Adaptive robust control based on radial basis function neural networks for electro-hydraulic brake-by-wire system

Abstract

The electro-hydraulic brake-by-wire system of engineering vehicles is a strongly nonlinear electromechanical-hydraulic system whose pressure control performance is susceptible to parameter uncertainties, unknown disturbances, and varying operating conditions. Existing nonlinear control and model-based disturbance estimation methods suffer from insufficient tracking accuracy and robustness, as well as limited disturbance estimation performance under complex operating conditions. To address these issues, this study proposes an adaptive robust pressure control method based on radial basis function neural networks. A radial basis function neural network observer is designed to estimate lumped disturbances in real time, while weight update laws enable the network parameters to be updated online without extensive offline training. The estimated disturbances are incorporated into the control system through feedforward compensation. Furthermore, the backstepping method, Lyapunov stability theory, and parameter adaptation are integrated to design an adaptive robust feedback controller that compensates for neural network estimation errors and system uncertainties. The main novelty lies in the coordinated integration of online neural-network-based disturbance estimation, feedforward disturbance compensation, and adaptive robust feedback within a unified closed-loop control framework. Stability analysis demonstrates that all closed-loop error signals are ultimately bounded. The comparison results demonstrate that the proposed method effectively improves pressure response speed, tracking control accuracy, and robustness under different operating conditions.