Periodic CAN Message Transmission

Set up Communication Between Host and Target Models

Transmit and Receive CAN Messages

Using A2L Description Files

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sabahparveen · 3 years ago

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Sabah Parveen on Computer Engineering

Sabah Parveen developed algorithms and worked on signal processing for modem and network systems.

Created simulations of modem and network performance and worked on channel estimation, equalization and coding theory.

Experience with start to end product development from conceptualising to deployment.

Sabah Parveen worked on WSN technology and bringing state of the art in the development cycle

Looking for a leadership role, where in you Innovate every day and do things beyond your current capabilities.

Hands on experience on sensor-related and display-related sub-systems across various LVE systems, including telematics, wireless networking, in-cabin radar and camera monitoring system for vehicle entry and security.

Collaborated with hardware architecture, design and software teams to create comprehensive characterization plans to surface key performance metrics, locate bottlenecks and chokepoints.

Identified under-design scenarios related to application requirements.

Executed characterization plans, develop analytics, and present clear validation metrics and associated reports to help drive architecture planning and decision-making.

Experience in building test equipment, benchtop mock-ups, prototypes, and POCs as necessary to evaluate sub-system performance, and investigate characterization of future-facing hardware options.

Collaborated and Contributed to discussions, evaluation and design review of next generation architectures, leveraging insights from performance characterization efforts and worked on monitoring in-system performance of h/w in the field through telemetry and analytics to provide additional insights for next generation architectures.

Sabah Parveen expertise in Digital Signal Processing, Wireless Communications, and wireless chip development. Solid grasp of complex wireless systems with strong capability to comprehend dependencies between system components and protocol layers and their interactions. Hands-on SW programming skills for modeling and simulation of sophisticated systems from RF/PHY layer signals and channel modeling all the way to MAC protocol/networking simulations.

Proficient with Matlab. Experienced with fixed-point design and RTL vector generation. Experience with handling and processing large amounts of field and simulation data, including user-friendly visualization of complex test setups and simulation results such as measuring accuracies and system latencies. Worked with common analog/RF impairments encountered in wireless systems.

Working knowledge of industry standards such as Bluetooth, Ultra-Wideband and GPS wireless protocols.

Proficient with FPGA bring-up, PHY and MAC testing and debugging.

Experienced in camera and image signal processing.

Sabah Parveen worked on end to end architecture and design for a developing network and responsible for the operation of this network fabric and the optical network.

Created simple processes that help operate and build network. Worked closely with our internal customers to help alleviate their problems and ensure our network continues to meet their demands.

Sabah Parveen worked on new designs and solutions, bringing them from concept to in life operations. Created and updated network standards and ensured that the network adheres these standards.

Reviewed and implemented changes on the network. Involved with our automation teams to assist in defining the tools we require to drive operational projects and to drive improvements in our network quality and reliability.

Troubleshooted complex problems and developed innovative solutions on network. Worked with complex technologies including optical engineering.

Have strong written and verbal communication skills, strong project management and time management skills. Sabah Parveen delivered solutions and troubleshooting complex network problems and designing simple innovative solutions.

#Sabah Parveen

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keerthana06 · 4 years ago

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Matlab Projects With Source Code

Millions of engineers and scientists worldwide use Matlab projects with source code to research and style the systems and products transforming our world. Such huge usage results in some very interesting prospects in designing. This list of 20 MATLAB projects ideas ranges over a number of the solutions that use or can use MATLAB. After all, the list of applications of such software is endless.

Takeoff Projects helps students complete their academic projects. You can enroll with friends and receive Matlab Projects With Source Code kits at your doorstep. You can learn from experts, build the latest projects, showcase your project to the world and grab the best jobs. Get started today!

1. Vehicle Number Plate Detection Using MATLAB

The project presented here is often wont to detect a vehicle’s number plate from the pictures stored during a database. That is, it aims at detecting the car place of a vehicle then extracting the knowledge regarding that vehicle using MATLAB software.

2. Equipment Controller Using MATLAB-Based GUI

during this project, a MATLAB platform to manage up to four electrical equipment is presented.

3. Logging Sensor Data in MS EXCEL through MATLAB GUI

This project presents a MATLAB graphical user interface-based approach many |to avoid wasting to save lots of lots of real-time process data obtained from a temperature sensor. The GUI allows the user to graphically view the temperature variation at the highest of sensor data acquisition.

4. Light Animations Using Arduino and MATLAB

during this project, a MATLAB-based GUI approach to regulate the glowing pattern of a variety of LEDs is made. the utilization of GUI is advantageous since the user can control the lighting patterns while performing other tasks on the PC.

5. Audio Compression using Wavelets in MATLAB

Audio frequencies range from 20Hz to 20kHz but these frequencies aren't heard in the same way. Frequencies below 20Hz and above 20kHz are very difficult to concentrate on. we frequently got to process these audio signals for various applications. MATLAB is one of the simplest signal analysis and signal processing tools.

6. Automatic Certificate Generation Using MATLAB

Presented here may be a MATLAB code to get certificates for workshops, conferences, symposiums, etc. This MATLAB code is often extended to urge analysis reports for large data sets also.

7. Image processing using MATLAB

during this series of 4 articles, fundamentals, also as advanced topics of image processing using MATLAB, are discussed. The articles cover basic to advanced functions of MATLAB’s image processing toolbox (IPT) and their effects on different images.

