Jingyi Lu

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 multi-objective model predictive control for temperature control in extrusion processes

In this paper, we consider the temperature control problem of an extrusion process with both heaters and coolers. For the purpose of energy saving and avoiding frequent switch between the heaters and coolers, the coolers are only used to guarantee the barrel temperature below a given safety bound. When this safety constraint is satisfied, the heaters take actions for reference tracking. This scheme is formulated as a multi-objective optimization problem in the framework of model predictive control. Different objectives have different priority. The safety constraint is of the highest priority, and formulated as a constraint in the optimization. Minimization of the inputs corresponding to the cooler is of the second priority, and incorporated into the objective function with heavy penalty weight. Minimization of the tracking error is of the lowest priority. Thus, this term is also incorporated into the objective function, but with light penalty weight. In this way, energy consumption can be reduced and frequent switch can be avoided. Moreover, a polytopic invariant set is developed to guarantee recursive feasibility of the proposed MPC. Simulations are also conducted to show the effectiveness of the proposed method.

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.