RISE-Based Adaptive Control of Hydraulic Systems With Asymptotic Tracking

Parametric uncertainty associated with unmodeled disturbance always exist in physical hydraulic systems, and complicate the advanced nonlinear controller design. In this paper, an adaptive compensation with a robust integral of the sign of the error (RISE) feedback is developed for high precise tracking control of hydraulic motion system. To handle both payload and hydraulic unknown parameters in one controller, a chain of integrator nonlinear system model is first derived, and an adaptive RISE controller is then proposed, in which adaptive law is synthesized to handle parametric uncertainty and RISE robust term to attenuate unmodeled disturbance. The major feature of the proposed controller is that it can theoretically guarantee asymptotic tracking performance with a continuous control input, in the presence of various parametric uncertainties and unmodeled disturbances such as unconsidered dynamics as well as external disturbances via Lyapunov analysis. However, the proposed controller takes the acceleration as a system state, which usually suffers heavy noise pollution and thus cannot be utilized directly in actual control. To solve this practical issue, in this paper, a tracking differentiator is employed to extract high-quality acceleration signal and to make the proposed controller feasible execution. The effectiveness of the proposed nonlinear controller is demonstrated via comparative experimental results. Note to Practitioners-This paper demonstrates how to use adaptive and robust control methods to, respectively, handle parametric uncertainties and unmodeled disturbances together in one controller, for hydraulic motion servo systems. The proposed controller guarantees that if the unmodeled disturbance is smooth enough, the tracking error will converge to zero as time goes to infinity, no matter the payload is known or not. Experiments reveal that the proposed controller can be easily implemented in practice, and a great performance improvement can be achieved, in comparison to the traditional proportional-integral-derivative (PID) controller and model-based adaptive controller.