Event‐triggered adaptive neural network control design for stochastic nonlinear systems with output constraint

Abstract

SummaryThis paper is concerned with the adaptive neural network event‐triggered control (ETC) problem for stochastic nonlinear systems with output constraint. The influence of stochastic disturbance inevitably exists in many practical systems, which leads to system instability. Meanwhile, a novel tan type barrier Lyapunov function (Tan‐BLF) structure is proposed to deal with the constraint requirements of stochastic systems. In the sense of probability, the output constraints will not be violated during the operation of the system. In addition, the ETC strategy is adopted to reduce the burden of communication. The asymptotic stability of the closed‐loop system is guaranteed without violating output constraints. Meanwhile, the tracking error converges to a small region of the origin. Two simulations results demonstrate the effectiveness of theoretical analysis.

Fixed Time Synchronization of Stochastic Takagi–Sugeno Fuzzy Recurrent Neural Networks with Distributed Delay under Feedback and Adaptive Controls

“…In practice, the neural network often can not be automatically implemented and usually needs a suitable controller to be designed. Hence, researchers have come up with various control methods and techniques to achieve synchronization, including feedback control [23], adaptive control [24], and so on. These control methods provide great help to solve the problem of system synchronization and promote the development and progress of technology.…”