In this article, the sliding mode control issue is investigated for a class of discrete-time Takagi–Sugeno fuzzy networked singularly perturbed systems via an observer-based technique. Moreover, to process the measurement output and schedule the transmission sequence for relieving the communication burden, a logarithmic quantizer and a weighted try-once-discard protocol are synthesized, which can further improve the network bandwidth utilization in networked control systems. Based on the fuzzy observer states, a novel fuzzy sliding surface is established by considering the singularly perturbed parameter properly, and we endeavor to synthesize a sliding mode control law such that the reachability of the prescribed sliding surface could be guaranteed. In addition, by virtue of the convex optimization theory and Lyapunov approach, sufficient conditions are developed to guarantee the asymptotic stability of the sliding mode dynamics as well as the error system with an expected $H_{\infty }$ performance. Finally, a verification example is presented to illustrate the feasibility and effectivity of the proposed method.

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.

In this paper, an adaptive fuzzy controller is constructed to address the finite-time tracking control problem for a class of strict-feedback nonlinear systems, where the full state constraints are strictly required in the systems. Backstepping design with a tan-type barrier Lyapunov function is proposed. Meanwhile, fuzzy logic systems are used to approximate the unknown nonlinear functions. The addressed control scheme guarantees that the output is followed the reference signals within a bounded error, and all the signals in the closed-loop system are bounded. The simulation results demonstrate the validity of the proposed method.

This paper investigates the non-fragile $H_{\infty}$ synchronization issue for a class of discrete-time T-S fuzzy Markov jump systems. With regard to the T-S fuzzy model, a novel processing method based on matrix transformation is introduced to deal with the double summation inequality containing fuzzy weighting functions, which may be beneficial to obtain conditions with less conservatism. In view of the fact that the uncertainties may occur randomly in the execution of actuator, a non-fragile controller design scheme is presented by virtue of the Bernoulli distributed white sequence. The main novelty of this paper lies in that the transition probabilities of Markov chain are considered to be piecewise time-varying, and whose variation characteristics are described by the persistent dwell-time switching regularity. Then, based on Lyapunov stability theory, it is concluded that the resulting synchronization error system is mean-square exponentially stable with a prescribed $H_{\infty}$ performance in the presence of actuator gain variations. Finally, an illustrative example about Lorenz chaotic systems is provided to show the effectiveness of the established results.

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