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 studies an adaptive control problem of pure‐feedback non‐linear systems with full state constraints. The mean value theorem is employed to deal with unknown non‐linearities. A novel backstepping design is constructed via barrier Lyapunov function (BLF) combined with dynamic surface control (DSC). The BLF guarantees the full state constraints are not violated and all the closed‐loop signals remain bounded. DSC solves the problems of restrictions on high order differentiability of stabilising functions and avoiding the complexity that arises due to the explosion of terms in backstepping design. It is shown that all the signals in the closed‐loop system are ultimately bounded and the tracking error converges to an adjustable neighbourhood of the origin while the full state constraints remain unchanged. The performance of the BLF‐based DSC is illustrated with two simulation examples.

In this presented paper, the trajectory tracking control is considered for an autonomous underwater vehicle subjected to model uncertainties and output constraints. The tan-type barrier Lyapunov function is adopted to deal with the requirement of output constraints. In order to cope with the existing system uncertainties, the adaptive fuzzy is employed to approximate the unknown system parameters of an autonomous underwater vehicle. Both state feedback tracking controller and output feedback tracking controller are proposed in this work. The state feedback tracking control scheme is designed by comprehensive application of tan-type barrier Lyapunov function and adaptive control design provided that all system states are measurable. The output feedback tracking control scheme is proposed by employing a high-gain observer. Through analysis, it can be shown that the designed control laws are able to achieve the desired output constraints. In addition, all the signals of the resulting closed-loop system are semiglobally uniformly bounded. Finally, simulation is carried out to demonstrate the validity and feasibility of the designed control strategies.

A novel adaptive fuzzy control scheme is designed in this paper for a class of stochastic non-strict feedback nonlinear systems with output constraint and unknown control coefficients. A reduced adaptive fuzzy system is proposed to approximate the unknown function which contains all state variables of the whole system and ensures that the backstepping design method works normally for non-strict feedback nonlinear systems. With the use of this reduced adaptive fuzzy control method and a combination of Barrier Lyapunov Function control design and Nussbaum gain technique, a novel adaptive fuzzy controller is proposed to guarantee that the output tracking error always meets the given constraint requirement in a sense of probability and the resulting closed-loop states are bounded in probability. Finally, an example is presented to confirm the effectiveness of the designed method.

This article focuses on the design of a novel adaptive fuzzy event-triggered tracking control approach for a category of high-order uncertain nonlinear systems with prescribed performance requirements, in which a high-order tan-type barrier Lyapunov function (BLF) is employed to handle and analyze the output tracking error, fuzzy systems are adopted to identify the totally unknown nonlinear functions, and only one gain function rather than parameter estimation functions is designed to cancel out all unknowns appearing in fuzzy systems. As a result, complicated calculations are avoided and a structured simple control is achieved. The proposed controller not only ensures that the tracking error is always within a predefined region but also reduces the communication burden from the controller to the actuator. Finally, comparison simulations are presented to verify the effectiveness of the proposed control schemes.