Data‐Based Optimal Guaranteed Cost Control for Uncertain Constrained‐Input Nonlinear Systems

ABSTRACT This paper investigates the data‐driven optimal guaranteed cost control problem for a class of uncertain nonlinear systems subject to bounded parameter perturbations and input constraints. To relax the stringent existence conditions of the controller, an auxiliary system with a modified cost function is reformulated. Through this transformation, the original robust control problem is converted into equivalent optimal control problems under restricted actuator outputs. Subsequently, a neural network‐based reinforcement learning algorithm is developed to approximately solve the complex Hamilton–Jacobi–Bellman equation. By utilizing real system data within a model‐free iterative learning framework, the parameterized representation of the optimal policy is achieved, strictly avoiding the identification of unknown model dynamics. Furthermore, a rigorous convergence analysis of the proposed iterative algorithm is provided. Finally, simulation examples are presented to illustrate the feasibility and effectiveness of the proposed theoretical results.