8. Lossless compression

Cameras are nowadays being given more and more megapixels to enhance the standard of captured images. With improvement in image quality, the dimensions of the image file also increase. one among the applications of compression with MATLAB employing a graphical interface is described during this project. This project proposes a way to compress the captured image to scale back its size while maintaining its quality.

9. Huffman Encoding and Decoding

Encoding the knowledge before transmission is important to make sure data security and efficient delivery of the knowledge. Huffman algorithm may be a popular encoding method utilized in transmission systems. it's widely utilized in all the mainstream compression formats that you simply might encounter. The project here encodes & decodes the knowledge and outputs the values of entropy, efficiency & frequency probabilities of characters.

10. Artificial Neural Network Simulation

a man-made neural network, in essence, is an effort to simulate the brain. When the user input and expected output, the program trains the system to offer a final weight. the ultimate weight is computed to urge the ultimate expected output. This program helps us to know the fundamentals of artificial neural networks and the way one can use them for further applications.

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juniperpublishers-robotics-blog · 6 years ago

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Adaptive Control for Quadrotor UAVs Considering Time Delay: Study with Flight Payload- Juniper Publishers

Abstract

Design of effective control for unmanned aerial vehicles (UAVs) requires consideration of several sources of uncertainty. These undesired uncertainties affect the flight stability and performance in an unpredictable manner. The existence of transmission delays caused by wireless communication and payload variation are among such critical challenges. Adaptive control (AC) can lead to high performance tracking in the presence of uncertainties. This paper presents the application of model reference adaptive control (MRAC) to quadrotor types of UAVs considering the time delay in the altitude control system. MATLAB system identification tool is applied to obtain the altitude motion model, without time delay, for the quadrotor. Proportional-plus-velocity (PV) and PV-MRAC altitude control systems are designed, by incorporating an estimated constant time delay. The designed controllers are validated using simulations and flight tested in an indoor environment. The robustness of the PV-MRAC controller is tested against the baseline non-adaptive PV controller using the quadrotor’s payload capability..

Keywords: Adaptive control; Flight payload; Flight stability; MIT rule; PV control; Quadrotor; Time delay

Abbreviations : UAV: Unmanned Aerial Vehicles; AC: Adaptive Control; MRAC: Model Reference Adaptive Control; PV: Proportional-Plus-Velocity; UDP: User Datagram Protocols; CTA: Continuous Time Approximation; DDE: Delay Differential Equations; LTI : Linear Time-Invariant

Introduction

The application of autonomous control techniques to quadrotor types of unmanned aerial vehicles (UAVs) has been the focus of active research during the past decades. Design of control for aircraft requires several important considerations. There are numerous sources of uncertainty. For example, devices are ageing and wearing (e.g., actuator degradation), external disturbances, control input saturation, payload fluctuations, and potentially uncertain time delays in processing or communication [1]. These undesired nonlinearities affect the flight stability and performance of the controlled systems. Adaptive control (AC) is a candidate to resolve the issues, because of its ability to generate high performance tracking in the presence of uncertainties [1].

Safe, cooperative transportation of possibly large or bulky payloads by UAVs is extremely important in various missions, such as military operations, search and rescue, and personal assistants. Another huge area of application is in the service industry, where commercial quadrotor types of UAVs or drones are being used in the delivery of packages of varying masses. There are several substantial challenges and open research questions related to this application. For example, this task requires that hovering vehicles remain stable and balanced in flight as payload mass is added to the vehicle. If payload is not loaded centered or the vehicle properly trimmed for offset loads, the drone experiences bias forces that must be rejected [2]. Flying with a suspended load, known as slung load or sling load, can be a very challenging and sometimes hazardous task because the suspended load significantly alters the flight characteristics of the quadrotor [3,4].

Time delays exist in autonomous dynamic systems when signals are transmitted wirelessly. One of the challenges in designing effective control systems for UAVs is existence of signal transmission delay. The delay has nonlinear effects on the flight performance of autonomously controlled UAVs. For autonomous aerial robots, typical values of the time delay have been known to be around 0.4±0.2s [5] or 0.2s [6]. For large delays (e.g., larger than 0.2 s) the system response might not be stabilized or converged due to the dramatic increase in torque. This poses a significant challenge [7]. Since its effect is not trivial the delay need be estimated and considered in designing controllers.

Parrot AR. Drone 2.0, a commercialized drone, is controlled via WiFi network. Data streaming, commanding and reading the vehicle sensors are done using user datagram protocols (UDPs). The drone dynamics contains a time delay due to such communication. Refer to Section 2 for the drone's altitude control architecture. The overall time delay is attributed to:

I. The processing capability of the host computer,

II. The electronic devices processing the motion signals,

III. The measurement reading devices, e.g., the distance between the ultrasonic altitude sensor and the ground, and

IV. The software on the host computer for implementing the controllers.

For UAVs, wireless communication delays may not be critical when the controllers are on board. However, delays have significant effects when the control software is run on an external computer and signals are transmitted wirelessly. For example, the experiments presented in this paper were conducted using MATLAB/Simulink on an external computer, and decoding process of the navigation data (yaw, pitch, roll, altitude, etc.) contributes to the delay. Also, the numerical solvers introduce additional delay. Estimating delays is a challenging problem and has been an area of great research interest in fields as diverse as radar, sonar, seismology, geophysics, ultrasonic, controls, and communications [8,9]. A considerable number of system identification methods have been reported in the literature [10,11]. However, most of the existing methods for transfer function identification do not consider the process delay (or dead-time) or just assume knowledge of the delay [12]. Even though considerable efforts have been made on parameter estimation, there are still many open problems and there is no common approach in time-delay identification due to difficulty in formulation [13]. Several approaches for estimation of delay have been introduced in the literature [14-18]. In [14] for example, the finite dimensional Chebyshev spectral continuous time approximation (CTA) was used to solve delay differential equations (DDEs) for estimation of constant and time-varying delays.

