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Online adaptive controller design of partially unknown nonlinear systems with state time-delay via actor-critic architecture

The paper is concerned with online adaptive control for partially unknown nonlinear discrete-time time delayed systems. The main object is to establish an online adaptive control strategy using reinforcement learning principle, so that the long cost function can be optimized and the closed time delayed system is stable. The reinforcement learning based adaptive controller contains two neural networks(NNs), one is to give the reasonable control law and another is to evaluate the actor network performance. By utilizing the actor-critic (AC) architecture, the reasonable control law can be reached finally and the optimal long performance function can be approximated. An example is illustrated so as to show the practicability of the provided adaptive control strategy.