Iterative Learning Control Integrated with Model Predictive Control for Real-Time Disturbance Rejection of Batch Processes

Abstract

In the present paper, iterative learning control (ILC) is integrated with a model predictive control (MPC) technique to reject real-time disturbances. The proposed scheme is called iterative learning model predictive control (ILMPC). ILC is an e ective control technique for batch processes, but it is not a real-time feedback controller. Thus, it should be combined with MPC for real-time disturbance rejection. The existing ILMPC techniques make the error converge to zero. However, if the error converges to zero, an impractical input trajectory may be calculated. We use a generalized objective function to independently tune weighting factors of manipulated variable change with respect to both the time index and batch horizons. If the generalized objective function is used, output error converges to non-zero values. We provide convergence analysis for both cases of zero convergence and non-zero convergence.

A novel iterative learning control method and control system design for active magnetic bearing rotor imbalance of primary helium circulator in high-temperature gas-cooled reactor

“…In addition, it can be further seen from Figure 7 that the control error converges to target value at the 9th iteration via ILC-1, while at the 18th iteration via ILC-2. Obviously, the relationship of iterations between ILC-1 and ILC-2 satisfies the theoretical calculation results of equations (29) and (30). Although the former is faster than the latter, the suppression effect of the two methods on repetitive periodic disturbance is consistent in the end.…”

“…From equations (29) and 30, we can get a conclusion that the evaluation of convergence rate based on iterations is consistent with spectral radius, which also fully conforms that the dependence of ILC algorithm on previous system information is gradually decreasing. Of course, this conclusion is also consistent with the ILC algorithm described in equation 7and the convergence conditions described in equation (20).…”

“…Furthermore, this combination can be linear or nonlinear, and it is a typical intelligent control method by learning from the previous system information. In recent years, ILC has been successfully applied in robot control, 25,26 hard disk drive, 27 linear motor, 28 chemical engineering, 29 and so on. Considering the rigid rotor characteristics of the PHC and the requirement of the periodic component extraction in the AMB rotor unbalance control, an open-loop differential ILC-2 with variable forgetting factor and variable learning gain is proposed and expressed as shown in equation (7)…”

Particle-Swarm-Optimization-Based 2D Output Feedback Robust Constraint Model Predictive Control for Batch Processes

“…With the deepening of scientific research, people have found that model predictive control (MPC) is well-applied in industrial processes because of its "receding optimization" ability, especially in processes that cannot be accurately modeled. Based on the advantages of MPC, the combination design of ILC and MPC in a 2D framework was very meaningful for batch processes [17][18][19][20][21][22][23][24][25][26]. In these designs, the design of combining ILC and MPC was included [17][18][19][20][21][22], as well as the design of feedback control combined with ILC and MPC [23][24][25][26][27][28].…”

2D Terminal Constrained Model Predictive Iterative Learning Control of Batch Processes With Time Delay

“…In [44], an iterative learning model predictive control method involving the dynamic parameter R was proposed, which combined model identification with the dynamic parameter R, eliminated the incompatibility between the model and controlled object, and achieved the zero-error tracking. In [45], to inhibit the realtime disturbances, Oh et al combined the iterative learning control and model predictive control in batch processes. Under the framework of two-dimensional systems, a novel predictive iterative learning control strategy was designed in [46].…”