Fault-Tolerant Constrained Control of Nonlinear Active Suspension Systems Using Adaptive Filtering and Neural Approximation

This paper investigates the fault-tolerant constrained control problem of a nonlinear quarter-car active suspension system subject to road disturbances, body-state constraints, and mixed actuator faults. When mixed actuator faults, state constraints, unknown nonlinear suspension dynamics, and convergence-time requirements coexist, it remains challenging to simultaneously guarantee fault-tolerant compensation, constraint preservation, and implementable control laws. To address these challenges, a neural-network control method based on an adaptive prescribed-time filter (APF) is proposed. A logarithmic state transformation is introduced to convert the body-displacement and velocity constraints into boundedness problems of transformed variables, and the sprung-mass subsystem is represented in a strict-feedback form. The unknown nonlinearities induced by suspension dynamics, road disturbances, and additive actuator faults are approximated online by radial basis function neural networks. Meanwhile, the APF is employed to avoid repeated differentiation of virtual control laws in backstepping and to achieve practical prescribed-time stability. Lyapunov analysis proves that all closed-loop signals are bounded, the body-state constraints are preserved, and sufficient conditions are obtained for the boundedness of the unsprung-mass dynamics, as well as the safety of suspension travel and tire dynamic load. Simulation results under sinusoidal road excitation and smooth-transition actuator faults show that, compared with PID control, passive suspension, and sliding mode control, the proposed method reduces the body-displacement RMSE by 77.39%, 91.83%, and 73.12%, respectively, and the RMS body acceleration by 70.34%, 87.73%, and 50.22%, respectively, while maintaining suspension travel and tire dynamic load within their safety bounds.