Abstract
This paper addresses the event-driven-modular optimal tracking control problem for nonlinear strict-feedback systems with external disturbances. Through the backstepping feedforward control, the optimal tracking problem is transformed into an equivalent optimal regulation problem of affine tracking error system. Subsequently, adaptive dynamic programming technique is introduced to generate the optimal feedback controller, and solve the optimization problem of two-player zero-sum differential game. A single critic neural network is constructed to evaluate the associated cost function online, where the novel weight updating law is derived based on the gradient-descent technique. The resulting event-triggered closed-loop system, modeled as an impulsive system, is proved to be asymptotically stable by Lyapunov theory. Finally, the reliability and effectiveness of the theoretical results is validated by numerical simulation examples.
Highlights
During the past decades, research on nonlinear strictfeedback systems have drawn considerable attention, i.e., hypersonic flight vehicle [1], inverted pendulum system [2], quadrotor [3], helicopter [4] ship autopilot [5], and robot manipulator [6], as the nonlinear general system satisfying certain geometric conditions can be transformed into strict-feedback form by the diffeomorphism theory
Considering high-order nonlinear multiagent systems in semi-strict-feedback form, the neural network state observer is constructed for each follower and an adaptive consensus tracking control is studied via backstepping techniques [7]
An optimal tracking control scheme, which is composed of backstepping-generated adaptive feedforward control and dynamic programming-based optimal feedback control, is proposed for continuous-time strict feedback systems [19]
Summary
Research on nonlinear strictfeedback systems have drawn considerable attention, i.e., hypersonic flight vehicle [1], inverted pendulum system [2], quadrotor [3], helicopter [4] ship autopilot [5], and robot manipulator [6], as the nonlinear general system satisfying certain geometric conditions can be transformed into strict-feedback form by the diffeomorphism theory. Considering high-order nonlinear multiagent systems in semi-strict-feedback form, the neural network state observer is constructed for each follower and an adaptive consensus tracking control is studied via backstepping techniques [7]. In view of the strict feedback system, adaptive dynamic programming(ADP) is proposed to design the optimal control and minimize some predefined performance cost function. An optimal tracking control scheme, which is composed of backstepping-generated adaptive feedforward control and dynamic programming-based optimal feedback control, is proposed for continuous-time strict feedback systems [19]. For multi-agent systems in strictfeedback form with a fixed directed graph, the commandfiltered backstepping and adaptive dynamic programming technique are introduced to investigate the distributed fuzzy optimal tracking control [21].
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