Adaptive Event-triggered Control with Prescribed Performance for Nonlinear System with Full-state Constraints.

An adaptive event-triggered tracking control method with preset performance is proposed for a class of uncertain nonlinear systems with full-state constraints and unknown time-varying disturbances. Firstly, a performance function is designed and the original system error is converted. In order to eliminate the influence of state constraints on the system, a barrier Lyapunov function is introduced. Secondly, the influence of unknown function is solved by using radial basis function neural network (RBFNN). Simultaneously, eventtriggering mechanism was used to reduce the update frequency of control signals, thereby the communication burden is reduced. Then, based on the Lyapunov stability theorem, all signals of the closed-loop control system are verified to be bounded, all states do not violate the predefined interval, and the Zeno behavior does not occur. Finally, the effectiveness of the proposed method was verified using a single-link flexible manipulator as a simulation example. [ABSTRACT FROM AUTHOR]