This paper investigates the problem of adaptive event-triggered control with prescribed performance for a class of strictly feedback-controlled nonlinear systems with unknown initial tracking conditions. To overcome the dependence of traditional prescribed performance control on the system’s initial tracking conditions, this paper introduces a novel algebraic saturation function that first maps tracking error initial values of arbitrary magnitude to a bounded interval and then imposes predefined performance constraints on this bounded interval. This strategy ensures that, even when the system’s initial state is unknown, the tracking error still converges to a small neighborhood near the equilibrium point in accordance with the prescribed performance. Furthermore, the strategy employs a fixed-threshold event-triggered mechanism, which effectively reduces the system’s update frequency and alleviates the communication load. Furthermore, by combining a logarithmic barrier Lyapunov function with neural network-based unknown function approximation techniques, this strategy proposes an adaptive prescribed performance event-triggered controller that is independent of the system’s initial state, in other words, independent of the initial tracking conditions. Simulation results validate the effectiveness and superiority of the proposed controller.
Liu et al. (Wed,) studied this question.