Hybrid extended state observer-based integral sliding mode control of the propulsion for a hydraulic roofbolter

For a propulsion system with dead-zone, time-varying parameters and sudden disturbances, the existing hybrid control methods based on sliding-mode and extended state observer still face challenges such as chattering, high-frequency switching of controller, and low anti-disturbance ability. To solve the problems, this paper proposes an integral sliding-mode controller based on hybrid extended state observer (ISM-HESO), which includes a transition process, an Observer unit, a sliding-mode control (SMC) unit and a parameter adaptation law. Taking dead-zone, time-varying parameters and disturbances into consideration, the propulsion system of a hydraulic roofbolter is modeled after compensating for dead-zone. Based on this, a transition process is introduced to transform the reference propulsion of step input into a continuous one, with the purpose of improving the response speed and avoiding overshoot. Then, in Observer unit, a hybrid extended state observer is designed to effectively estimate the dynamic disturbances. SMC unit provides a continuous and effective sliding-mode control law for the propulsion system, with the purpose of speeding up the response and eliminating the chattering. In SMC unit: (1) A novel integral sliding-mode surface is presented to eliminate the negative effect of disturbance estimation error and avoid high-frequency switching of ISM-HESO; (2) A novel sliding-mode reaching law with a Saturation function is developed, with the purpose of enhancing robustness, improving reaching speed and eliminating chattering; (3) An adaptive sliding-mode control law is presented based the above two strategies, which aims to provide an effective and continuous control signal. Moreover, a parameter adaptation law is designed to tune the estimations of time-varying parameters and improve control performance. Finally, the effectiveness of ISM-HESO is verified by comparative experiments. The experimental results show that ISM-HESO has better dynamic and steady-state performance.