This research paper studies the consensus tracking control problem for a class of uncertain non-affine non-linear multi-agent systems (MASs). First, different from the separation design scheme using the mean value theorem in previous works, this research paper not only uses the mean value theorem but also introduces the Taylor decoupling method to decouple the complex unknown non-affine structure. Second, to solve the difficulty of unknown non-linear functions in non-linear MASs, an intelligent technique based on neural networks was used. In addition, compared with the existing traditional event-triggered control strategy based on the relative threshold, an improved event-triggered control strategy based on the decreasing function of error variables was introduced to reduce the waste of unnecessary resources. The theoretical result shows that the whole closed-loop system is stable under the action of the proposed control protocol. Finally, the simulation experiment verifies the effectiveness of our control method.
In this work, the issue of event-triggered-based asymptotic tracking adaptive control of stochastic nonlinear systems in pure-feedback form with strong interconnections is considered. First, a new decentralized control scheme is developed by introducing the new types of Nussbaum functions, which enables the output of each subsystem to asymptotically track the desired reference signal. Second, the nonaffine structures and the unknown control gains existing in the nonlinear systems are a part of the considered system model, which makes it more complicated to design the decentralized controllers. Therefore, the complexity caused by the nonaffine structures is faciliated by mean value theorem and the unknown control gains are handled by a novel Nussbaum function in our proposed design scheme. Meanwhile, the unknown nonlinearities of the system are approximated by using intelligent control technology. Furthermore, an event-triggered method is introduced in the design process to save communication resources effectively. It is shown that all signals of the closed-loop systems are bounded in probability and the tracking errors asymptotically converge to zero in probability. Finally, the simulation results illustrate the effectivity of the presented scheme.
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