Reinforcement Learning-based Adaptive Control Optimization for Nonlinear Systems

This paper introduces a novel approach to adaptive control optimization for nonlinear systems using reinforcement learning (RL). The proposed algorithm, RL-ACOS (Reinforcement Learning-based Adaptive Control Optimization System), employs RL to adjust control strategies dynamically, responding to real-time environmental feedback. By merging RL with adaptive control techniques, this algorithm optimizes system performance, enhances stability, and bolsters resilience to nonlinear disturbances and uncertainties. Through extensive simulation experiments, we demonstrate that RL-ACOS outperforms conventional control methods in terms of both convergence speed and overall system efficiency, offering a promising solution for complex nonlinear systems in real-world applications.