This paper focuses on the practical output tracking control for a category of high-order uncertain nonlinear systems with full-state constraints. A high-order tan-type barrier Lyapunov function (BLF) is constructed to handle the full-state constraints of the control systems. By the BLF and combining a backstepping design technique, an adding a power integrator, and a fuzzy control, the proposed approach can control high-order uncertain nonlinear system with full-state constraints. A novel controller is designed to ensure that the tracking errors approach to an arbitrarily small neighborhood of zero, and the constraints on system states are not violated. The numerical example demonstrates effectiveness of the proposed control method.

This paper reports our study on the issue of finite-time adaptive fuzzy tracking control for a class of uncertain nonlinear systems with prescribed performance. A novel finite-time prescribed performance control approach, in which fuzzy systems are employed to approximate completely unknown nonlinear functions, is proposed by incorporating the technique of prescribed performance control with the method of command filtered design. Based on the finite-time stability theory, all the signals of the closed-loop system can be bounded and the output tracking error can converge to a prescribed small region within a finite-time by the proposed scheme; meanwhile, the problem of complexity explosion can be avoided. The effectiveness of the presented method is verified by two examples.

This paper reports our study on adaptive fuzzy tracking control for flexible-joint robots with full state constraints. In the control design, fuzzy systems are adopted to identify the totally unknown nonlinear functions and can properly avoid burdensome computations. The tan-type barrier Lyapunov functions are used to deal with state constraints so that even without state constraints, the controller is still valid. By combining the method of backstepping design with adaptive fuzzy control approaches, a novel simpler controller is successfully constructed to ensure that the output tracking errors converge to a sufficiently small neighborhood of the origin, while the constraints on the system states will not be violated during operation. Finally, comparison simulations are presented to demonstrate the effectiveness of the proposed control schemes.

In this paper, the issue of finite-time command filterbased adaptive fuzzy tracking control based on event-triggered scheme for stochastic strict-feedback nonlinear systems is studied. By using fuzzy logic system, finite-time command filter with compensation signals, an event-triggered adaptive controller is designed. The newly designed controller not only guarantees the property of finite-time convergence, but also reduces the communication burden from the controller to the actuator. Meanwhile, the problem of complexity explosion caused by backstepping method is avoided by using command filter technology. The proposed controller can ensure the output signal tracks the given reference signal under the bounded error. Finally, the simulation result proves the effectiveness of the proposed control method

This article reports our study on a reduced adaptive fuzzy decoupling control for our lower limb exoskeleton system which typically is a multi-input-multi-output (MIMO) uncertain nonlinear system. To show the applicability and generality of the proposed control methods, a more general MIMO uncertain nonlinear system model is considered. By decoupling control, the entire MIMO system is separated into several MISO subsystems. In our experiments, such a system may have problems (even unstable) if a traditional fuzzy approximator is used to estimate the complicated coupling terms. In this article, to overcome this problem, a reduced adaptive fuzzy system together with a compensation term is proposed. Compared to traditional approaches, the proposed fuzzy control approach can reduce possible chattering phenomena and achieve better control performance. By employing the proposed control scheme to an actual 2-DOF lower limb exoskeleton rehabilitation robot system, it can be seen from the experimental results that, as expected, it has good performance to track the model trajectory of a human walking gait. Therefore, it can be concluded that the developed approach is effective for the control of a lower limb exoskeleton system.

This paper reports our study on adaptive tracking control for a mobile-wheeled inverted pendulum with periodic disturbances and parametric uncertainties. With an appropriate reduced dynamic model, incorporating repetitive learning strategies with dynamic decoupling and related adaptive control techniques, a novel controller is successfully constructed to ensure that the output tracking errors of the system will stay within a small neighborhood around zero and all of the other signals are semiglobal uniform bounded. Meanwhile, only one parameter estimation is used for adaptive controller design, which overcomes the problem of over-parametrization. Furthermore, a required condition of period identifier mechanisms is proposed. Finally, detailed simulation results are presented to demonstrate the effectiveness of the proposed control schemes.