Adaptive Tracking Control for Active Seat Suspension System with Time-Varying Full State Constraints

This study investigates a kind of active seat suspension systems with time-varying full state constraints (TFSCs), whose aim is to improve the riding comfort. In order to handle the unknown functions, the radial basis function neural networks (RBFNNs) are adopted. Combining the Tangent Barrier Lyapunov Function (BLF-Tan) with backstepping technique, a novel adaptive controller is designed for active seat suspension systems. It is proven that all signals in the resulted system are semi-globally uniformly ultimately bounded (SGUUB) with TFSCs. Further, the simulation results demonstrate the effectiveness of presented control method.