Kwang S. Lee

A two-stage iterative learning control technique combined with real-time feedback for independent disturbance rejection

A novel control framework for batch and repetitive processes is proposed. The currently practiced methods to combine real-time feedback control (RFC) with iterative learning control (ILC) share a problem that RFC causes ILC to digress from its convergence track along the run index when there occur real-time disturbances. The proposed framework provides a pertinent means to incorporate RFC into ILC so that the performance of ILC is virtually separated from the effects of real-time disturbances. As a prototypical algorithm, a two-stage algorithm has been devised by modifying and combining the existing QILC and BMPC techniques.

A control-relevant model reduction technique for nonlinear systems

Abstract A novel control-relevant model reduction technique for nonlinear systems is proposed utilizing the idea of the balanced truncation. Unlike the widely-accepted Karhunen — Loeve method where the state basis for the reduced system is found from the state snapshots, the proposed technique takes into account the input, state, and output information together and provides a near-balanced reduced-order model that approximates the system map instead of the state snapshots. Performance of the technique is demonstrated for a linear system and a non-adiabatic fixed-bed reactor model.

Integrated quality and tracking control of a batch PMMA reactor using a QBMPC technique

Abstract QBMPC is a recently developed technique for combined quality and tracking control of batch processes where prediction-based real-time control and batch-wise integral control are conducted simultaneously. The most important advantage of QBMPC is that both quality and tracking variables converge to the optima dictated by the objective function despite model uncertainty and run-wise repeating disturbances. In this paper, with the purpose to enhance the quality control aspect, the QBMPC algorithm has been newly formulated based on a time-varying state space model. The new algorithm has been applied to a batch reactor model for methylmethacrylate (MMA) polymerization where the weight-average molecular weight and polydispersity index of the end product, and monomer conversion are considered important quality variables to control. Key steps for the controller design are described and the resulting control performance is demonstrated.

An on-line batch span minimization and quality control strategy for batch and semi-batch processes

Abstract A novel strategy is proposed to minimize the operation time of batch and semi-batch processes. The proposed on-line strategy is based on linear regression models and employs a cascade control structure in which the primary controller calculates an optimal operation profile for the secondary controller to follow. A special feature of the proposed on-line strategy is that it conducts run-wise information feedback and achieves the attainable minimum operation time as the batch run is repeated despite model uncertainty. The performance of the proposed strategy is illustrated through simulation studies involving an exothermic batch reactor and a semi-batch reactor producing 2-acetoacetyl pyrrole.

Optimal iterative learning control of wafer temperature uniformity in rapid thermal processing

Abstract An optimal iterative learning control (ILC) technique based on a quadratic optimal criterion has been implemented and evaluated in an experimental rapid thermal processing (RTP) system fabricating 8-inch silicon wafers. The control technique is based on a time-varying linear state space model which approximates a nonlinear system along a reference trajectory. This ILC control technique is capable of making improvements in the control performance from one run to next and eventually converges to a minimum achievable tracking error despite model error. Through a series of experiments with wafers on which thermocouples are glued, it was observed that the wafer temperatures are steered to the reference trajectory reducing the differences overcoming various disturbances.

A TWO-STAGE ALGORITHM FOR COMBINED ITERATIVE LEARNING CONTROL WITH REAL-TIME FEEDBACK; A STATE SPACE FORMULATION

Abstract A new combined iterative learning control (ILC) and real-time feedback control (RFC) algorithm has been proposed on the basis of the state space formulation and the two-stage implementation. The proposed method assumes Gaussian disturbances and deals with the batch-wise recurrent and nonrecurrent disturbances by independent LQG formulation for ILC and RFC, respectively. In this way, the problem with the existing combined ILC-RFC methods that the nonrecurrent real-time disturbance causees ILC to digress from its convergence track along the run index could be overcome. The performance of the proposed technique has been demonstrated using numerical simulation.

A generic framework for integrated quality and profile control for industrial batch processes

Abstract Importance of batch processes has grown more in recent years as the increasing economic competition has pushed the manufacturing industries to pursue small quantity production of diverse high value-added products. Accordingly, system engineering study on advanced control and dynamic optimization of batch operation has drawn much attention recently. The purpose of this paper is to introduce recent advances in batch process control discuss possible extensions focusing on the contributions by the authors. For this, the so-called run-to-run approaches, that may play a breakthrough in batch operation problems and also the authors work have been based on, are briefly reviewed first from control and optimization points of view. Then a novel MPC(Model Predictive Control)-like run-to-run batch control framework, called QBMPC, that can simultaneously perform prediction-based end-product quality and profile control is proposed aiming at a generic industrial batch control technique. Some possible extensions and modifications are discussed subsequently. Performance of QBMPC is demonstrated with a semi-batch reactor model.

An On-line Batch Span Minimization and Quality Control Strategy for Batch and Semi-Batch Processes

Abstract A novel strategy is proposed to minimize the operation time of batch and semi-batch processes. The proposed on-line strategy is based on linear regression models and employs a cascade control structure in which the primary controller calculates an optimal operation profile for the secondary controller to follow. A special feature of the proposed on-line strategy is that it conducts run-wise information feedback and achieves the attainable minimum operation time as the batch run is repeated despite model uncertainty. The performance of the proposed strategy is illustrated through simulation studies involving an exothermic batch reactor and a semi-batch reactor producing 2-acetoacetyl pyrrole.

METASTABLE LIMIT DYNAMICS AND OPTIMAL COOLING CURVE OF BATCH SEEDED CRYSTALLIZATION

Abstract For seeded cooling crystallization, itn is required that the solution is maintained at a supersaturation state within the metastable limit for suppression of homogeneous nucleation. However, the metastable limit is not fixed but time-varying depending upon the solution history. In this study, dynamics of the metastable limit has been exploited. Using the experimental results obtained under various cooling rates, the metastable limit was expressed to have a nonlinear first-order dynamics to a change in the cooling rate. By incorporating this model, an optimal cooling curve was determined for (NH 4 ) 2 SO 4 crystallization from the (NH 4 ) 2 SO 4 - NH 4 NO 3 - H 2 O ternary system and experimental verification has been performed.

Semi-empirical model-based MIMO iterative learning control for an RTP system

Abstract Study on control system design for a rapid thermal processing (RTP) equipment has been conducted with a purpose to obtain maximum temperature uniformity across the wafer surface while precisely tracking a given reference trajectory. The study covers from model development, optimum multivariable iterative learning control (ILC), optimum sensor location, to reduced-order controller design, but the main emphasis was placed on a new ILC technique based on a semi-empirical dynamic radiation model named as T4-model. All the proposed techniques have been evaluated in an RTP equipment for 8-inch wafers.