Design of Fractional-Order Controller for Trajectory Tracking Control of a Non-holonomic Autonomous Ground Vehicle

Abstract

(2016) Design of fractional-order controller for trajectory tracking control of a non-holonomic autonomous ground vehicle. Journal of Control, Automation and Electrical Systems, 27 (1). pp. 29-42. ISSN 2195-3880 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/57057/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Abstract A robust control technique is proposed to address the problem of trajectory tracking of an autonomous ground vehicle (AGV). This technique utilizes a fractional-order proportional integral derivative (FOPID) controller to control a non-holonomic autonomous ground vehicle to track the behaviour of the predefined reference path. Two FOPID controllers are designed to control the AGV's inputs. These inputs represent the torques that are used in order to manipulate the implemented model of the vehicle to obtain the actual path. The implemented model of the non-holonomic autonomous ground vehicle takes into consideration both of the kinematic and dynamic models. In additional, a particle swarm optimization (PSO) algorithm is used to optimize the FOPID controllers' parameters. These optimal tuned parameters of FOPID controllers minimize the cost function used in the algorithm. The effectiveness and validation of the proposed method have been verified through different patterns of reference paths using MATLAB-Simulink software package. The stability of fractional-order system is analysed. Also, the robustness of the system is conducted by adding disturbances due to friction of wheels during the vehicle motion.The obtained results of FOPID controller show the advantage and the performance of the technique in terms of minimizing path tracking error and the complement of the path following.B Auday Al-Mayyahi

Control of a 3-RRR Planar Parallel Robot Using Fractional Order PID Controller

“…Such trajectories are commonly utilized in industrial automation applications. Additionally, to measure the advantages of such a controller in comparing with the state of the art, we have taken the following data which had been already studied as in [40,41]. Table 2 demonstrates all the numerical values from the state of the art [40] and our proposed control system.…”

“…Additionally, to measure the advantages of such a controller in comparing with the state of the art, we have taken the following data which had been already studied as in [40,41]. Table 2 demonstrates all the numerical values from the state of the art [40] and our proposed control system. The length of the links = = 0.4 m, the concentrated mass of the links ; the concentrated mass of the moving platform .…”

A Novel Design of a Neural Network-Based Fractional PID Controller for Mobile Robots Using Hybridized Fruit Fly and Particle Swarm Optimization

“…Recently, researchers are applying a new control paradigm named active disturbance rejection control (ADRC) [25][26][27][28][29][30] on a wide range of applications [31,32] and particularly on DDMR [33]. e authors in the literature proposed many algorithms for tuning parameters of the FOPID controller with different applications, where [34,35] used the genetic algorithms and [36][37][38][39] utilized PSO algorithm. Others like Rajasekhar et al [40] applied the gravitational search optimization technique based on the Cauchy and Gaussian mutation, and El-Khazali [41] exploited the artificial bee colony algorithm.…”

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