In this paper, the constant delay applied in the drone's altitude control system was estimated using an approach based on analytical solutions to DDEs [19,20]. The time delay has been estimated as 0.36s in [21,22]. The altitude dynamics was assumed to be linear time-invariant (LTI) first order and the time delay, assumed to be single constant, was incorporated into the model as an explicit parameter. Experimental data and analytical solutions of infinite dimensional continuous DDEs were used for estimation. Measured transient responses were compared to time domain descriptions obtained by using the dominant characteristic roots based on the Lambert W function written in terms of system parameters including the delay. Proportional (P) time-delay control system, retarded type, was used to generate the responses for estimation of the delay.

The contribution of this paper is the identification of altitude motion model, without time delay, for the drone and the extension of incorporating the estimated time delay of 0.36 sin the design of an AC system. Then, validating the controller using the payload capability of the quadrotor. A model reference adaptive control (MRAC) based on the MIT rule is designed. In general, the MIT rule cannot guarantee stability [23], so the MRAC is combined with a tuned proportional-plus- velocity (PV) controller. To validate the robustness of the PV- MRAC controller, the results are compared to a baseline nonadaptive PV-controller.

This paper continues with the description of quadrotor's altitude dynamics and the AR. Drone 2.0 control system in Section 2. The altitude model identification and the design of the PV and PV-MRAC control systems are presented in Section 3. Simulations and experiments setups are presented in Section 4. In Section 5results are summarized. Concluding remarks and future work are presented in Section 6.

Quadrotor Altitude Dynamics and Control System

The notation for a typical quadrotor model is shown in Figure 1. Quadrotors has three main coordinate systems attached to it; the body-fixed frame,{b},the vehicle frame, {v} and the global inertial frame,{ w }. There are two other coordinate systems, not shown, that are of interest, the vehicle-1 frame,{v1} ,and vehicle-2 frame, {v2} [24,25]. The configuration of a quadrotor is represented by six degrees of freedom in terms of position, (xw ,yw,zw)T and the attitude defined using the Euler angles (roll, pitch, and yaw), (Øv2,θv1,φv)T, which gives a 12-state equation of motion [26]. The AR. Drone quadrotor platform adopted for this work has the same architecture design shown in Figure 1.

The quadrotor has four rotors, labelled 1 to 4, mounted at the end of each cross arm. The rotors are driven by electric motors powered by electronic speed controllers. Each rotor is controlled independently by nested feedback loops as shown in Figure 2. The inner attitude control loop uses on-board accelerometers and gyros to control the roll, pitch, and yaw while the outer position control loop uses estimates of position and velocity of the center of mass to control the trajectory in three dimensions [27]. The components of angular velocity of the robot in the body frame are p, q, and r.

The vehicle's total mass is m and d is distance from the motor to the center of mass. The total upward thrust,u(t) , on the vehicle is given by Where ωi(t) is the rotore angular speed and KF>0 is the thrust constant [26]. In addition to forces, each rotor produces a moment perpendicular to the plane of rotation of the blade, Mi(t) .The equation of motion in the z-direction can be obtained as [24,27]

Linearizing (1) at an operating point that corresponds to a nominal hover state, z0 , where the roll and pitch angles are small and the nominal thrusts, F0 , from the propellers satisfy . At the hover state the average motor angular speed, F0= mg , is given by Then, the change in the vertical acceleration can be derived as [27-29]

Thus, only the rotor's average angular speed, ωF(t), necessary to generate F (t) needs to be controlled to regulate the altitude, z(t) , of the quadrotor, since m ,and kF are g constants.

According to the AR. Drone 2.0 SDK documentation, z(t) is controlled by applying a reference vertical speed , as control input. The motion commands for the drone are encoded under a specific protocol, where high-level control signals, including , are normalized between -1 and 1to prevent damage. The drone's flight management system sampling time, Ts , is 0.065 s, which is also the sampling time at which the control law is executed and the navigation data received.

The control block diagram for the drone's altitude motion regulation is shown in Figure 3. The desired height and the actual system response are denoted zdes(t) and z(t) , respectively. The dynamics is implemented using (2), used to determine ωf(t) from the actual drone vertical speed, z(t) . The rotors rotate at equal speed,ωf(t) , which will generate F(t) to make z (t) reach the reference (zdes (t) = 1m). These computations take place in the on-board control system programmed in C. The simulation setup used is shown in Figure 4. The overall time delay,Td, single constant, in the system is represented as actuator time delay, as an explicit plant parameter. G(s) is the plant model for the combined motor and rigid body altitude dynamics without uncertainty effect.

