Composite nonlinear feedback control with multi-objective particle swarm optimization for active front steering system

The purpose of controlling the vehicle handling is to ensure that the vehicle is in a safe condition and following its desire path. Vehicle yaw rate is controlled in order to achieve a good vehicle handling. In this paper, the optimal Composite Nonlinear Feedback (CNF) control technique is proposed for an Active Front Steering (AFS) system for improving the vehicle yaw rate response. The model used in order to validate the performance of controller is nonlinear vehicle model with 7 degree-of-freedom (DOF) and a bicycle model is implemented for the purpose of designing the controller. In designing an optimal CNF controller, the parameter estimation of linear and nonlinear gain becomes very important to produce the best output response. An intelligent algorithm is designed to minimize the time consumed to get the best parameter. To design an optimal method, Multi Objective Particle Swarm Optimization (MOPSO) is utilized to optimize the CNF controller performance. As a result, transient performance of the yaw rate has improved with the increased speed of in tracking and searching of the best optimized parameter estimation for the linear and the nonlinear gain of CNF controller.

[1]  Kemao Peng,et al.  Robust Composite Nonlinear Feedback Control With Application to a Servo Positioning System , 2007, IEEE Transactions on Industrial Electronics.

[2]  Meir Pachter,et al.  Toward improvement of tracking performance nonlinear feedback for linear systems , 1998 .

[3]  Kemao Peng,et al.  A Yaw Rate Tracking Control of Active Front Steering System Using Composite Nonlinear Feedback , 2013, AsiaSim.

[4]  You-Sub Kim,et al.  Development of an active front steering (AFS) system with QFT control , 2008 .

[5]  Ben M. Chen,et al.  A Hard-Disk-Drive Servo System Design Using Composite Nonlinear-Feedback Control With Optimal Nonlinear Gain Tuning Methods , 2010, IEEE Transactions on Industrial Electronics.

[6]  Mehdi Mirzaei,et al.  A new strategy for minimum usage of external yaw moment in vehicle dynamic control system , 2010 .

[7]  Ahmad Bagheri,et al.  Optimal robust sliding mode tracking control of a biped robot based on ingenious multi-objective PSO , 2014, Neurocomputing.

[8]  Tong Heng Lee,et al.  Composite nonlinear feedback control for linear systems with input saturation: theory and an application , 2003, IEEE Trans. Autom. Control..

[9]  Yahaya Md Sam,et al.  A control performance of linear model and the MacPherson model for active suspension system using composite nonlinear feedback , 2012, 2012 IEEE International Conference on Control System, Computing and Engineering.

[10]  Yoichi Hori,et al.  Advanced Motion Control of Electric Vehicles Based on Robust Lateral Tire Force Control via Active Front Steering , 2014, IEEE/ASME Transactions on Mechatronics.

[11]  Ben M. Chen,et al.  On selection of nonlinear gain in composite nonlinear feedback control for a class of linear systems , 2007, 2007 46th IEEE Conference on Decision and Control.

[12]  Yoshio Izui,et al.  Multiobjective energy management system using modified MOPSO , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[13]  Hazriq Izzuan Jaafar,et al.  Optimal PID controller parameters for nonlinear gantry crane system via MOPSO technique , 2013, 2013 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (CSUDET).

[14]  Yi Lin,et al.  Yaw Stability Control of Active Front Steering with Fractional-Order PID Controller , 2009, 2009 International Conference on Information Engineering and Computer Science.

[15]  Muhamad Khairi Aripin,et al.  Optimal Composite Nonlinear Feedback Controller for an Active Front Steering System , 2014 .

[16]  Adel M. Sharaf,et al.  A novel discrete multi-objective Particle Swarm Optimization (MOPSO) of optimal shunt power filter , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[17]  Changxin Liu,et al.  Research on Grid Workflow Scheduling Based on MOPSO Algorithm , 2009, 2009 WRI Global Congress on Intelligent Systems.

[18]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[19]  N. Hamzah,et al.  Yaw stability improvement for four-wheel active steering vehicle using sliding mode control , 2012, 2012 IEEE 8th International Colloquium on Signal Processing and its Applications.

[20]  Ben M. Chen,et al.  On improvement of transient performance in tracking control for a class of nonlinear systems with input saturation , 2006, Syst. Control. Lett..

[21]  Tad Gonsalves,et al.  Multi objective particle swarm optimization algorithm for the design of efficient ATO speed profiles in metro lines , 2014, Eng. Appl. Artif. Intell..

[22]  Yuan Xiang,et al.  A Fuzzy Control Method to Improve Vehicle Yaw Stability Based on Integrated Yaw Moment Control and Active Front Steering , 2007, 2007 International Conference on Mechatronics and Automation.