In this article, an adaptive antisaturation fixed-time control method with a faster convergence rate is studied for uncertain robotic systems. First, a new segmental sliding variable is constructed to solve the singularity problem brought by the terminal sliding mode control (TSMC) and achieve a faster convergence rate. Second, to approximate and compensate for the model uncertainty and the viscous friction parameter, an adaptive neural network (ANN) is employed. Then, a novel auxiliary system is constructed to mitigate the effects of input saturation. Based on this, a novel nonsingular TSMC algorithm integrated with the ANN and the auxiliary system is designed, so that the trajectory tracking errors of the robotic system can converge within a faster fixed time with actuator saturation. Finally, the superiority and practicability of the present method are verified by comparative experiments.

In this article, a fixed-time observer-based sliding-mode control strategy is proposed for a permanent-magnet synchronous motor. Both the current regulation loop (inner loop) and the speed-tracking loop (outer loop) are controlled by the designed fixed-time convergence sliding-mode controller, improving the control performance and guaranteeing the convergence in a fixed-time manner. To guarantee the robustness of the proposed control algorithm, an observer is constructed to estimate and attenuate the parameter uncertainties and load disturbance in the outer loop. The fixed-time convergence of the inner and outer loop error systems are proved. Moreover, a parameter tuning method of the designed controller is proposed to provide guidance for practical applications. Finally, experiments are conducted by using three different control schemes, that is, PI control, super-twisting sliding-mode control, and the controller designed in this article, the results of which show the effectiveness and advantages of the proposed control scheme.

In this paper, the fixed-time control problem of DC-DC buck converter systems with mismatched disturbances is investigated. Firstly, the sliding mode fixed-time observers are constructed to estimate the matched and mismatched disturbances of the systems. Secondly, a novel segmented terminal sliding mode control (TSMC) variable considering the mismatched disturbance of the system is designed based on the observations. To improve the tracking performance, a new second-order fixed-time reaching law is proposed. Then, in order to make the DC-DC buck converter systems with mismatched disturbances achieve accurate control in a fixed-time independent of the initial state, a novel fixed-time nonsingular TSMC based on the fixed-time observers is proposed. Finally, comparative experiments are conducted to verify the effectiveness and practicality of the proposed control strategy.

This article proposes a novel adaptive disturbance observer-based fixed-time backstepping control algorithm for uncertain robotic systems. First, an adaptive sliding mode disturbance observer (ASMDO) is designed to compensate for the lumped disturbances of the robotic system, such as model uncertainty and friction. Second, considering the actuator saturation problem of the robotic system, a new adaptive fixed-time auxiliary system is developed. Then, based on the ASMDO and the adaptive auxiliary system, a novel fixed-time backstepping tracking control algorithm is proposed to achieve faster fixed-time high accuracy tracking control for uncertain robotic systems under actuator saturation. Finally, comparative experiments validate the practicality of the proposed faster fixed-time backstepping control algorithm. Both theoretical analysis and experimental results demonstrate the superiority of the proposed fixed-time backstepping control algorithm.

In this article, an improved adaptive neural network (NN) nonsingular terminal sliding mode control (NTSMC) scheme is proposed for prescribed-performance trajectory tracking of manipulators with unmodeled dynamics and input saturation. In order to reduce the adverse effect of input saturation due to the conflict between excessive control force and limited motor torque, an auxiliary system is constructed. With the help of prescribed performance functions, we develop an improved NN-based NTSMC strategy to achieve tunable prescribed tracking errors under limited control, where it does not need prior precise knowledge of uncertainties. Theoretically, the uniform ultimate boundedness of the closed-loop system is proved by using the Lyapunov function. Finally, extensive comparative experiments are carried out on a ROKAE platform of a multidegree-of-freedom manipulator, and the improved tracking performance of the proposed scheme is verified.

Both the proportional-integral (PI) controller and the super-twisting algorithm (STA) are attractive alternatives to effectively govern power converters. The PI controller is smooth but is less robust to disturbances, while the STA exhibits good robustness with respect to disturbances but suffers from the negative effect of chattering when some high control gains are required in actual applications. To combine the advantages of both controllers, this paper explores a novel observer-based varying exponent gain STA control scheme for the three-level neutral-point-clamped (NPC) converter. By introducing a time-varying exponent gain into the STA, the proposed novel varying exponent gain STA combines the attractive traits of the PI controller (smooth control input) and the STA (robustness and fast dynamic response) simultaneously. In this sense, the controller exhibits high performance in steady state as well as transient and provides strong robustness with respect to disturbances. In addition to the new controller, a higher-order sliding mode observer is integrated into the control scheme to compensate the disturbances and further enhance the disturbances rejection ability of the system. Finally, the effectiveness of the proposed control method is confirmed by comparing its results with the PI controller and the STA based on a set of experiments.