Adaptive nonlinear active suspension control based on a robust road classifier with a modified super-twisting algorithm

For the suspension system equipped with nonlinear hydraulic actuators and excited by external road conditions, a road adaptive intelligent suspension control strategy is developed. In this work, (1) a multi-phase intelligent road adaptive control architecture is developed to enhance the ride comfort in the presence of varying road excitations; (2) a modified algorithm is proposed to improve the system performance. Initially based on the nonlinear system dynamics, a sliding mode controller based on an improved super-twisting algorithm is proposed. In the Off-line phase, the optimized control parameters based on particle swarm optimization (PSO) approach for each road level are determined and supplied to a probabilistic neural network (PNN)-based classifier for training. In the On-line phase, the PNN classifier employs the measured unsprung mass acceleration to determine the road level and supplies the information to the controller database. Based on the classified road level, corresponding control parameters as determined by PSO are then selected. These control parameters are then supplied to the nonlinear controller which provides the active control. The closed-loop stability of the proposed approach is proved, and the simulation results for different road levels are presented to show the effectiveness of the proposed approach.