Hybrid Physics-Informed Reinforcement Learning for Adaptive Control of Nonlinear Systems

Abstract

The problem with adaptive control of nonlinear systems is that model uncertainty, dynamic variations and constraints of conventional methods make such control difficult. Despite reinforcing learning (RL) being a data-driven solution, it is commonly characterized by low sample efficiency, instability, as well as physical inconsistency. In order to address them, this paper will suggest a hybrid physics-informed reinforcement learning (PI-RL) architecture to adaptive control of nonlinear dynamical systems. The suggested approach combines the controlling physical principles with the training procedure by introducing a physics-constrained learning objective, which guarantees physically consistent and stable learning of policies. The model incorporates model-based knowledge and model-free learning to improve the rate of convergence, stability and efficiency of data. There is an adaptive mechanism that will allow real-time reactiveness to uncertainties and external disturbances. The given strategy is tested on nonlinear benchmark systems and compared to the standard RL and classical control strategies. Findings have shown better tracking of the trajectories, convergence, and performance across different conditions. This PI-RL hybrid is a scalable and efficient model that can be used to make intelligent control of complex nonlinear systems in advanced engineering practices.