Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes

This paper proposes a linear quadratic predictive fault-tolerant control (LQPFTC) scheme for multi-phase batch processes with input time-delay and actuator faults. First of all, according to a given model with input time-delay, a new variable is introduced and the given model is transformed into an extended state-space model without time delay. And then, a new 2D switched system model based on an equivalent 2D-Roesser model is constructed by introducing the state error and the output tracking error to solve the actuator fault and realize the optimal control performance. By adjusting the variable in the function, a quadratic performance function based on the model is designed and a linear predictive fault-tolerant controller by combining with the principle of predictive control is proposed. Then, using the Lyapunov function and the average dwell time method, the sufficient conditions for the robust exponential stability of the system along the time and batch direction, and the minimum running time of each phase are derived. Finally, taking the injection molding process as an example, different fault values are selected for simulation. The results show that the 2D controller designed can realize tracking control with different actuator fault values and even a serious one.INDEX TERMS Model predictive fault-tolerant control, batch process, input time-delay, equivalent 2D-Roesser model, actuator faults.

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“…, k l represents the Lyapunov function of batch k l−1 in the i th phase. From Document [34], we get:…”

Section: Design Of Switching Lawmentioning

confidence: 99%

Linear Quadratic Predictive Fault-Tolerant Control for Multi-Phase Batch Processes

Wang

Luo

2019

IEEE Access

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“…Lee [19] proposed Batch-MPC based on a time-varying MIMO linear model representing the underlying nonlinear batch process, attaining asymptotically perfect tracking despite initialization errors and model errors. Zhou [20] developed an iterative learning model predictive control technique for batch processes, that an iterative learning scheme of batch repetitive disturbances was incorporated into model predictive control scheme.…”

Section: B Iterative Learning Controlmentioning

confidence: 99%

Optimization and Control of Batch Chemical Processes

Hongyan

Decheng

Wang

2011

2011 Fourth International Conference on Intelligent Computation Technology and Automation

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Batch chemical processes have become significant in chemical manufacturing. Recently, economy globalization has resulted in growing worldwide competitions in traditional chemical process industry. In order to increase competition, reduce production costs and meet safety requirements, it is necessary to implement varies optimization methods and advanced control strategies. The goal of this paper is to review the recent developments in this challenging area. Solution methods and classification for dynamic optimization are reviewed, measurement-based optimization methods and two kinds of advanced control strategies are analyzed and summarized. Finally, challenges and development trends in future research are presented.

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“…It is shown that proportional-integral-derivative control may lack adequate robustness for such processes [10,11]. As a result, model predictive control (MPC) has been studied in recent years [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…MPC on industrial coke unit was also witnessed in recent years. For example, intelligent linearisation model based MPC [8], iterative learning MPC [11], adaptive control strategies [34], DMC [35], artificial intelligence online advisor based MPC [36] and state space model based MPC [29, 31, 37]. However, there still remain challenges for the industrial coke processes because of strong subsystem interactions, time delay and coke deposition [38].…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of an improved linear quadratic regulation control for oxygen content in a coke furnace

Zhang

Cao

et al. 2014

IET Control Theory & Applications

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In view of the performance deterioration of conventional model predictive control strategies under model/plant mismatches, this study first proposes an improved linear quadratic regulation control (LQR) strategy and then tests it on the oxygen content in an industrial coke furnace. The control system design consists of two steps. Based on the direct use of the measured input and output variables and the formulation of output error, a state space model is first developed. Then, by incorporating the dynamics of process states into the cost function of the controller design, a new LQR is proposed to obtain an improved performance for closed-loop control systems. Comparisons with conventional state space model predictive control are also given in terms of both regulatory and servo performance. The case study on the oxygen content in the coke furnace shows that the proposed improves control performance under model/plant mismatches.

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Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes

Cited by 13 publications

References 12 publications

Linear Quadratic Predictive Fault-Tolerant Control for Multi-Phase Batch Processes

Linear Quadratic Predictive Fault-Tolerant Control for Multi-Phase Batch Processes

Optimization and Control of Batch Chemical Processes

Design and implementation of an improved linear quadratic regulation control for oxygen content in a coke furnace

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