This article focuses on a novel robust adaptive sliding mode control strategy of an electro-hydraulic force loading system with consideration of external disturbances and parameter uncertainties. To obtain the proposed controller, the nonlinear dynamic model of the electro-hydraulic force loading system is firstly provided, where external disturbances and system’s uncertain parameters are merged. Then, with consideration of external disturbances and parameter uncertainties, a robust adaptive sliding mode controller for the force control system is proposed and designed according to Lyapunov stability theory. The proposed controller has the merit of suppressing chattering phenomenon in traditional adaptive backstepping controllers when designing sign functions. In order to verify effectiveness and feasibility of the proposed controller, experimental studies are implemented on an electro-hydraulic force loading system by xPC rapid prototyping technology. Experimental results demonstrate that the proposed controller exhibits more excellent performance on force tracking control in comparison of a traditional proportional-integral controller and an adaptive backstepping controller.

In this article, a nonlinear adaptive fuzzy backstepping controller combined with an adaptive backstepping controller and an adaptive fuzzy controller is proposed for real-time tracking control of an electro-hydraulic force loading system. Firstly, a nonlinear dynamic model for the electro-hydraulic force loading system is built with consideration of parameter uncertainties and external disturbances. Then, the adaptive backstepping controller is employed to obtain desired control output for the force loading control system considering parameter uncertainties and external disturbances. Furthermore, an adaptive fuzzy control scheme is designed to adjust uncertain control parameters based on adaptive fuzzy system to cope with the chattering condition that results from the overwhelming external disturbances. The stability of the overall system with the proposed control algorithm can be proved by Lyapunov stability theory. Finally, an electro-hydraulic force loading experimental system with xPC rapid prototyping technology is carried out to verify the effectiveness of the proposed nonlinear adaptive fuzzy backstepping controller. Experimental results verify that the proposed control method exhibit excellent performances on force loading tracking control of the electro-hydraulic force loading experimental system compared with a conventional proportional-integral-derivative (PID) controller with velocity feedforward and adaptive backstepping control schemes.

In the fully mechanized mining face, proper chain tension of chain transmission system is of great significance in reducing the failure rate and improving the service life of scraper conveyor. However, the disturbance caused by random load and variable stiffness makes it very difficult to control chain tension. An adaptive tension control scheme combining a neural command filtering backstepping algorithm and online identification is proposed in this article based on a novel tension model. First of all, the proposed tension model is represented by a linear regression part and a nonlinear mapping part, which effectively reflects the chain transmission characteristics and avoids the full measurement of each chain ring. Then, Levant's differentiator and state observer are used to expand input variables for identification algorithm and neural networks estimation. To avoid the adverse effect of random load on parameter adaption, stiffness online identification algorithm is employed in improving the tension tracking performance. To compensate for nonlinear mapping part and uncertain nonlinearity caused by random load, neural adaptive estimator is effectively integrated with the robust integral term in backstepping design. Moreover, Lyapunov stability of the proposed controller is guaranteed. Finally, comparative experimental results of six controllers are completed to verify the effectiveness of the proposed controller in the static and random load mode.