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

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. (c) 2020 Elsevier B.V. All rights reserved.