Optimal Tracking Control for Constrained Two-Link Robot Based on Adaptive Dynamic Programming

This paper introduces a novel state and input constrained optimal tracking control method to address limitations posed by only partially known robot system models and constrained physical variables, such as joint position, velocity, and torque during the control process. The method employs the slack function and nonquadratic function methods to effectively handle state error and input constraints, ensuring the overall stationarity and safety of the control process. Subsequently, the adaptive dynamic programming (ADP) algorithm is designed to formulate update laws for the approximating neural networks, enabling the derivation of optimal control solutions without relying on an internal dynamic model of the system. Finally, convergence and performance are validated through simulation experiments, confirming its efficacy in managing constraints and demonstrating its application perspectives in real-world scenarios.