The issue of neural adaptive self-triggered tracking control for uncertain nonlinear systems with input hysteresis is considered. Combining radial basis function neural networks (RBFNNs) and adaptive backstepping technique, an adaptive self-triggered tracking control approach is developed, where the next trigger instant is determined by the current information. Compared with the event-triggered control mechanism, its biggest advantage is that it does not need to continuously monitor the trigger condition of the system, which is convenient for physical realization. By the proposed controller, the hysteresis's effect can be compensated effectively and the tracking error can be bounded by an explicit function of design parameters. Simultaneously, all other signals in the closed-loop system can be remaining bounded. Finally, two examples are presented to verify the effectiveness of the proposed method.

The fixed-time formation control problem for uncertain nonlinear multiagent systems (MASs) with time-varying actuator failures is investigated. Actuator failures would have a huge impact on the system performance, especially when the actuator failures are time-varying, which may even cause system insecurity. In order to cope with time-varying actuator failures, a fixed-time convergence approach is proposed based on adaptive fuzzy control technology. Simultaneously, solving the above problems would aggravate the occupation of communication resources of the system. Therefore, a periodic adaptive event-triggered control scheme is developed, in which the triggering period can be adjusted adaptively. Furthermore, the convergence time of formation errors can be preset by applying the fixed-time control method. The convergence velocity can be accelerated immensely. Eventually, theoretical analysis and simulation illustrate that the proposed method can effectively compensate the time-varying actuator failures, and the MASs can achieve formation within a fixed time by using less communication resources.

In this paper, the uncertain direct of the hysteretic system component will be considered. Besides, the effect of stochastic disturbance inevitably exists in many practical systems, which would cause the instability. Simultaneously, it is significant to guarantee the perfect error tracking performance for the uncertain nonlinear hysteresis systems when operation suffers the failure. To ensure the maintaining acceptable system performance in reality, the new properties of the Nussbaum function are proposed, and an auxiliary virtual controller is designed through the neural network (NN) universal approximator. Furthermore, it is challenged to save the system-limited transmutation resource for nonlinear systems, especially for stochastic nonlinear systems, with unknown hysteresis input and actuator failures. The coupling effect of the system communication resource constrains has to arise the issue of the mutual coupling function, which makes that the tracking control design is more complicated. Using the proposed event-triggered controller and back-stepping technology, a new optimization algorithm is proposed to ensure that the states of the closed-loop system and the tracking error remain bounded in probability. Finally, to illustrate the effectiveness of our proposed adaptive NN control method with the event-triggered strategy, some numerical examples are provided.

In this paper, an adaptive neural network control for stochastic nonlinear systems with uncertain disturbances is proposed. The neural network is considered to approximate an uncertain function in a nonlinear system. And computational burden in operation is reduced by handling the norm of the neural-network vector. However, it will arise chattering issue, which is a challenge to avoid it from the symbolic operation. Further, traditional schemes often view error of estimate as bounded constant, but it is a time-varying function exactly, which may lead control schemes cannot conform to practical situation and guarantee stability of systems. Thus, backstepping technology and the neural network technology combined to stabilize stochastic nonlinear systems together to handle the aforementioned issues. It is proved that the proposed control scheme can guarantee the satisfactory asymptotic convergence performance and predetermined transient tracking error performance. From simulation results, the proposed control scheme is verified that can guarantee the satisfactory effectiveness.