Zhixing Cao

State-Space Predictive-P Control for Liquid Level in an Industrial Coke Fractionation Tower

In this study, a predictive-p control system is developed for the level process in an industrial coke fractionation tower. Such processes typically have integrating and nonlinear dynamics causing the performance of conventional control designs and tuning to be poor or to require significant effort in practice. The process model is derived using data of step-response test and control implementation is designed through a new state-space structure. The closed-loop control system contains both the improved predictive control and P control. The performance of the proposed control for regulatory/servo, disturbance rejection, and measurement noise problems are studied and the obtained results show that the control system is of simple implementation with more robustness and provides better responses than conventional predictive control.

Discrete-Time Robust Iterative Learning Kalman Filtering for Repetitive Processes

A discrete-time, robust, iterative learning Kalman filter is proposed for state estimation on repetitive process systems with norm-bounded uncertainties in both the state and output matrices. The filter design combines iterative learning control and robust Kalman filtering by exploiting process repetitiveness.

Rejection of Periodic Disturbances Based on Adaptive Repetitive Model Predictive Control

Abstract The paper presents an adaptive strategy to reject periodic disturbances with unknown period based on a combination of model predictive control and repetitive control. A novel period estimator is presented. For the integer period case, the estimator is designed based on integer programming. For the non-integer period case, it is designed based on a two-step optimization, namely integer programming followed by a constrained least square method. With the estimated period, feedforward compensation is made to improve the tracking performance asymptotically. Simulation results are given to show the effectiveness of the algorithm.

A Repetitiveness Index-Based Adaptive Two Dimensional Iterative Learning Model Predictive Control

Abstract In this paper, we consider about the control strategy design for batch processes with sever non-repetitive disturbances. An index is proposed to measure the repetitive extent of batch processes. An adaptive two dimensional iterative learning model predictive control (ILMPC) method is designed based on this index. The control algorithm is switched between an one-dimensional Model Predictive Control (MPC) and a two time dimensional ILMPC according to this index. Simulation shows the superior effects of the proposed algorithm in handling abrupt changes of plant dynamics.

Two Dimensional Recursive Least Squares for Batch Processes System Identification

Abstract Recursive system identification is an important problem in many advanced control techniques, such as adaptive control. This paper presents a new approach of two dimensional recursive least squares identification method suitable for batch processes. In this way, system identification is carried out not only using the information from time direction within the batch but also from batch to batch direction. A constraint term is incorporated in the cost function to reduce parameters varying. A guideline for selecting weight matrix in application is also provided. Furthermore, simulation results based on the data obtained from a model of injection moulding, a typical batch process, are illustrated to testify the superiority of the proposed method over the conventional recursive leasts squares.