Altitude Model and Design of Controllers

Altitude Model

Quadrotor altitude motion has second-order dynamics (Equation 1 & 2). Black-box approach was used to determine the model for the drone altitude control. MATLAB system identification toolbox App was applied to obtained LTI SISO ARX transfer function, stable system, without time delay, as

Measured data, recorded from the drone's altitude motion response, was imported into the App for the modeling process.The input data was the reference vertical speed, , constrained to [-1 1] m/s, and the output data was the vertical height, z(t). A suitable P-feedback controller was used to obtain the data. The model has a fit to estimation (best fit) of91.04%, final prediction error (FPE) of 0.000200948, and mean squared error (MSE) of 0.0001864.The system is completely state controllable and completely observable. See Figure 5 for the open-loop unit step response of the plant.

Non-adaptive control: PV

The conceptual application of this control strategy is common in angular position of DC motors, typically the control of robot manipulators [30]. PV-control, unlike proportionalplus-derivative (PD) control, does not induce numerator dynamics. The setup of the PV control system is shown in Figure 6, where Kp and Kv are the proportional (position) and velocity (derivative) gains, respectively. The transfer function of the time-delay closed-loop system is given as

Note, if G(s)= 1/s, a first order plant, used in [19,20] for the estimation of the delay, then this time-delay system will be neutral type, see [21,22] for analysis of such control system. Also, if G(s)= 1/s and then we have a P-control,retarded type time-delay, system.

Adaptive control: PV-MRAC

The primary function of adaptive controller is to accommodate uncertainties, which may introduce nonlinearities on the system dynamics. There are several broad categories of AC, and in this brief MRAC is applied. The type of MRAC depends on the adjustable mechanism, and one of the modern ones is the MIT rule. The setup of the MIT rule MRAC is shown in Figure 7, where Gp(s) and Gm(s) are transfer functions of the plant (including the uncertainties) and the reference model respectively, and yp(t) and ym(t) are their respectively outputs.

The reference signal is r(t) , is an updating parameter, γ> 0 is a tuning parameter, and the control input signal, u(t) — θ(t)r(t) , where n is the order of the system. The constant k for the plant is unknown (e.g. is the effect of quadrotor payload fluctuations or time-delay or both).CO is chosen as multiplied of Gm(s)by a desired constant, ka , so that and can matched. The tracking error, e(t)=yp(t)-ym(t),given by

Next is to define a cost function, J [θ(t)] , in terms of e(t) . The goal here is to adjust θ(t) to minimize J related to e(t) , so that as t increases e(t) → 0 . One simplest way is to expressed [23,31-34]. The rule employs a feed-forward gain adaption to update θ , as shown below [23,32-34].

Where, is sensitivity derivative of the system, and it is evaluated under assumption that θ varies slowly [23,33,34].

Now, θ(t)=[θ1(t)θ2(t)] since quadrotor altitude motion dynamics is a second-order system, then . The reference model is selected based on desired transient specifications, given in the form as [23,32]

where r(t)=zdes(t). Substituting into (8), and simplifying, we obtain

And substituting (9) into (5), and then differentiating with respect θ1(t) and θ2(t) , to obtain

If the reference model is close to the plant, then their characteristic equations can be equally approximated ,then the MIT update rule, from (6) and (10), can be written as

It is important to note that the MIT rule by itself does not guarantee error convergence or stability [23,32-34], so the MRAC is combined with a tuned PV controller. An MRAC designed using the MIT rule is very sensitive to the amplitudes of the signals. As a general rule, the value of γ is kept small.Tuning of γ is crucial to the adaptation rate and stability of the control system [23,32-34].

Simulations and Experiments

The MATLAB/Simulink program was developed for the experiments. The vertical speed control input constraints, [-1,1] ms(-1) , are applied using the saturation block. The estimated time delay of 0.36s obtained in [21,22] was implemented using the transport delay block. The experiments were conducted in an office environment with the drone's indoor hull attached. The drone is connected to the host PC through WiFi (Figure 8). Data streaming, commanding and reading, are done using UDPs. UDP is a communication protocol that offers a limited amount of service when messages are exchange between computers in a network that uses IP.

The drone navigation data (from the sensors, cameras, battery, etc.) are received, and the control signals are sent, using AT commands. AT commands are combination of short text strings sent to the drone to control its actions. The drone has ultrasound sensor (at the bottom) for ground altitude measurement. It has 1GHz 32-bit ARM Cortex A8 processor, 1GB DDR2 RAM at 200MHz, and USB 2.0 high speed for extensions. The drone�s total mass (including the indoor hull), m, is 453g.

Figure 9 shows the Simulink diagram developed for conducting the PV control system simulations. For the PV control, a high pass filter (HPF) with damping ratio, was used for the derivative controller. A natural frequency value, ωf =38 rad/s, for the filter was selected by tuning and the use of the Bode plot. The filter�s cut off frequency was determined as 0.90 Hz. Figure 10 shows the Simulink diagram developed to simulate a unit-step response for the PV-MRAC control system. The same HPF was applied in the control system.

Flight payload: disturbance rejection

To demonstrate the robustness of the PV-MRAC controller, it was tested against the PV controller using the payload capability of the drone. To accomplish this, the three cases below were considered.

Case 1: point-mass load: before flight: Masking tape was used to attach point-mass loads at the top of the drone before (Figure 11). The controllers were tested using different masses. This setup was used to determine the stability bounds within which the changing mass-inertia parameters of the system due to the acquired payload will not destabilize the drone. The mass is assumed to be close to the drone�s center of mass.

