Dual heuristic programming has gained an increasing interest in recent years because it provides an effective process for optimal adaptive control of uncertain nonlinear systems. However, it requires an off-line stage to train a global system model from a representative model, which is often infeasible to obtain in practice. This paper presents a new and efficient approach for online self-learning control based on dual heuristic programming. This method uses a recursive least square method to online identify an incremental model of the system instead of a global system model. The presented incremental model based dual heuristic programming method can adaptively generate a near-optimal controller online without a priori information of the system dynamics or an off-line training stage. To compare the online adaptability of the conventional dual heuristic programming method and the newly proposed method, two numerical experiments are performed: an online reference tracking task and a fault-tolerant control task. The results reveal that the proposed method outperforms the conventional dual heuristic programming method in online learning capacity, efficiency, accuracy, and robustness.
Control Engineering Practice – Elsevier
Published: Apr 1, 2018
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