VELOCITY CONTROL OF A UNICYCLE TYPE OF MOBILE ROBOT USING OPTIMAL PID CONTROLLER
DOI:
https://doi.org/10.11113/jt.v78.9415Keywords:
Unicycle type of mobile robot, tuning dynamic gains, PSO-PID controllerAbstract
A unicycle model of control a mobile robot is a simplified modeling approach modified from the differential drive mobile robots. Instead of controlling the right speed,  and the left speed,  of the drive systems, the unicycle model is using  and  as the controller parameters. Tracking is much easier in this model. In this paper, the dynamic of the robot parameter is controlled using two blocks of Proportional-Integral-Derivative (PID) controllers. The gains of the PID are firstly determined using particle swarm optimization (PSO) in offline mode. After the optimal gain is determined, the tracking of the robot’s trajectory is performed online with optimal PID controller. The achieved results of the proposed scheme are compared with those of dynamic model optimized with genetic algorithm (GA) and manually tuned PID controller gains. In the algorithm, the control parameters are computed by minimizing the fitness function defined by using the integral absolute error (IAE) performance index. The simulation results obtained reveal advantages of the proposed PSO-PID dynamic controller for trajectory tracking of a unicycle type of mobile robot. A MATLAB-Simulink program is used to simulate the designed system and the results are graphically plotted. In addition, numerical simulations using 8-shape as a reference trajectory with several numbers of iterations are reported to show the validity of the proposed scheme.
References
(1) DeVon, D. Bretl, T. 2007. Kinematic and Dynamic Control Of A Wheeled Mobile Robot. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007), San Diego, CA. 29 October 2007. 4065-4070.
(2) Martins, F. N., Celeste, W. C., Carelli, R., Sarcinelli-Filho, M., Bastos-Filho, T. F. 2008. An Adaptive Dynamic Controller For Autonomous Mobile Robot Trajectory Tracking. Control Engineering Practice. 16(11): 1354-1363.
(3) Huang, J. T. 2009. Adaptive Tracking Control Of High-Order Non-Holonomic Mobile Robot Systems. Control Theory and Applications, IET. 3(6): 681-690.
(4) Oriolo, G., De Luca, A., Vendittelli, M. 2002. WMR Control Via Dynamic Feedback Linearization: Design, Implementation, And Experimental Validation. IEEE Transactions on Control Systems Technology. 10(6): 835-852.
(5) Lee, T. C., Song, K. T., Lee, C. H., Teng, C. C. 2001. Tracking Control Of Unicycle Modeled Mobile Robots Using A Saturation Feedback Controller. IEEE Trans. Control System Technology. 9(2): 305-318.
(6) Martins, F. N., Almeida, G. M., IFES, C. S. 2012. Tuning a Velocity-based Dynamic Controller for Unicycle Mobile Robots with Genetic Algorithm. Jornadas Argentinas de Robotica, JAR. 12: 262-269.
(7) Hassan R, Cohanim B, De Weck O, Venter G. 2005. A Comparison Of Particle Swarm Optimization And The Genetic Algorithm. Proceedings of the 1st AIAA Multidisciplinary Design Optimization Specialist Conference. Austin, Texas. 18 April 2005. 18-21.
(8) Hashim, N. L. S., Yahya, A., Andromeda, T., Kadir, M. R. R. A., Mahmud, N., Samion, S. 2012. Simulation of PSO-PI Controller of DC Motor in Micro--EDM System for Biomedical Application. Procedia Engineering. 41: 805-811.
(9) De La Cruz, C., Carelli, R. 2006. Dynamic Modeling And Centralized Formation Control Of Mobile Robots. 32nd Annual Conference on IEEE Industrial Electronics (IECON), 2006. 6-10 November 2006. Paris. 3880-3885.
(10) Lalitha, M. P., Reddy, V. V., Usha, V. 2010. Optimal DG Placement For Minimum Real Power Loss In Radial Distribution Systems Using PSO. Journal of Theoretical and Applied Information Technology. 13(2): 107-116.
(11) Eberhart, R. C., Shi, Y. 2000. Comparing Inertia Weights And Constriction Factors In Particle Swarm Optimization. Proceedings of the IEEE Congress on Evolutionary Computation, La Jolla, CA. 16-19 July 2000. 84-88.
(12) Serrano, M. E., Godoy, S. A., Mut, V. A., Ortiz, O. A. and Scaglia, G. J. 2015. A Nonlinear Trajectory Tracking Controller For Mobile Robots With Velocity Limitation Via Parameters Regulation. Robotica. 1-20.
(13) Allaoua, B., Gasbaoui, B., Mebarki, B. 2009. Setting up PID DC Motor Speed Control Alteration Parameters Using Particle Swarm Optimization Strategy. Leonardo Electronic Journal of Practices and Technologies. 14: 19-32.
(14) Basri, M, A., Danapalasingam, K. A., Husain, A. R. 2014. Design and Optimization of Backstepping Controller for An Underactuated Autonomous Quadrotor Unmanned Aerial Vehicle. Transactions of FAMENA. 38(3): 27-44.
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