Multiloop IMC-Based PID Controller for CSTR Process
01 Jan 2016-pp 615-625
TL;DR: A multiloop proportional-integral-derivative (PID) controller for a nonlinear plant CSTR system, where CSTR exhibits extremely nonlinear behaviors and habitually have broad operating ranges.
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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01 Sep 2017
TL;DR: A new PID controller design scheme is proposed for non-linear systems where the system model is first generated by using local linear models and a design of internal model controllers (IMC) based on the locallinear models is described.
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.
2 citations
Cites background from "Multiloop IMC-Based PID Controller ..."
...Morari and Zafiriou[4] have proposed the IMC tuning scheme of PID parameters in continuous time version....
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...There are, however, few examples of application to non-linear systems, while there are many examples [3] [4] of the application of the internal model control(IMC)[5] to linear systems....
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TL;DR: In this paper , a metaheuristic optimization technique-based PID, fractional-order PID controller (FOPID) control evolved using the behaviour of a swarm of birds named coot bird optimization algorithm (CBOA) has been highlighted in this paper.
Abstract: The demand for novel and innovative control techniques has increased due to complexity in the process control industries which expect better control with enhanced performance. The optimization of proportional–integral–derivative (PID) controller parameters plays a significant role in the petrochemical and biochemical industries. In all these industries, controlling of continuous stirred tank reactors (CSTRs) is highly deployed whose dynamic characteristics are non-linear and difficult to control as there is a risk of its temperature deviating from the set point. To have an efficient control, a metaheuristic optimization technique-based PID, fractional-order PID controller (FOPID) control evolved using the behaviour of a swarm of birds named coot bird optimization algorithm (CBOA) has been highlighted in this paper. The tuning has been done using the weighted combination of fitness functions, specifically the overshoot (Os) and the settling time ( T s ) $$ {T}_s\Big) $$ . The simulated results demonstrate the improvement of the time domain response parameters of CBOA tuned PID, FOPID controllers providing an optimum performance when compared to other optimization algorithms and a conventional controller.
1 citations
References
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TL;DR: In this paper, a multiple model adaptive control strategy for multivariable dynamic matrix control (DMC) is proposed, which combines the output of multiple linear DMC controllers, each with their own step response model describing process dynamics at a specific level of operation.
141 citations
TL;DR: In this article, a stepwise regression algorithm based on orthogonalization and a series of statistical tests is employed for designing and training of the RBF networks, which yields non-linear models, which are stable and linear in the model parameters.
94 citations
TL;DR: In this article, a 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.
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.
41 citations
Journal Article•
25 Aug 2010-World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering
TL;DR: 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 for the control of multivariable system.
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.
11 citations
Proceedings Article•
01 Jan 2003
TL;DR: 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.
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.
9 citations