Case 2: cable-suspended point-mass load: Here, a 100g point-mass load was attached to the end of an approximately 45cm length of rope, which was hooked to the drone before flight (Figure 12). Masking tape was used to hold firm the rope across the top and the bottom of the drone. In this second case, apart from the drone carrying an extra payload, the suspension system created an oscillating (pendulum) disturbances to the system. The rope is assumed to be massless and is attached to the drone�s center of mass.

apart from the drone carrying an extra payload, the suspension system created an oscillating (pendulum) disturbances to the system. The rope is assumed to be massless and is attached to the drone's center of mass.

Case 3: point-mass load: during flight: In this setup, the designed controllers were used to demonstrate the stability behavior of the drone undergoing a range of instantaneous step payload changes. Here, the masses were attached to a sticking masking tape at the top of the drone, after it has stabilized to the desired height, =1m, see Figure13.

Results and Discussion

The drone response contains a 'dead-time' due 1) time delay in the control system and 2) the time between when the realtime application from MATLAB/Simulink is connected to the drone and when the controller is switched on. Consequently, for ease of analyzing the experiments data, some of the drone's responses plots are shifted to start at (0s,0m). For example, see Figure 14 for the drone altitude responses using a P-control system.

Note from Figure 14 that, for a P-control system, if there is no delay ( T d=0) in the system and KP > 0 , there should be no overshoot in the response. Also, increase in Kp should not destabilize the system. However, as seen in Figure 14, when Kp >1, the output has a nonzero overshoot. Moreover, when Kp ≥ 5 , the amplitude of the response grows over time and the system becomes unstable. This is partly due to the time delay in the system, which introduces nonlinearity on the dynamics. Therefore, the delay does have nontrivial effects on response and need be precisely estimated and considered in designing effective control.

Non-adaptive control: pv

The controller gains were obtained by tuning. Figure 14 & 15 show the simulation and experimental responses, respectively. It can be observed that as the value kv value increases Mo decreases, but the responses becomes slower. The PV controller with kv= 0.3 and kp=2-0 gives the best stable control system performance, with rise time tr (10%-90%range) of 1.30 s and M0 = 2.03% .The results in Figure 16 and Table 1 compares simulated and experimented with and without the time-delay effects on the control system, with Kv = 03 and kv=2-0It can be observed from the rise time values the importance of incorporating the time delay into the design of the control system.

MATLAB-based software package [35] was used to study the equation, 1+(kp+KvS)e-stdG(s) =0 , from (4). The closed-loop system stability of the time-delay system, by solving the characteristic characteristic roots within a specified region are then plotted for various kv values with k p= 2.0 . Figure 17 shows the shows the spectrum distribution of the characteristic roots. When the rightmost (i.e., dominant) roots for each case are considered k v- 0.3 the value yields the most stable rightmost root ( spectrum distribution of the characteristic roots. When the R(s)=-3.10).

Adaptive control: PV-MRAC

Figure 18&19 show simulated and experimented responses, respectively, for different values. It can be seen that, increasing γ makes the response faster but it introduces oscillations, while decreasing γmakes the response slower leading to an unstable system. The results show that there is a range of values of to achieve stabilization, assuming the other tuning parameters remains constant. The results in Figure 20 and Table 2 compares simulated and experimented with and without the time-delay effects. Here again, the results show the effect of including the time delay in the control systems design. Note that, using the PV controller and MRAC controller parameters on their own never stabilized the system.

Flight payload: disturbance rejection

Figure 21-23 show the responses for the three cases considered in introducing disturbances to the drone, see Section 4.1. In general, the results confirm that the PV-MRAC offers several benefits over the fixed-gain approach, PV controller. The PV-MRAC was found to offer enhanced robustness to the change in mass uncertainty and the oscillating disturbances.

Case 1: point-mass load: before flight

Figure 21 shows the altitude responses for the PV and PV-MRAC controllers for the case of attaching 130g point- mass load at the top of the drone. For the PV-MRAC, it was observed that beyond 130g the oscillations were larger, which destabilizes the drone. The drone's stability bounds reached at 130g for the PV-MRAC. For the PV control, it can be seen that with 130g attached, the drone was destabilized. The drone's stability bounds reached at 120g for the PV control. The difference in performance of the controllers was obvious, the adaptive controller allowed safe operation and landing, while the PV controller failed to prevent instability and sometimes resulted in a crash landing.

Case 2: cable-suspended point-mass load

The case of attaching 100g suspended point-mass load, which introduced oscillating disturbances, both controllers achieved the task of rising to the desired height, however, the PV-MRAC controller still performed better, see Figure 22. The PV-MRAC controller response has smaller oscillations about the desired height value compared to the PV controller response.

Case 3: point-mass load: during flight

Figure 23 show the responses for the PV and PV-MRAC controllers, respectively, for the case when instantaneous point-mass load of 135g was attached at the top of the drone. As it can be observed, the PV controller failed to prevent instability and resulted in a crash landing, but the adaptive controller maintained the stability and allowed safe operation and landing. The drone's stability bounds reached at 125g and 135g using the PV and PV-MRAC controllers, respectively.

