Adaptive self-structuring neural network control for full-state constrained hydraulic systems with disturbance compensation

Abstract

An adaptive self-structuring neural network for full-state constrained systems with a prescribed performance control scheme is proposed for hydraulic systems to achieve asymptotic tracking under parametric uncertainties and time-varying disturbances. To overcome external disturbances in hydraulic systems, a self-structuring neural network is employed to approximate mismatched disturbances, reducing the computational burden. Additionally, an output feedback filtering control technique is introduced to address the challenges of uncertain parameter sets, which deteriorates the control performance when affected by oscillations triggered by high-frequency noise. The prescribed performance control achieves the desired transient and steady-state performance of the system by constructing a sequence of error transition variables that guarantee that all the state errors of the hydraulic systems are within the appointed-time reachable performance function boundary range. For the proposed control scheme, the stability of the closed-loop system can be demonstrated via Lyapunov theory, and all signals are guaranteed to be bounded. Ultimately, the tracking performance of the proposed controller is verified through abundant comparative experiments under the influence of parameter uncertainty and time-varying disturbances.