Adaptive Neural Command Filtered Control for Pneumatic Active Suspension With Prescribed Performance and Input Saturation

In this paper, an adaptive neural command filtered backstepping scheme is proposed for the pneumatic active suspension with vertical displacement constraint of sprung mass and actuator saturation. A quarter car model a spring first fabricated on basis thermodynamic theory to describe dynamic characteristics. To overcome lumped unknown nonlinearities enhance requirement modeling precision, radial function networks (RBFNNs) are approximate continuous functions caused by uncertain body other factors spring. solve explosion complexity problem in traditional designs, filter control applied using Levant differentiators which approach derivative virtual signals. Nussbaum gain technique then incorporated into controller avoid completely directions actuator. addition, prescribed performance (PPF) suggested guarantee that tracking error does not violate boundaries. Based PPF, Lyapunov theorem indicate system stability analysis. Finally, comparative simulation examples given verify effectiveness reliability control.