Closed Loop Stability

Closed-loop stability research focuses on ensuring the reliable and safe operation of control systems, particularly those incorporating complex models like neural networks or facing uncertainties. Current efforts concentrate on developing methods that guarantee stability during learning and operation, often employing techniques like Lyapunov analysis, Bayesian optimization, and model predictive control (MPC) alongside neural network architectures such as recurrent equilibrium networks (RENs) and gated recurrent units (GRUs). These advancements are crucial for deploying advanced control systems in safety-critical applications, improving performance while mitigating risks associated with instability and unpredictable behavior.