This article considers the robust tracking control problem of uncertain nonlinear systems with asymmetric input constraints. Initially, the tracking error dynamics and the desired trajectory dynamics are constructed as an augmented system. Then, with a discounted value function being introduced for the nominal augmented system, the original tracking control problem is transformed into a constrained optimal control problem. To solve the constrained optimal control problem, its related Hamilton–Jacobi–Bellman equation (HJBE) is developed. After that, a critic approximator is constructed to solve the HJBE in the reinforcement learning framework. To tune the parameters used in the critic approximator, a novel concurrent learning technique is introduced, which could remove the persistence of excitation condition. Moreover, the uniform ultimate boundedness of the tracking error and the parameters' estimation error of the critic approximator is assured via the Lyapunov's approach. Finally, a spring‐mass‐damper mechanical system and a robot manipulator system are given to validate the theoretical claims.

This article presents a decentralized neuro‐control scheme for a class of continuous‐time nonlinear systems with mismatched interconnections through an optimal control method. The decentralized control problem of the studied nonlinear‐interconnected systems is transformed into a group of optimal control problems of auxiliary subsystems. First, the value functions of auxiliary subsystems are designed, which are related to the boundaries of interconnected terms. It is proved that, under certain conditions, the decentralized control consisting of optimal control policies of auxiliary subsystems can stabilize the entire interconnected system. Then, an adaptive dynamic programming algorithm is presented to solve the Hamilton–Jacobi–Bellman equations. Critic neural networks (NNs) are employed to approximate the value functions of auxiliary subsystems in order to acquire the optimal control policies. Moreover, the auxiliary subsystems' states and the critic NNs' weight estimation errors are proved to be uniformly ultimately bounded. Finally, a numerical example and an interconnected power system are provided to validate the proposed decentralized neuro‐control strategy.