Abstract:

Vehicle suspension systems have attracted considerable attention both in the academia and automotive industry for their potential to improve ride comfort, vehicle maneuverability, and passenger safety. In this research, to deal with the conflicting control objectives of active suspension system (ASS) including ride comfort, suspension travel, and road handling, the control design is reformulated as an optimization problem and the linear quadratic control framework and model predictive control strategy have been explored. To address the weight selection problem of Linear Quadratic Regulator (LQR), the study firstly present an eigenstructure assignment (EA) technique based on particle swarm optimization (PSO) algorithm for vibration suppression problem of the active suspension system. Unlike the standard fitness functions like Integral Square Error (ISE) and Integral Time-weighted Absolute Error (ITAE), the fitness function formulated using the EA technique improves the convergence of PSO by increasing the orthogonality between the left and right eigenvectors. The efficacy of the proposed scheme is assessed using the hardware in loop testing on a laboratory scale quarter car ASS, and the experimental results highlight that the EA based technique can significantly improve the convergence and yield optimal response.