Abstract
The prescribed finite-time bounded-H∞ optimal tracking control (OTC) problem is investigated for a class of nonlinear systems with unknown initial tracking condition and external disturbances in this paper. A novel variable barrier function is designed to develop a new approach of prescribed performance control (PPC) which is irrelevant to the initial condition of the constrained variable in the system. Meanwhile, the reinforcement learning (RL) method with the actor–critic architecture is used to optimize the control input of the system in order to obtain the least energy consumption. By means of a newly proposed prescribed finite-time performance function (PFTPF), a prescribed finite-time optimal tracking controller is obtained regardless of initial tracking condition of the system. At the same time, the bounded-H∞ control problem is considered for the external disturbances in the system. The designed controller not only ensures the boundedness of all signals in the closed-loop system, but also has a finite-time control performance and an H∞ anti-interference performance. Finally, the effectiveness and the superiority of this proposed method are verified by the simulations.
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