Sliding mode control of electro-hydraulic servo based on neural network adaptive observer

This paper addresses the common challenges of multi-source nonlinear disturbances and parameter uncertainties in traditional electro-hydraulic servo systems by proposing an enhanced sliding mode control method based on a neural network adaptive observer. First, a nonlinear mathematical model of the electro-hydraulic system was established based on the structure and working principle of the valve-controlled hydraulic cylinder. Second, by leveraging the strong nonlinear approximation capability of radial basis function (RBF) neural networks, the nonlinear terms in the system were approximated online and compensated in real time. Next, an adaptive state observer utilizing RBF neural networks was developed to precisely estimate the difficult-to-measure states within the hydraulic system. The state estimates from the observer were integrated with sliding mode control to design the system control law. Additionally, a low-pass filter was introduced to smooth the control law, improving the smoothness of the controller's output and the overall system stability. Finally, the bounded stability of the closed-loop system was proven by constructing a Lyapunov function. Simulation results demonstrate that under various reference signal inputs and parameter abrupt change scenarios, the designed controller exhibits faster convergence and superior robustness compared to traditional nonlinear controllers. Its average tracking error is reduced by 27.51%–90.2% relative to the intelligent PID controller and by 14.55%–68.82% relative to the adaptive sliding mode controller (ASMC). It exhibits notable improvements in control precision, dynamic response velocity, and disturbance rejection performance, thereby offering an efficient approach to addressing nonlinear control challenges in electro-hydraulic servo systems.