Model predictive control of a CSTR: A comparative study among linear and nonlinear model approaches (original) (raw)
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Temperature Control of Continuous Stirred Tank Reactor Using Model Predictive Controller Anurag
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The objective of this paper is to develop a Model Predictive Control (MPC) to control the temperature in Continuous Stirred Tank reactor (CSTR), which exhibits highly nonlinear dynamics. PID controllers are widely used in industry, however the control of nonlinear system using PID control scheme doesn’t give satisfactory performance at all operating points, the reason behind is that the parameters of these nonlinear process varies with the operating conditions [1]. Here, a model based predictive controller is designed and implemented for the temperature control of the process reactor of Continuous Stirred Tank Reactor (CSTR). PID controller is also designed. The PID Controller and MPC controller both are tuned and simulated using MATLAB. The transients results are compared in both PID and MPC controller and analysis is conducted.
A comparison of nonlinear control techniques for continuous stirred tank reactors
1992
Globally linearixi ng control, a differential geometry-based technique (continuous), and nonlinear predictive conuol, an optimization-based approach (discrete), are compared for temperature control of a classical exothermic CSTR The two strategies can be tuned to have identical performance for setpoint changes or measured disturbances when there are no bounds on the manipulated variable. As the sample time is deca the two performance for unmeasured disturbances or uncertain models. approaches also yield identical However, NLFC pc4fom18 beater in the presence of constraints on the manipulated variable. An open-loop observer for the unmcasurcd state variable (composition) has been used. The system studied is minimum-phase, allowing a filtered deadbeat control law for the nonlinear predictive control strategy. MOTIVATION Chemical reactors create some of the most challenging feedback control problems faced by chemical process control engineers. Complex static and dynamic behavior, such as input or output multiplicities, ignition-extinction behavior and parametric sensitivity create challenges that are tough for traditional linear controllers to handle. An excellent review of multiplicities and instabilities in chemical reacting systems is provided by Raxon and Schmitx (1987). During the past five years there have been a number of control strategies developed that are based explicitly on a nonlinear process model. These nonlinear control strategies can be conveniently lumped into two categories: (i) differential geometry-based control and (ii) optimization-based control. A tutorial review of differential geometry-based control techniques has been provided by Kravaris and Kantor (1990); a comprehensive review of nonlinear control is presentedby Bequette (1991).
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Over the past three decades, Model Predictive Control (MPC) has emerged as one of the most powerful and widely used control algorithm. Model predictive control uses the explicit process model to predict the future behaviour of a plant. This algorithm also takes into consideration the various constraints in input and output while designing the controller. This paper explores the capability of model predictive control algorithm in controlling the temperature parameter of a non linear cascaded Continuous Stirred Tank Reactor (CSTR) process model. The model predictive control algorithm is implemented in LabVIEW using the control and simulation toolkit and the reference tracking capability of the system is verified. The simulated performance of the system widens the option of using LabVIEW platform in designing MPC for a non-linear multi inputmulti output (MIMO) process.
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this paper presents a predictive control strategy based on neural network 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., neural network predictive control, can be a better match to govern the system dynamics. In the paper, the NN 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. Predictive control algorithm is applied to control the concentration in a continuous stirred tank reactor (CSTR), whose parameters are optimally determined by solving quadratic performance index using the optimization algorithm. An efficient control of the product concentration in cstr can be achieved only through accurate model. Here an attempt is made to alleviate the modeling difficulties using Artificial Intelligent technique such as Neural Network. Simulation results demonstrate the feasibility and effectiveness of the NNMPC technique.
A neural network model based predictive control approach: application to a semi-batch reactor
Neural networks can be considered to be new modelling tools in process control and especially in non-linear dynamical systems cases. Their ability to approximate non-linear functions has been very often demonstrated and tested by simulation and experimental studies. In this paper, a predictive control strategy of a semi-batch reactor based on neural network models is proposed. Results of a non-linear control of the reactant temperature of a semi-batch reactor are presented. The process identification is composed of an off-line phase that consists in training the network, and of an on-line phase that corresponds to the neural model adaptation so that it fits any modification of the process dynamics. Experimental results when using this method to control a semi-batch reactor are reported and show the great potential of this strategy in controlling non-linear processes.
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In this work we study the use of nonlinear model predictive control for the control of fed-batch processes. The main idea is to use composite nonlinear models consisting of multiple linear models that are identi ed and interpolated. The approach is illustrated by a simulation study of a fed-batch process for the synthesis of hexyl monoester maleic acid.
Model predictive control of CSTR based on local model networks
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A non-linear predictive controller is presented. It judiciously combines predictive controllers with a local model network utilizing a neural-network-like gating system. It avoids the time consuming quadratic optimization calculation, which is normally necessary in non-linear predictive control. A controller simulation on a Continuous Stirred Tank Reactor (CSTR) case study was shown to be satisfactory both in terms of set point tracking and regulation performance over the entire operating range. Moreover, the inherent integration action in the local predictive controller provides zero static offsets.
IEEE Conference on Decision and Control and European Control Conference, 2011
Almost all industrial processes exhibit nonlinear dynamics, however most model predictive control (MPC) applications are based on linear models. Linear models do not always give a sufficiently adequate representation of the system and therefore Nonlinear Model Predictive Control (NMPC) techniques have to be used. In this article, two techniques of NMPC, namely successive linearization nonlinear model predictive control (SLNMPC) and wiener nonlinear model predictive control (WNMPC) are applied to nonlinear process systems. The major advantage of the two methods being that the NMPC problem is reduced to a linear model predictive control (LMPC) problem at each time step which thereafter allows the optimization problem to be solved using quadratic programming (QP) techniques. Another advantage of these methods is the reduced computational time in calculating the control effort which makes them suitable for online implementation. Both simulation and experimental results show the superior...
Nonlinear Multiple Model Predictive Control in a Fed-Batch Reactor
2000
In this work we study the use of nonlinear model predictive control for the control of fed-batch processes. The main idea is to use composite nonlinear models consisting of multiple linear models that are identi ed and interpolated. The approach is illustrated by a simulation study of a fed-batch process for the synthesis of hexyl monoester maleic acid.