Adaptive Dynamic Event-Triggered Asymptotic Tracking Control for Strict-Feedback Nonlinear Systems With a Self-Adjusting Performance Function

Abstract

The issue of adaptive dynamic event-triggered asymptotic tracking control for strict-feedback nonlinear systems with unknown functions and full-state prescribed-performance constraints is discussed in this paper. A novel self-adjusting performance function (SAPF) is constructed by merging a continuous function with a finite-time performance function. By associating a transformation function and SAPF, the state constraints problem is recast into analyzing the boundedness of the new variables. Moreover, a new dual dynamic variable-dependent event-triggered mechanism is provided to reduce redundant data transmission. By using a command-filter technique and neural network method, a control scheme is proposed to guarantee the system output asymptotically tracks the reference signal and all system states satisfy specified constraints. Lastly, an applied example is introduced to illustrate the effectiveness of the proposed scheme. Note to Practitioners—In reality, many practical systems, such as flexible manipulators and unmanned underwater vehicles, need to operate in a constrained region. Motivated by this, we design controller for nonlinear systems to guarantee steady-state and transient performance. To reduce the conservatism, a novel SAPF is constructed and then a prescribed performance control strategy is proposed, which can not only achieve the asymptotical tracking of the nonlinear system, but also ensure the constraint performance. In addition, to decrease the transmitted data via communication network, a new dual dynamic variable-dependent event-triggered mechanism is proposed. This method has been illustrated to be feasible via a simulation example.