Fixed-time sliding mode control with nonlinear disturbance estimation for active suspension via Self-Adaptive Particle Swarm Optimization

Research on suspension control continues to face challenges associated with nonlinear dynamics and uncertainties, degraded convergence properties, and the chattering phenomenon that may accelerate mechanical wear and increase control input. This article proposes an optimal robust control architecture to enhance the performance of active suspension systems. The developed scheme is organized into three hierarchical layers. The outer layer integrates Fixed-Time Sliding Mode Control with Nonlinear Active Disturbance Rejection Control to provide fast convergence and adequate disturbance compensation, thereby generating the desired control force. The middle layer employs a Proportional–Integral mechanism to regulate the spool valve dynamics, while the inner layer uses another Proportional–Integral control to produce the actuator input. Controller parameters are optimally tuned using a Self-Adaptive Particle Swarm Optimization algorithm to reduce vehicle body acceleration and control input simultaneously. Simulation results demonstrate that the proposed strategy yields remarkable improvements in suspension dynamics behavior. Under ISO D-class road excitation, characterized by a given geometric mean, the root-mean-square values of vehicle body displacement and acceleration are reduced to 0.35 mm and 0.18 m/s 2 , respectively, which are substantially lower than those achieved by the benchmark controllers under identical conditions. In addition, chattering is largely mitigated, and the estimation error is significantly decreased, highlighting the potential applicability of the proposed method to automotive mechatronic systems.