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The topic of adaptive event‐triggered control for multi‐input‐multi‐output nonlinear systems with asymmetric state constraints is considered in this article. First, by introducing unified barrier functions method, the initial system is transformed to a non‐constraints system, which brings that the requirement of feasibility conditions could be eliminated and the constraint functions could be relaxed effectively. Then, an adaptive tracking controller is designed by combining some excellent technology, where command filter is utilized to overcome the explosion of complexity, neural network is introduced to approximate unknown nonlinear function, and event‐triggered mechanism is proposed to save communication resources. The designed control scheme can make the outputs of the system to track the target trajectories within a small bounded error range, all signals in the closed‐loop system are bounded, and all states do not escape the state constraints. Finally, practical and comparative examples are given to verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

In this paper, the issue of adaptive neural networks event-triggered control for nonstrict-feedback nonlinear multi-input-multi-output(MIMO) systems containing unmeasured states is investigated. All unmeasured states are approximated by using neural networks observer. Meanwhile, the neural networks are used to estimate the unknown continuous function at each step of recursion. Then, an observer-based adaptive neural networks event-triggered tracking control strategy is proposed based on backstepping technique. The designed controller enables the outputs of the system to track the target trajectory within a small bounded error range, and all signals in the closed-loop system are bounded. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive backstepping control scheme based NNs technique for a class of un- certain nonlinear MIMO systems with unmeasured states and unmodeled dynamics. The contributions of this paper are listed as follows: • The unmodeled dynamics of the system are processed by a dynamic signal. Then, the combinational uncertainties caused by unmodeled dynamics, unknown nonlinear functions and dynamic disturbances are approximated by neural networks. • An observer is established to estimate the unmeasured states in the system. It is proved that the observer error vectors can be made arbitrarily small by selecting appropriate parameters. In this paper, an adaptive neural networks control scheme is developed for a class of multi input and multi output uncertain nonlinear systems with unmeasured states and unmodeled dynamics. In the control scheme, a dynamic signal is used to deal with the unmodeled dynamics and a neural observer is designed to estimate the unmeasured states. Meanwhile, the neural networks are used to estimate the combinational unknown nonlinear function at each step of backstepping process. It is proved that all signals of the closed-loop system are semi global uniformly ultimately bounded (SGUUB). Finally, a simulation example is provided to show the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]