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

被引:37
作者
Al-Mayyahi A. [1 ]
Wang W. [1 ]
Birch P. [1 ]
机构
[1] School of Engineering and Informatics, University of Sussex, Chichester 1 Room 012, Falmer, Brighton
关键词
Autonomous ground vehicle; Fractional-order PID controller; Particle swarm optimization; Robustness; Stability; Trajectory tracking;
D O I
10.1007/s40313-015-0214-2
中图分类号
学科分类号
摘要
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. © 2015, Brazilian Society for Automatics--SBA.
引用
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页码:29 / 42
页数:13
相关论文
共 21 条
  • [1] Aboelela M.S., Ahmed M.F., Dorrah H.T., Design of aerospace control systems using fractional PID controller, Journal of Advanced Research, 3, 3, pp. 225-232, (2012)
  • [2] Aldair A.A., Wang W.J., Design of fractional order controller based on evolutionary algorithm for a full vehicle nonlinear active suspension systems, International Journal of Control and Automation, 3, 4, pp. 33-46, (2010)
  • [3] Al-Mayyahi A., Wang W., Birch P., Adaptive neuro-fuzzy technique for autonomous ground vehicle navigation, Robotics, 3, 4, pp. 349-370, (2014)
  • [4] Cao J., Cao B., Design of Fractional Order Controllers Based on Particle Swarm Optimization, In 1st IEEE Conference on Industrial Electronics and Applications, pp. 1-6, (2006)
  • [5] Cao J., Liang J.I.N., Cao B., Optimization of Fractional Order PID Controllers Based on Genetic Algorithms, In The fourth International Conference on Machine Learning and Cybernetics, pp. 5686-5689, (2005)
  • [6] Chen Y., Petr I., Xue D., Fractional Order Control - A Tutorial, In American Control Conference, pp. 1397-1411, (2009)
  • [7] Fierro R., Lewis F.L., Control of a nonholonomic mobile robot?: Backstepping kinematics into dynamics, Journal of Robotic Systems, 14, 3, pp. 149-163, (1997)
  • [8] Fierro R., Lewis F.L., Control of a nonholonomic mobile robot using neural networks, IEEE Transactions on Neural Networks, 9, 4, pp. 589-600, (1998)
  • [9] Huang J., Wen C., Wang W., Jiang Z.-P., Adaptive output feedback tracking control of a nonholonomic mobile robot, Automatica, 50, 3, pp. 821-831, (2014)
  • [10] Lee C.-H., Chang F.-K., Fractional-order PID controller optimization via improved electromagnetism-like algorithm, Expert Systems with Applications, 37, 12, pp. 8871-8878, (2010)