Active Suspension Control of Full-car Systems without Function Approximation

This paper proposes a new control approach for full-car active suspension systems with unknown nonlinearities. The main advantage of this approach is that the uncertainties and nonlinearities in the system can be handled without using any function approximator (e.g., neural networks (NNs), fuzzy logic systems (FLSs)), and the associated online adaptation. Hence, the heavy computational costs and sluggish learning phase to achieve convergence can be remedied. To maintain the transient and steady-state suspension responses, a coordinate suspension error transformation with prescribed performance functions (PPF) is adopted. Then an approximation-free control (AFC) is developed to achieve stabilization of the transformed system so as to retain predefined suspension response. Extreme Value Theorem is used together with Lyapunov theorem to prove the stability and convergence of the closed-loop control system. To validate the proposed method and show its practical applicability, a dynamic simulator is built by using a commercial vehicle software, Carsim, where an E-SUV type vehicle is configured to describe realistic vehicle dynamics. Simulation results reveal that the proposed control can achieve better suspension performance and require less model information compared with some existing approaches

Unknown dynamics estimator-based output-feedback control for nonlinear pure-feedback systems

Most existing adaptive control designs for nonlinear pure-feedback systems have been derived based on backstepping or dynamic surface control (DSC) methods, requiring full system states to be measurable. The neural networks (NNs) or fuzzy logic systems (FLSs) used to accommodate uncertainties also impose demanding computational cost and sluggish convergence. To address these issues, this paper proposes a new output-feedback control for uncertain pure-feedback systems without using backstepping and function approximator. A coordinate transform is first used to represent the pure-feedback system in a canonical form to evade using the backstepping or DSC scheme. Then the Levant's differentiator is used to reconstruct the unknown states of the derived canonical system. Finally, a new unknown system dynamics estimator with only one tuning parameter is developed to compensate for the lumped unknown dynamics in the feedback control. This leads to an alternative, simple approximation-free control method for pure-feedback systems, where only the system output needs to be measured. The stability of the closed-loop control system, including the unknown dynamics estimator and the feedback control is proved. Comparative simulations and experiments based on a PMSM test-rig are carried out to test and validate the effectiveness of the proposed method