Dual heuristic dynamic programming based event-triggered control for nonlinear continuous-time systems

Abstract

A novel event-triggered approach for a class of nonlinear continuous-time system is proposed in this paper to reduce the computation cost of the dual heuristic dynamic programming (DHP) algorithm. Two neural networks are included in our design. A critic network is used to estimate the partial derivatives of the cost function with respect to its inputs, and an action network is used to approximate the optimal control law. Instead of periodical sampling in the traditional DHP approach, under the event-triggered mechanism, both of the neural networks are only updated at the jump instants, and kept constant during the inter-event time. With the designed trigger threshold, the proposed DHP-based event-triggered approach can save computation time significantly while obtaining competitive control performance when comparing with those of the traditional DHP approach. Two simulation tests are presented to verify the theoretical results.