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Book ChapterDOI: 10.1007/978-81-322-2671-0_59

Multiloop IMC-Based PID Controller for CSTR Process

01 Jan 2016-pp 615-625
Abstract: In this paper, we have designed a multiloop proportional-integral-derivative (PID) controller for a nonlinear plant CSTR system. CSTR exhibits extremely nonlinear behaviors and habitually have broad operating ranges. The tuning of controller for each operating points of CSTR (continuous stirred tank reactor) is based on internal model control (IMC) tuning method. The main objective of this paper is to design a multiloop PID controller for the control of variable specifically concentration and temperature of multivariable nonlinear system CSTR. A multiple input multiple output (MIMO) process that merges the output of several linear PID controllers, each describing process dynamics at a precise level of operation. The global output is an interruption of the individual multiloop PID controller outputs weighted based on the current value of the deliberated process variable. A common approach to crack the nonlinear control problem such as CSTR is using gain scheduling with linear multiple PID controllers.

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Topics: PID controller (62%), Control theory (56%), Nonlinear control (56%) ...read more
Citations
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Proceedings ArticleDOI: 10.1109/ETFA.2017.8247753
Shinichi Imai1, Toru Yamamoto2Institutions (2)
01 Sep 2017-
Abstract: In this paper, a new PID controller design scheme is proposed for non-linear systems. According to this scheme, the system model is first generated by using local linear models. Then, it is described a design of internal model controllers(IMC) based on the local linear models. The internal model control has a simple structure and has a high robustness for system uncertainties. However, there are few studies of IMC schemes for non-linear systems. On the other hand, lots of controlled systems have non-linearities. Finally the effectiveness of the newly proposed control scheme is experiment examples in comparison with the conventional control methods for non-linear systems.

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Topics: PID controller (59%), Internal model (51%), Process control (51%) ...read more

2 Citations

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Journal ArticleDOI: 10.1016/S0967-0661(02)00170-3
Danielle Dougherty1, Doug Cooper1Institutions (1)
Abstract: Model predictive control (MPC) has become the leading form of advanced multivariable control in the chemical process industry. The objective of this work is to introduce a multiple model adaptive control strategy for multivariable dynamic matrix control (DMC). The novelty of the strategy lies in several subtle but significant details. One contribution is that the method combines the output of multiple linear DMC controllers, each with their own step response model describing process dynamics at a specific level of operation. The final output forwarded to the controller is an interpolation of the individual controller outputs weighted based on the current value of the measured process variable. Another contribution is that the approach does not introduce additional computational complexity, but rather, relies on traditional DMC design methods. This makes it readily available to the industrial practitioner.

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Topics: Model predictive control (62%), Adaptive control (58%), Control theory (56%) ...read more

140 Citations


Journal ArticleDOI: 10.1016/0959-1524(92)80008-L
Martin Pottman1, Dale E. Seborg1Institutions (1)
Abstract: In this paper radial basis function (RBF) networks are used to model general non-linear discrete-time systems. In particular, reciprocal multiquadric functions are used as activation functions for the RBF networks. A stepwise regression algorithm based on orthogonalization and a series of statistical tests is employed for designing and training of the network. The identification method yields non-linear models, which are stable and linear in the model parameters. The advantages of the proposed method compared to other radial basis function methods and backpropagation neural networks are described. Finally, the effectiveness of the identification method is demonstrated by the identification of two non-linear chemical processes, a simulated continuous stirred tank reactor and an experimental pH neutralization process.

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Topics: Radial basis function network (62%), Activation function (57%), Backpropagation (53%) ...read more

88 Citations


Journal ArticleDOI: 10.1021/IE0601753
R. Senthil1, K. Janarthanan1, J. Prakash1Institutions (1)
Abstract: In this paper, the authors have presented an approach for designing a nonlinear observer to estimate the states of a noisy dynamic system. The nonlinear observer design procedure involves representation of the nonlinear system as a family of local linear state space models; the state estimator for each linear local state space model uses standard Kalman filter theory and then a global state estimator is developed that combines the local state estimators. The effectiveness of the proposed fuzzy Kalman filter (nonlinear observer) has been demonstrated on a continuously stirred tank reactor (CSTR) process. The performances of the fuzzy Kalman filter (FKF) and the extended Kalman filter (EKF) have been compared in the presence of initial model/plant mismatch and input and output disturbances. Simulation studies also include an estimation of reactor concentration (inferential measurement), based only on the measured variable temperature of the reactor.

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Topics: Extended Kalman filter (73%), Alpha beta filter (71%), Invariant extended Kalman filter (70%) ...read more

38 Citations


Open accessJournal Article
Abstract: Multi-loop (De-centralized) Proportional-IntegralDerivative (PID) controllers have been used extensively in process industries due to their simple structure for control of multivariable processes. The objective of this work is to design multiple-model adaptive multi-loop PID strategy (Multiple Model Adaptive-PID) and neural network based multi-loop PID strategy (Neural Net Adaptive-PID) for the control of multivariable system. The first method combines the output of multiple linear PID controllers, each describing process dynamics at a specific level of operation. The global output is an interpolation of the individual multi-loop PID controller outputs weighted based on the current value of the measured process variable. In the second method, neural network is used to calculate the PID controller parameters based on the scheduling variable that corresponds to major shift in the process dynamics. The proposed control schemes are simple in structure with less computational complexity. The effectiveness of the proposed control schemes have been demonstrated on the CSTR process, which exhibits dynamic non-linearity. Keywords—Multiple-model Adaptive PID controller, Multivariable process, CSTR process.

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11 Citations


Open accessProceedings Article
M. Jalili-Kharaajoo1Institutions (1)
01 Jan 2003-
Abstract: In this paper, a predictive control strategy based on neuro-fuzzy (NF) model of the plant is applied to continuous stirred tank reactor (CSTR). This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neuro-fuzzy predictive control, can be a better match to govern the system dynamics. In the article, the neuro-fuzzy model and the way in which it can be used to predict the behavior of the CSTR process over a certain prediction horizon are described, and some comments about the optimization procedure are made. An optimizer algorithm based on evolutionary programming technique (EP) uses the identifier-predicted outputs and determines input sequence in a time window. The present optimized input is applied to the plant, and the prediction time window shifts for another phase of plant output and input estimation. Afterwards, the control aims, the steps in the design of the control system, and some simulation results are discussed. Using the proposed neuro-fuzzy predictive controller, the performance of PH tracking problem in a CSTR process is investigated. Obtained results demonstrate the effectiveness and superiority of the proposed approach.

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Topics: Model predictive control (60%), Process control (56%), Continuous stirred-tank reactor (55%) ...read more

9 Citations