Nonlinear Multiple Model Predictive Control of Solution Polymerization of Methyl Methacrylate

Abbaszadeh, Masoud

doi:10.4236/ica.2011.23027

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…As seen in (15) and (19), + G and + ∆U are independent of N . The only thing determined by N is the dimension of − G , which is omitted now.…”

Section: The Modified Dmcmentioning

confidence: 68%

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Constrained Nonlinear Model Predictive Control of a Polymerization Process via Evolutionary Optimization

Abbaszadeh¹,

Solgi²

2014

JILSA

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In this work, a nonlinear model predictive controller is developed for a batch polymerization process. The physical model of the process is parameterized along a desired trajectory resulting in a trajectory linearized piecewise model (a multiple linear model bank) and the parameters are identified for an experimental polymerization reactor. Then, a multiple model adaptive predictive controller is designed for thermal trajectory tracking of the MMA polymerization. The input control signal to the process is constrained by the maximum thermal power provided by the heaters. The constrained optimization in the model predictive controller is solved via genetic algorithms to minimize a DMC cost function in each sampling interval.

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“…As seen in (15) and (19), + G and + ∆U are independent of N . The only thing determined by N is the dimension of − G , which is omitted now.…”

Section: The Modified Dmcmentioning

confidence: 68%

“…Similarly, termination rate constant, t k , is given by θ and t θ are adjustable parameters related to propagation and termination rate constants, respectively. All other necessary parameters and constants for this model are given in [1], [14] and [15].…”

Section: Mathematical Modeling Of Polymerizationmentioning

confidence: 99%

Constrained Nonlinear Model Predictive Control of a Polymerization Process via Evolutionary Optimization

Abbaszadeh¹,

Solgi²

2014

JILSA

Self Cite

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“…Here the squared error is studied, because the main effects are similar with absolute error. As a recent example [8] uses the same objective function for a MIMO case. Let the cost function be the following:…”

Section: The Objective Function and The Closed-loop Specificationmentioning

confidence: 99%

Similarities of Model Predictive Control and Constrained Direct Inverse

Tóth

Nagy²,

Szeifert³

2012

ICA

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To reach an acceptable controller strategy and tuning it is important to state what is considered “good”. To do so one can set up a closed-loop specification or formulate an optimal control problem. It is an interesting question, if the two can be equivalent or not. In this article two controller strategies, model predictive control (MPC) and constrained direct inversion (CDI) are compared in controlling the model of a pilot-scale water heater. Simulation experiments show that the two methods are similar, if the manipulator movements are not punished much in MPC, and they act practically the same when a filtered reference signal is applied. Even if the same model is used, it is still important to choose tuning parameters appropriately to achieve similar results in both strategies. CDI uses an analytic approach, while MPC uses numeric optimization, thus CDI is more computationally efficient, and can be used either as a standalone controller or to supplement numeric optimization

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“…It belongs to a general class of predictive control. Numerous implementation algorithms have been addressed to this topic which are published in literature (Abbaszadeh, 2011;Rawling, 2000). The control problem involves an objective function subject to constraints which are generally associated to the inputs and outputs of the system (Garcia et al, 1989).…”

Section: Introductionmentioning

confidence: 99%

Neural Networks Predictive Controller Using an Adaptive Control Rate

Mnasser

Bouani

Ksouri

2014

International Journal of System Dynamics Applications

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A model predictive control design for nonlinear systems based on artificial neural networks is discussed. The Feedforward neural networks are used to describe the unknown nonlinear dynamics of the real system. The backpropagation algorithm is used, offline, to train the neural networks model. The optimal control actions are computed by solving a nonconvex optimization problem with the gradient method. In gradient method, the steepest descent is a sensible factor for convergence. Then, an adaptive variable control rate based on Lyapunov function candidate and asymptotic convergence of the predictive controller are proposed. The stability of the closed loop system based on the neural model is proved. In order to demonstrate the robustness of the proposed predictive controller under set-point and load disturbance, a simulation example is considered. A comparison of the control performance achieved with a Levenberg-Marquardt method is also provided to illustrate the effectiveness of the proposed controller.

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Nonlinear Multiple Model Predictive Control of Solution Polymerization of Methyl Methacrylate

Cited by 5 publications

References 12 publications

Constrained Nonlinear Model Predictive Control of a Polymerization Process via Evolutionary Optimization

Constrained Nonlinear Model Predictive Control of a Polymerization Process via Evolutionary Optimization

Similarities of Model Predictive Control and Constrained Direct Inverse

Neural Networks Predictive Controller Using an Adaptive Control Rate

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