Model predictive control technique combined with iterative learning for batch processes

Lee, Kwang-Sik; Chin, In-Shik; Lee, Hyuk J.; Lee, Jay H.

doi:10.1002/aic.690451016

Cited by 224 publications

(186 citation statements)

References 21 publications

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“…bmpc is an iterative-model based predictive controller. Originally, it has been devised for dealing with linear batch processes, specially chemical processes (Lee et al, 1999), and it belongs to the class of model-based iterative controllers. Other example of model-based iterative controller is the qilc, presented by Amann et al (1996).…”

Section: Resultsmentioning

confidence: 99%

“…Because feedback control can respond to disturbances immediately and batch-to-batch control can correct any bias left uncorrected by the feedback controller, which may be due to unmodelled disturbances, parameter errors, and dynamics, the combined scheme can potentially complement each other to render the benefits of both. The idea of combining batch-to-batch control with feedback control has appeared in Lee et al (1999).…”

Section: Introductionmentioning

confidence: 99%

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Iterative nonlinear model predictive control. Stability, robustness and applications

Cueli

Bordons

2008

Control Engineering Practice

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This paper describes the control of a batch pH reactor by a nonlinear predictive controller that improves performance by using data of past batches. The control strategy combines the feedback features of a nonlinear predictive controller with the learning capabilities of run-to-run control. The inclusion of real-time data collected during the on-going batch run in addition to those from the past runs make the control strategy capable not only of eliminating repeated errors but also of responding to new disturbances that occur during the run. The paper uses these ideas to devise an integrated controller that increases the capabilities of Nonlinear Model Predictive Control (nmpc) with batch-wise learning. This controller tries to improve existing strategies by the use of a nonlinear controller devised along the last-run trajectory as well as by the inclusion of filters. A comparison with a similar controller based upon a linear model is performed. Simulation results are presented in order to illustrate performance improvements that can be achieved by the new method over the conventional iterative controllers. Although the controller is designed for discrete-time systems, it can be applied to stable continuous plants after discretization.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iterative nonlinear model predictive control. Stability, robustness and applications

Cueli

Bordons

2008

Control Engineering Practice

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“…The following assumptions are imposed on plant (1 [ 41] It is necessary for any algorithm to guarantee convergence in the presence of constraints. It is similar to the reachability condition of a control system.…”

Section: Problem Formulationmentioning

confidence: 99%

“…[35][36] However, the input saturation makes the control systems highly nonlinear and brings serious influence on ILC systems performance because ILC by its nature is an integral control along the iteration direction. [37] In order to integrate the system constraints into the control law, several papers [38][39][40][41][42] have explored optimization-based constraint ILC, where a quadratic programming problem (QP) is constructed to minimize the designed objective function subject to system constraints. However, the optimization-based methods are typically model-based and rely on an exact linear model.…”

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confidence: 99%

Constraint data‐driven optimal terminal ILC of end product quality for a class of I/O constrained batch processes

et al. 2017

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In order to deal with the I/O constraints in a practical plant, a soft limiter is often added into the control design procedure directly; however, the performance of the soft limiter based control approach will be degraded greatly due to the use of the soft constraints. This paper proposes a datadriven optimal terminal iterative learning control (constraint-DDOTILC) approach for the end product quality control of batch processes with I/O hard constraints. To deal with nonlinearities, a novel iterative dynamic linearization method without omitting any information of the original plant is introduced such that the derived linearized data-driven model is completely equivalent to the original nonlinear system. By transferring all the constraints on the system output, control input, and the change rate of input signals into a linear inequality, a novel constraint-DDOTILC is developed by minimizing an objective function under the derived linear matrix inequality constraint. The optimal learning gain of the constraint-DDOTILC can be updated iteratively according to the input and output measurements to enhance the flexibility for modifications and expansions of the controlled plant. Both theoretical analysis and simulation results confirm the effectiveness of the proposed constraint-DDOTILC.

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“…This kind of systems can be found in several industrial fields such as robot manipulation (Tan, Huang, Lee & Tay, 2003), injection molding (Yao, Gao & Allgöwer, 2008), batch processes (Bonvin et al, 2006;Lee & Lee, 1999; and semiconductor processes (Moyne, Castillo, & Hurwitz, 2003). Because of the repetitive characteristic, these systems have two count indexes or time scales: one for the time running within the interval each operation lasts, and the other for the number of operations or repetitions in the continuous sequence.…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning - MPC: an Alternative Strategy

Adam¹,

González²

2012

Frontiers in Advanced Control Systems

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Model predictive control technique combined with iterative learning for batch processes

Cited by 224 publications

References 21 publications

Iterative nonlinear model predictive control. Stability, robustness and applications

Iterative nonlinear model predictive control. Stability, robustness and applications

Constraint data‐driven optimal terminal ILC of end product quality for a class of I/O constrained batch processes

Iterative Learning - MPC: an Alternative Strategy

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