In intelligent electric vehicle (EV) driving technology, the high safety and accuracy of steering system is an important concern. This article presents a dual-motor steer-by-wire (DMSBW) system to improve reliability and safety of the steering system for intelligent EV. However, the parameter uncertainties, time-varying road disturbances, and severe failures of the steering motors in the dual-motor SBW system deteriorates the vehicle tracking performance. In order to solve the above problems, an adaptive fault-tolerant control method is presented integrated with variable-exponential fixed-time sliding-mode control (VEFTSMC) technology and neural network (NN) for the dual-motor SBW system, in which the neural network is used to approximate the system uncertainty term. First, for the dual-motor SBW system loss of effectiveness, an adaptive law is developed to estimate the unknown fault coefficient. Then, considering the unknown upper bound of the time-varying disturbances, the switching function containing the disturbance term is designed to improve the robustness. Furthermore, considering the transient response, a VEFTSMC is developed for the dual-motor SBW system, which has better convergence rate than conventional SMC. Experimental results are given to validate the effectiveness of the proposed control strategy. The verification results show that the proposed control strategy can improve the stability under the parameters disturbances and different types of actuator faults.

This paper presents a composite control strategy for gearless coal mill to improve the disturbance immunity under low-speed variable operating conditions. First, the gearless coal mill encounters power supply voltage fluctuations, mechanical failures, or ambient temperature changes during operation. These situations can cause the system to suffer from the problem of insufficient control accuracy of the rotational speed. Therefore, a non-singular fast terminal sliding mode control strategy is proposed to improve the speed response. Then, to address the problem of load perturbation caused by different coal quality, this paper designs the extended state observer. Feed-forward compensation of the perturbation is performed to improve the robustness. Finally, due to the parameter mismatch problem caused by heat in operations that take a long time, this paper proposes a sliding-mode-based deadbeat predictive current control. The strategy possesses the fast dynamic response of deadbeat predictive current control while retaining the strong robustness of sliding mode control. Lyapunov proved the stability of the proposed control strategy. The experimental results verified that the proposed control strategy had better control performance.