access icon free Adaptive neural network control for active suspension system with actuator saturation

© The Institution of Engineering and Technology
Received 23/12/2015, Accepted 03/05/2016, Revised 22/03/2016, Published 10/06/2016

This study investigates adaptive neural network (NN) state feedback control and robust observation for an active suspension system that considers parametric uncertainties, road disturbances and actuator saturation. An adaptive radial basis function neural network is adopted to approximate uncertain non-linear functions in the dynamic system. An auxiliary system is designed and presented to deal with the effects of actuator saturation. In addition, since it is difficult to obtain accurate states in practice, an NN observer is developed to provide state estimation using the measured input and output data of the system. The state observer-based feedback control parameters with saturated inputs are optimised by the particle swarm optimisation scheme. Furthermore, the uniformly ultimately boundedness of all the closed-loop signals is guaranteed through rigorous Lyapunov analysis. The simulation results further demonstrate that the proposed controller can effectively suppress car body vibrations and offers superior control performance despite the existence of non-linear dynamics and control input constraints.

Inspec keywords: particle swarm optimisation; adaptive control; observers; neurocontrollers; Lyapunov methods; nonlinear control systems; closed loop systems; state feedback; suspensions (mechanical components); robust control

Other keywords: car body vibrations; uncertain nonlinear functions; state estimation; Lyapunov analysis; robust observation; adaptive radial basis function neural network; NN observer; particle swarm optimisation scheme; adaptive neural network state feedback control; actuator saturation; active suspension system; state observer-based feedback control parameters; the closed-loop signals

Subjects: Transportation system control; Nonlinear control systems; Self-adjusting control systems; Neurocontrol; Mechanical components; Optimisation techniques; Stability in control theory

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