Recent advances on advanced control strategies for valves with deadband and nonlinear gain

This review synthesizes and critically evaluates the recent literature on advanced control strategies for industrial valves subject to severe nonlinearities, including deadband, stiction, and nonlinear gain. We trace the evolution from established model-based robust and adaptive methods, such as Sliding Mode Control (SMC) and Adaptive Robust Control (ARC), to the increasingly prevalent optimization-based and intelligent paradigms, including Model Predictive Control (MPC) and Reinforcement Learning (RL). Key themes analyzed include the trade-offs between model fidelity and controller complexity, the shift from direct nonlinearity compensation to observer-based disturbance rejection, and the emergence of hybrid control architectures. Through a comparative analysis, we distill the performance trade-offs inherent in different strategies concerning tracking accuracy, disturbance rejection, and actuator preservation. We identify critical research gaps, including the need for verifiable and safe learning-based controllers, the development of physics-informed AI, and the exploration of valve-controller co-design. The review concludes that while no single strategy is universally superior, the field is advancing towards a sophisticated toolbox of specialized, often hybrid, solutions, with significant future potential in data-driven and learning-based methods that are rigorously integrated with established control theory.