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摘要
In this paper, an event-triggered heuristic dynamic programming algorithm for discrete-time nonlinear systems with a novel triggering condition is studied. Different from traditional heuristic dynamic programming algorithms, the control law in this algorithm will only be updated when the triggering condition is satisfied to reduce the computational burden. Three neural networks are employed, which are model network, action network, and critic network. Model functions, control laws, and value functions are estimated using neural networks, respectively. The main contribution of this algorithm is the novel triggering condition with simpler form and fewer assumptions. Additionally, a proof of the stability for discrete-time systems using Lyapunov technique is given. Finally, two simulations are shown to verify the effectiveness of the developed algorithm.
