Unknown Nonlinear System

Controlling unknown nonlinear systems is a significant challenge in control theory, with research focusing on developing robust and stable controllers without relying on explicit system models. Current approaches leverage reinforcement learning algorithms, often incorporating neural networks (e.g., recurrent neural networks, deep neural networks) to learn system dynamics and optimal control policies, sometimes augmented by techniques like Lyapunov function learning for stability guarantees. These methods aim to improve control performance and provide theoretical assurances of stability and convergence, impacting diverse fields from robotics to process control by enabling effective control of complex, poorly understood systems.