Neural networks-based adaptive dynamic surface control for vehicle active suspension systems with time-varying displacement constraints

Abstract

This paper addresses controlling of displacements of vehicle active suspension systems with an active seat suspension and a human-body model. A neural networks-based dynamic surface control strategy is constructed for the active suspension systems. Specifically, asymmetric time-varying barrier Lyapunov functions are applied to ensure that the displacements of vehicle active suspensions do not violate their time-varying constraint bounds. Neural networks are used to approximate unknown functions in the active suspension systems and their basis function properties are employed to deal with functions with non-strict form. Dynamic surface control technique is used to reduce the complexity of the controller. Advantages of the control strategy are substantiated by two simulation examples.