Conclusion

This paper has presented a second order LTI SISO ARX altitude motion model, without time delay, for a quadrotor types of UAVs. The effect of time delay on the drone's altitude response was analysed including system stability conditions for the tuned PV control system. The non-adaptive PV controller was designed for improved transient responses based on the rightmost roots calculated using the estimated delay value. Also, the MRAC based on the MIT rule was designed by incorporating the time delay. The MRAC is combined with a tuned PV controller, since the MIT rule MRAC on its own cannot guarantee stability.?

The robustness of the PV-MRAC controller was validated by comparing its performance to that of the non-adaptive PV controller, using the payload capability of the drone and the introduction of oscillating disturbances to the system. It was shown that the PV-MRAC offers several benefits over the fixed- gain approach of the PV controller. The PV-MRAC was found to offer enhanced robustness to the parametric (uncertainty in mass) and the oscillating disturbances. The difference in performance of the controllers was obvious. The adaptive controller allowed safe operation and landing while the PV controller failed to prevent instability and sometimes resulted in a crash landing. The drone's stability bounds reached at 120g and 130g for added attached mass-inertia using the PV and PV-MRAC controllers, respectively. In the case of the instantaneous introduction of the mass-inertia, the drone's stability bounds reached at 125g and 135g using the PV and PV-MRAC controllers, respectively.

Future work could involve comparing the performance of the MRAC based on the MIT rule to that based on the Lyapunov stability arguments, including composite adaptation. Composite MRAC adapts to both estimation and tracking errors. Also, multi-axis dynamics of the drone system considering attitude and lateral motions (x and y) can be considered. This problem is significantly more challenging, since the equation of motions are coupled and more complex than that of the altitude motion.

For More Open Access Journals Please Click on: Juniper Publishers

Fore More Articles Please Visit: Robotics & Automation Engineering Journal

#Juniper Publishers #open acess journals #Artifical Intelligence #Robotics #Computational networks

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juniperpublishers-etoaj · 6 years ago

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Juniper Publishers - Open Access Journal of Engineering Technology

Implementation of A* Algorithm to Autonomous Robots-A Simulation Study

Authored by : Selvakumar AA

Abstract

This article presents some of the current contributions to the robotic path planning field. Reliable collision free path is the fundamental thing for proper working of autonomous vehicle/robots. In order to find an optimal path for robot, environment or workspace need to be understood correctly and suitable algorithm need to used. Many researchers developed different algorithm techniques as per the required operations. This paper presents an overview of strength and weakness of A* algorithm for static environment using distance formula. A* algorithm works on the lower details of the map hence, it is considered as a higher-level path planning technique. A* algorithm is based on the availability of adjacent nodes and distance of goal location from the current state of the robot.

Keywords: A* algorithm; Path planning; Hierarchical algorithms; Heuristics

Introduction

Autonomous robots attained genuine attention because of variety of applications in the household, industrial as well as military purpose. For the accurate performance of such mobile robots, navigation or motion planning is the key aspect. It includes perception of environment based on sensory data, configuring with the surrounding and decision making which is an important phase to find an optimal path from the start location to the goal location without any collision with the surrounding. Different algorithm techniques have been used the path planning of autonomous robots. For example, genetic algorithm, grid-based algorithms, geometry-based algorithms, sampling-based algorithms. Most commonly used technique for static environment is grid-based algorithm. It includes configuration space which is divided into no. of small grids and hence detecting the obstacles robot finds the path from start location to goal location in the configuration space. But, in order to find the optimum solution, i.e. collision free path with the shortest distance so as to minimize the travel time, distance formula is implemented with the A* algorithm [1].

A* algorithm is grid-based path planning technique. Basically, A* algorithm was initially designed for the graph transversal problems. Later, it was commonly used for path finding applications such as computer games. A* algorithm is practically easier and faster for implementation [2]. A* algorithm is suitable for the static environments only. Also, it is not good for obstacle shape changes. To reach the goal position, A* algorithm creates sub optimal paths with the help of neighboring grids. It is represented as f (n) = g (n) + h'(n) where, g (n) is the distance from the start position to the current position whereas h'(n) is the estimated distance from current state to the goal position. In order to find this estimation heuristic function is used. f(n) is nothing but the estimated shortest path from the start location to the goal destination. In this technique, distance formula is used as a heuristic function [3].

In practical situation, static environment may be large for this algorithm to solve. Such cases are solved by defining a hierarchical solution. A* algorithm works on probability-based maps, i.e., it always tries to find path with the smallest length having lowest probability of the collision with the surrounding. Here path length factor is dominant one. [3] Also, as the obstacle size increases probability of collision with the surrounding reduces drastically. [4] Stated in their article, A* algorithm is not suitable for the static environment with the smaller size obstacles as this algorithm tends to find shortest path only over the obstacle avoidance.

Problem Statement

In the static environment, position or orientation of the obstacles are not changing. Hence configuration space is considered as a rectangular terrain, which can be divided into number of small grid. Initial and final location of robot or obstacles can be represented in these grids. Here, the problem is considered as a two-dimensional transverse terrain shown in the following Figure 1.

As shown in the Figure 1, configuration space is divided into number of grids. Autonomous robot is represented by blue dot. It has to transverse the terrain and reach the goal location which is represented by red oval. Black dots represent the obstacles.

Algorithm Guidance

Consider the case of a 4x4 configuration space. The starting node is (1,1). The successive node is only one in this case which is (1,2). There is no confusion, until the Robot reaches node (2,4). Now, there are two nodes (3,4) and (3,3). The successor node can be determined by evaluating the cost to the target from both the nodes (Figure 2).

