An improved equivalent-input-disturbance (EID) approach is devised to enhance the disturbance-rejection performance for a strictly proper plant with a state delay in a modified repetitive-control system. A gain factor is introduced to construct an improved EID estimator. This increases the flexibility of system design and enables the adjustment of the dynamical performance of disturbance rejection. Moreover, the commutative condition, which is widely used for the conventional EID estimator, is avoided. Thus, it reduces the conservativeness of design by removing the constraints imposed by the commutative condition. The system is divided into two subsystems, and the separation theorem is applied to simplify the design. For one subsystem, the delay information on both the modified repetitive controller and the plant is used to reduce the conservativeness of stability condition. The resulting linear matrix inequality ('MI) is used to find the gain of the state-feedback controller. Another 'MI is derived to design the gains of the state observer and the improved EID estimator for the other subsystem. A case study on a metal-cutting system validates the superiority of the developed method.

A robust tracking control method is presented in this paper for an uncertain plant with an unknown state delay and an exogenous disturbance. The effects of the uncertainties, the delay, and the exogenous disturbance are treated as a total disturbance, thus the construction of the observer does not need the delay information. The system design is divided into the design of the gains of the state-feedback controller and the design of the gains of the observer and the improved equivalent-input-disturbance (EID) estimator. The pole-assignment method is used to design the gains of the state-feedback controller of a simplified system. A robust-stability condition in the form of a linear matrix inequality (LMI) is derived to determine the gains of the observer and the improved EID estimator. Since the devised Lyapunov functional is of a more general form than those in existing EID-based methods and the restrictive commutative condition is avoided in this design, the developed design method is less conservative. Finally, comparisons of the developed method with a sliding-mode control (SMC) method for a matched disturbance and a conventional EID-based method for an unmatched disturbance illustrate the validity and superiority of the developed method.

This paper presents an improved disturbance estimation and rejection method based on equivalent input disturbance (EID) with $H_{\infty}$ robust performance for a time-varying uncertain system. The analysis and synthesis are conduced in the frequency domain. Different from conventional two-degree-of-freedom (2-DOF) $H_{\infty}$ control, some related weighting functions are introduced into a new devised structure to separate the rejection of parameter uncertainties and disturbance from reference tracking. Compared with the existent EID-based approaches designed in the time domain of which the robust performance is not addressed, an $H_{\infty}$ robust performance of the closed-loop system is guaranteed in this design. Treating the difference between the real plant and an ideal plant, namely, the overall effect of parameter uncertainties and disturbance, as an EID, an EID estimation and compensation strategy is then established to cancel out the effect of the EID in system output. Further, a constant scaling matrix is introduced into system design to reduce the conservativeness of quadratic stabilization condition. Finally, a comparison with conventional 2-DOF robust $H_{\infty}$ control illustrates the advantages of the developed method.

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.