Model-free self-triggered control based on deep reinforcement learning for unknown nonlinear systems

This article proposes a joint learning technique for control inputs and triggering intervals of self-triggered control nonlinear systems with unknown dynamics. First, deep reinforcement learning is introduced to the self-triggered control system by considering both the control performance and triggering performance in the reward function. Then, the control inputs and triggering intervals are simultaneously learned by the developed deep deterministic policy gradient approach. Under this strategy, not only the desired control performance is guaranteed for unknown nonlinear systems, but also both the computation and communication occupation for the controlled system are decreased without any triggering thresholds. Finally, simulations for the cart-pole swing-up system are illustrated to verify the effectiveness of the proposed scheme.