Now, f (n) for (3,3) is smallest of the two, hence the successor node is f (n) . The robot can now transverse to the node (3, 3) and continue expanding the successor nodes as above, until the goal location is reached.

Consider a configuration with dead end condition (Figure 3).

Here, from Node (2,1) will be chosen as the successor node instead of Node (1,2). The robot will continue to traverse the route until it ends up at the block at Node (4,1). Here, need to add an algorithm by which the robot find outs alternate paths once it ends up at a dead node (dead end). Avoids traversing paths that follows to a dead node.

This is achieved by keeping up two records OPEN and CLOSED. OPEN list contains successive paths that are yet to be computed and CLOSED list is having all paths that have been explored. The list OPEN also stores the parent node of current location. This is used at the end to trace the path from the Goal to the Start position, thus calculating the optimum path. The start node has 2 successors (2,1) and (1,2). From the initial calculation (2,1) is chosen and the robot travels along that node, however ones it reaches the dead end, it discards the node (2,1) and takes the second successor (1,2) and explores that route Figure 4.

Once the goal location is reached the parent nodes are highlighted and tracked back to the start location to get the complete path. In the above example N(4,3),N(3,4), N(2,3), N(1,2), N(1,1) gives the optimum path. From the above conditions the following algorithm is obtained [3].

Algorithm Flow

Consider the first node and put it to the OPEN list. As it is the start point, is zero.

Now, next adjacent node's cost function is calculated. Smallest one is shifted to CLOSED list.

Suppose, robot reach to goal location, stop the algorithm. With the help of all cost functions, determine the path value. Otherwise, continue with the next nodes.

In the same manner, compute the cost function for all adjacent nodes with respect to robot's current position.

Now, with respect to parent node, sort the successor nodes to OPEN or CLOSED lists. Repeat the cycle till cost function of current location is zero (Distance between current location and goal becomes zero).

Simulation

Simulation is carried out using MATLAB. Based on MATLAB coding, two-dimensional array of a configuration space is created. Autonomous robot's initial location and goal location we can assigned. Obstacles are assigned manually. From these above inputs we got the output as Figure 5.

Result

Hence, the optimum path is found out from start location to the goal location in a static environment using classification of open nodes and closed nodes based upon distance formula. This technique is successfully implemented to the different static environments as far as obstacles are in its definite shape and size. If we consider each grid of dimension 1x1 unit, following are the results from the simulation of the total distance of some possible paths [5-11]. From the results shows in Table 1, the available shortest path is highlighted in the Figure 5.

Summary

Hence, A* algorithm is successfully implemented to the static environment using MATLAB simulation. This technique of path planning finds the optimal path with respect to the distance, but practically there are chances of collision with the surroundings as distance is the dominating factor in this algorithm over obstacle avoidance. In order to serve this purpose, more accurate understanding of the environment is essential. Also, to get more optimum results with respect to distance, integration of this technique with neural network can be used. Future scope in this area refers to integration of this algorithm with genetic algorithm, i.e., simulation results of this algorithm can be treated as an initial population for genetic method for determining more optimized path.

For more articles in Open Access Journal of Engineering Technology please click on: https://juniperpublishers.com/etoaj/index.php

To read more...Fulltext please click on: https://juniperpublishers.com/etoaj/ETOAJ.MS.ID.555564.php

#Engineering Technology open access journals #Juniper Journals Reviews #Juniper publisher journals #Juniper publishers #Open Access Journals #Peer Review Journals

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shazforiot · 6 years ago

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Open Source IoT Platforms

What is IoT ? or What is Internet of Things ?

The Internet of Things (IoT) is the network of things (physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators) connected through internet which enables these things to exchange data. This creates opportunities for more direct integration of the physical world into computer-based systems, resulting in efficiency improvements, economic benefits, and reduced human exertions.

The major enterprise IoT platform companies and few open source Internet of things platforms in the article top IoT platform companies.

Kaa IoT Platform

Kaa is an enterprise-grade IoT platform built on a modern cloud-native architecture and a fully customizable feature set. Based on flexible microservices, Kaa easily adapts to almost any need and application. It scales from a tiny start-up to a massive corporation and supports advanced deployment models for multicloud IoT solutions.

ThingSpeak (open IoT platform with MATLAB analytics)

ThingSpeak™ is an IoT analytics platform service that allows you to aggregate, visualize and analyze live data streams in the cloud. ThingSpeak provides instant visualizations of data posted by your devices to ThingSpeak. With the ability to execute MATLAB® code in ThingSpeak you can perform online analysis and processing of the data as it comes in. ThingSpeak is often used for prototyping and proof of concept IoT systems that require analytics.

