Dynamic neural network-based feedback linearization control of full-car suspensions using PSO

This paper proposes a nonlinear control approach using dynamic neural network-based input–output feedback linearization to resolve the inherent conflicting performance criteria for a full-car nonlinear electrohydraulic active vehicle suspension system. Particle swarm optimization is applied both for the dynamic neural network models' trainings and the computation of the controllers' parameters. The intelligent control scheme outperformed the passive vehicle suspension system and the benchmark particle swarm-optimized proportional+integral+derivative controller. Effectiveness and robustness of the proposed controller are demonstrated through simulations both in time- and frequency-domains.