Machine learning model-based optimal tracking control of nonlinear affine systems with safety constraints

Abstract

This work focuses on the development of a machine learning (ML) model-based framework for safe optimal tracking control of a class of nonlinear control-affine systems to ensure simultaneous closed-loop stability and safety. Specifically, a novel multilayer feedforward neural network (FNN) with a control-affine architecture is designed to model nonlinear dynamic systems. Subsequently, a model-based reinforcement learning (RL) framework is presented, utilizing a novel cost function with Control Lyapunov-Barrier Function (CLBF) properties, to learn both the control policy and the optimal value function for an infinite-horizon optimal tracking control problem for nonlinear systems with safety constraints. The efficacy of the proposed methodology is demonstrated through simulations of a one-link robot manipulator and a chemical process example.