#iot #internetofthings #iotplatform #open source #raspberry #arduino #sensor #things

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wolfliving · 6 years ago

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2019 Embedded Vision Summit

*Tiny embedded neural-net cameras all over the place. “Designing Home Monitoring Cameras for Scale, Enabling the Next Kitchen Experience Through Embedded Vision,” etc etc

https://www.embedded-vision.com/summit

MONDAY, MAY 20 9:00 AM - 5:00 PM Training: Computer Vision Applications in OpenCV (Separate Registration Required)

9:00 AM - 5:00 PM Training: Deep Learning for Computer Vision with TensorFlow 2.0 (Separate Registration Required)

TUESDAY, MAY 21 9:00 AM - 10:30 AM Making the Invisible Visible: Within Our Bodies, the World Around Us, and Beyond KEYNOTE SPEAKER Mission City B1-B5

10:45 AM - 11:15 AM What’s Changing in Autonomous Vehicle Investments Worldwide – and Why? BUSINESS INSIGHTS Theater

10:45 AM - 11:50 AM An Introduction to Machine Learning and How to Teach Machines to See FUNDAMENTALS Room 203/204

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THURSDAY, MAY 23 9:00 AM - 5:30 PM Intel Workshop: Advanced Workshop on Intel Vision Technology and OpenVINO Toolkit (Separate Registration Required)

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9:00 AM - 5:30 PM Khronos Workshop: Hardware Acceleration for Machine Learning and Computer Vision through Khronos Open Standard APIs Workshop (Separate Registration Required)

9:00 AM - 5:30 PM Synopsys Seminar: Embedded Vision Seminar: Navigating Intelligent Vision at the Edge (Separate Registration Required)

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arivontech-blog · 7 years ago

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Traffic surveillance

Arivon Technologies enables to explore the latest trends on TRAFFIC SURVEILLANCE and utilize the maximum benefits of this Technology. Please visit www.arivontech.com for more details. Contact us [email protected] .

VIDEO ACQUISITION ( SURVEILLANCE ):

Surveillance is the secret observation of people, places and vehicles, which law enforcement agencies and private detectives use to investigate allegations of illegal behavior. These techniques range from physical observation to the electronic monitoring of conversations.

TRAFFIC CAMERAS:

Traffic monitoring cameras typically sit on top of traffic lights and monitor traffic flowing through on the highway. They do not take pictures of vehicles that run red lights or issue citations. Red light cameras are much bulkier and are positioned on the side of the road.

Traffic cameras is an innovative functional use of video surveillance technology.

ADVANCED TRAFFIC MANAGEMENT SYSTEM:

Advance Traffic Management System (ATMS) is provided by Efkon India to ensure safety and reduce travel time. Over the highway, it becomes an absolute necessity to monitor incidents proactively by using modern technologies to minimize the effects of unavoidable disasters.

Advance Traffic Management System aim is to provide traffic management solutions that enable private concessionaires, highway operators or government authorities to take actions for improving the safety of road users and improving the traffic flow, increase transportation system efficiency, economic productivity and mobility.

ATMS is continuously monitors the expressway/ highway stretch providing valuable feedback and information to the Central Control Room to take suitable actions.

EFKON ADVANCE TRAFFIC MANAGEMENT SOLUTIONS BENEFITS:

Provide Real–time traffic information.

Display dynamic variable message signs, providing warnings to road users.

Reduced response time to an accident. This leads to an improved coordination between police and emergency services.

Reduced operations and maintenance costs.

VIDEO SURVEILLANCE SYSTEM:

Traffic signal light can be optimized using vehicle flow statistics obtained by Smart Video Surveillance Software (SVSS). This research focuses on efficient traffic control system by detecting and counting the vehicle numbers at various times and locations.

At present, one of the biggest problems in the main city in any country is the traffic jam during office hour and office break hour. Sometimes we can see the traffic signal green light is still ON even though there is no vehicle coming. Similarly, it is also observed that long queues of vehicles are waiting even though the road is empty due to traffic signal light selection without proper investigation on vehicle flow. This can be handled by adjusting the vehicle passing time implementing by our developed SVSS.

Our Advance Traffic Management System is a indigenously developed and designed specifically for Indian road conditions.

Research, Development and global exposure resulted in integration of latest traffic management system like speed violation, Automatic Number Plate Recognition and video incident detection systems with ATMS.

This State-of-Art system integrates the following main sub-systems into one powerful communication/ information tool for the operations company:

Variable Message Signs

Video Incident Detection System

Meteorological System

Automatic Traffic Control and Classification System

CCTV (Closed-Circuit Television) Monitoring System

Communication Systems

1.Emergency Call Box System 2.Mobile Communication

Optical Fiber or GSM based network backbone

Command and Control centre

TRAFFIC SURVELLIANCE USING MATLAB AND ARDUINO:

Traffic Cameras capture the image of vehicles in traffic and sends to MATLAB. Traffic Surveillance System is denoted by way of how the traffic could be controlled over traffic intersection points from the Traffic Control Room. This is a very effective system for Traffic Surveillance.

There are two Arduino UNO boards which are used for controlling all the operations. GPS and GSM technologies are also used. Here GPS is used for taking coordinates of a Traffic Jam and GSM is used for sending these coordinates to Traffic Control Room. But besides GPS and GSM there are two more other important things which are used in the Traffic Surveillance and that are Cameras and MATLAB.

MATLAB sends command to cameras for capturing the images of traffic, where MATLAB count the total number of vehicles over the Traffic Intersection or over on road. If the total number of vehicles over the road exceeds the limits in a given situation then the MATLAB sends signals to Arduino UNO (1).

Arduino UNO (1) sends interrupt to second Arduino UNO (2). Now the second Arduino UNO (2) takes coordinates of the Traffic Jam and sends these coordinates to the Traffic Control Room using GSM module. LCD for showing the traffic status like Traffic Jam or No Traffic.

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the-fitsquad · 7 years ago

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HP Workstation Z420 Xeon Pc Rental

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