Control engineering plays an indispensable role in enhancing safety, improving comfort, and reducing fuel consumption and emissions for various industries, for which system identification, control, and optimization are primary topics. Alternatively, artificial intelligence (AI) is a leading, multi-disciplinary technology, which tries to incorporate human learning and reasoning into machines or systems. AI exploits data to improve accuracy, efficiency, and intelligence, which is beneficial, especially in complex and challenging cases. The rapid progress of AI facilitates major changes in control engineering and is helping advance the next generation of system identification, control, and optimization methods. In this study, we review the developments, key technologies, and recent advancements of AI-based system identification, control, and optimization methods, as well as present potential future research directions.

In this paper, a novel disturbance rejection approach is developed for a class of nonlinear systems, which incorporates the Kalman filter into the equivalent-input-disturbance (EID) structure. First, the EID-based control system and the configuration of the Kalman Filter are illustrated. Then, an optimal estimation theorem is obtained by analyzing the dynamic of the Kalman filter, and a special Kalman gain is derived. Next, the dynamics of the closed-loop system are deduced, and stability is guaranteed based on the Lyapunov function. Finally, simulations with comparison are carried out to demonstrate the excellent performance of both disturbance rejection and noise attenuation.