A practical multiple model adaptive strategy for multivariable model predictive control

Dougherty, Danielle; Cooper, Douglas J.

doi:10.1016/s0967-0661(02)00170-3

Cited by 120 publications

(51 citation statements)

References 55 publications

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“…Ogunnaike et al (1983) have implemented PI controller with delay compensator to improve the control performance of PI controller without delay. Luyben, et al (1988) Although BLT was better than DMC (Luyben, et al, 1988), Dougherty and Cooper (2003) have proved that the applications of the non-adaptive DMC tuning strategy in the three 2x2 processes (general transfer function, multi-tank, and distillation column) have a satisfactory performance. So, we will implement this strategy to improve the control performance of OLMR distillation column that has 3x3 matrixes of input and output variables.…”

Section: Olmr Distillation Columnmentioning

confidence: 97%

“…(Marlin, 2000) are some examples of single-loop tuning in P, PI, and PID controllers. Huang, et al (2003) has proposed a direct method for multi-loop (multivariable) PI/PID controller design based on FOPDT/SOPDT model of each loop.…”

Section: Introductionmentioning

confidence: 99%

“…Those parameters are sampling time (T), prediction horizon (P), model horizon (N), control horizon (M), controlled variable weights ( ), and move suppression coefficients ( ). During the time, trialand-error efforts have been done to find out this goal until Shridhar and Cooper (Dougherty and Cooper, 2003) proposed a tuning strategy for unconstraint SISO and multivariable MPC. Dougherty and Cooper (2003) proposed a non-adaptive DMC tuning strategy (see Table 1) based on all of FOPDT models in systems.…”

Section: Introductionmentioning

confidence: 99%

“…(Marlin, 2000) are some examples of single-loop tuning in P, PI, and PID controllers. Huang, et al (2003) has proposed a …”

mentioning

confidence: 99%

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Application of Model Predictive Control (Mpc) Tuning Strategy in Multivariable Control of Distillation Column

Ahmad

Wahid

2007

Reaktor

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IntroductionPredictive control is now one of the most widely used advanced control methods in industry, especially in the control of processes that are constrained, multivariable and uncertain. A large number of implementation algorithms, including industrial predictive control applications (Qin and Badgwell, 2003) have appeared in the literature.The cornerstone of MPC is the model (Clarke, 1996). It cause MPC is called MBPC (model-based predictive control). MPC uses models in 2 ways: using a reliable model to predict effect of past control moves on P (prediction horizon) future outputs, assuming no future moves, and using the same model to compute the optimal M (control) horizon moves.Dynamic matrix control (DMC) (Cutler and Ramaker, 1980) is the most popular MPC algorithm used in chemical process industry today. Over the past decade, DMC has been implemented on a wide range of process applications. A major part of DMC's appeal in industry stems from the use of a linear finite step response model of the process and a simple quadratic performance objective function. The objective function is minimized over a prediction horizon to compute the optimal controller output moves as a least-squares problem.Tuning a controller is a direct way to reach its optimum performance. Tuning conventional controllers (P, PI, and PID) is related to obtain an optimum setting of controller parameters (controller gain K c , integral time T i , and derivative time T d ). Ziegler-Nichols, Lopez, Ciancone, etc. (Marlin, 2000) are some examples of single-loop tuning in P, PI, and PID controllers. Huang, et al. (2003) has proposed a

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Section: Olmr Distillation Columnmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…(Marlin, 2000) are some examples of single-loop tuning in P, PI, and PID controllers. Huang, et al (2003) has proposed a …”

mentioning

confidence: 99%

See 2 more Smart Citations

Application of Model Predictive Control (Mpc) Tuning Strategy in Multivariable Control of Distillation Column

Ahmad

Wahid

2007

Reaktor

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“…This plant is known to be highly nonlinear, which makes it suitable for comparison. In [1], [2] and [4] different solutions to this problem are proposed. Here, however we consider the design of a standard PID controller.…”

Section: Introductionmentioning

confidence: 99%

Tuning of a PID controller Using a Multi-objective Optimization Technique Applied to A Neutralization Plant

Popov

Farag

Werner

Proceedings of the 44th IEEE Conference on Decision and Control

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Abstract-Most control engineering problems are characterized by several, often contradicting, objectives, which have to be satisfied simultaneously. Two widely used methods for finding the optimal solution to such problems are aggregating to a single criterion, and using Pareto-optimal solutions. Here we propose a Genetic Algorithm (GA) approach using a combination of both methods to find a fixed-gain, discrete-time PID controller for a chemical neutralization plant. Known to be highly non-linear and with varying time delay, this plant provides a challenging testbed for nonlinear control strategies. Experimental results confirm that a multi-objective, Pareto-based GA search gives a better performance than a single objective GA. The former method was also used to design a gain-scheduled PID controller, for which also experimental results are shown.

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A Perspective on the Implementation of QbD on Manufacturing through Control System

Quiñones

Velázquez

2017

Comprehensive Quality by Design for Pharmaceutical Product Development and Manufacture

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A practical multiple model adaptive strategy for multivariable model predictive control

Cited by 120 publications

References 55 publications

Application of Model Predictive Control (Mpc) Tuning Strategy in Multivariable Control of Distillation Column

Application of Model Predictive Control (Mpc) Tuning Strategy in Multivariable Control of Distillation Column

Tuning of a PID controller Using a Multi-objective Optimization Technique Applied to A Neutralization Plant

A Perspective on the Implementation of QbD on Manufacturing through Control System

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