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In this brief, the problem of tracking control for uncertain nonlinear systems with periodic disturbances is studied. Based on the prescribed performance technology, two kinds of error transformation functions different from the traditional backstepping method are introduced, such that the proposed low-computation adaptive control method can ensure the boundedness of the signals of the closed-loop system. Meanwhile, the counter evidence method reveals that the system output can achieve accurate tracking. Compared with existing results, firstly, the complexity explosion problem is avoided without introducing a filter, making our result more applicable and less complex. Secondly, for the time-dependent periodic disturbances considered in the system, a function approximator based on Fourier series expansion can achieve accurate approximation with the least number of Gaussian basis functions. Comparative simulation results of the van der Pol oscillator illustrate the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

In this article, a command filtering‐based adaptive event‐triggered neural network control scheme is proposed for a class of uncertain switched nonlinear systems with unknown control coefficient and input saturation. First, radial basis function neural networks are used as function approximators to estimate unknown nonlinear functions. Then, an event‐triggering mechanism based on the tracking error is introduced to avoid the over‐consumption of communication resources. Furthermore, command filters are employed to solve the problem of complexity explosion that exists in conventional backstepping control design, and the error compensation signals are designed to reduce the errors caused by the filters. Considering that the unknown control gain and input saturation exist in many practical applications, a Nussbaum‐type function is thus introduced into the controller design to address these challenging issues. Finally, stability of the closed‐loop system is strictly proven under a standard Lyapunov stability analysis framework. The effectiveness of the proposed control scheme is illustrated by a simulation example. [ABSTRACT FROM AUTHOR]

This paper considers the issue of adaptive neural decentralised tracking control for a class of output-constraint switched interconnected nonlinear systems with unknown backlash-like hysteresis control input. First, neural networks (NNs) are applied to approximate unknown nonlinear functions, and an NNs switched state observer is designed to estimate unmeasured system states. Then, the dynamic surface control technique is used to avoid the influence of explosion of complexity. In addition, the problem of output constraints is solved by introducing the barrier Lyapunov functions. Based on the Lyapunov stability theory, all signals in the switched closed-loop system can be verified to be uniformly ultimately bounded under the proposed control method. Moreover, the system output can track the target trajectory well within a small bounded error. Finally, a numerical simulation result is given to illustrate the effectiveness of the adaptive decentralised control scheme. [ABSTRACT FROM AUTHOR]