A nonlinear regression model-based predictive control algorithm

Dubay, Rickey; Abu-Ayyad, Ma’moun; Hernandez, J. M.

doi:10.1016/j.isatra.2008.12.004

Cited by 8 publications

(9 citation statements)

References 16 publications

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“…In order to react to nonlinear characteristics of a plant [6], modifications must be made to the formulation of the canonical DMC. As discussed above, the dynamic matrix A contains the step response of the system at an operating point.…”

Section: Non-linear Regression Dynamic Matrix Controlmentioning

confidence: 99%

“…The formulation of the non-linear regression predictive controller (NRPC) differs from the standard DMC primarily in the construction of the dynamic matrix A [6]. This equation represents a scalar value of the fitted normalized step response at time k∆t.…”

Section: Non-linear Regression Dynamic Matrix Controlmentioning

confidence: 99%

“…The b k,n coefficients are the polynomial coefficients of the step inputs, and the h values represent the exponents which can be real or integer values. The importance of choosing proper h exponential values is not to be understated, as these will determine the accuracy of the curve fit of the responses, directly affecting performance of the controller [6]. This S k (ũ) is for a single step inputũ.…”

Section: Non-linear Regression Dynamic Matrix Controlmentioning

confidence: 99%

“…Using the resulting S k (ũ), a dynamic matrix A can be evaluated at each time step [6] using the inputũ…”

Section: Non-linear Regression Dynamic Matrix Controlmentioning

confidence: 99%

“…Next, [5] presents a multi-model adaptive modification which serves to improve the performance of the algorithm for non-linear processes. Finally, [6] introduces a more rigorous approach to control of non-linear processes using a non-linear regression to re-evaluate the estimated dynamic matrix at each time step based on the current system operating point. Once the theory of each modification has been presented, each method will be tested in simulation against one another, and these results will be presented and discussed.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Survey of Advanced Dynamic Matrix Control Algorithms for Improved Performance

Parrott¹,

Chevarie²,

Ellis³

et al. 2020

Progress in Canadian Mechanical Engineering. Volume 3

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Model Predictive Controllers, specifically Dynamic Matrix Controllers (DMCs), have become widespread in industry. However, they have some drawbacks, particularly when it comes to their tuning and their application to non-linear systems. This paper explores several modifications to the canonical DMC algorithm which aim to improve its performance and applicability in these situations. The formulation of each modification is presented and contrasted to the canonical formulation, and then each is tested against the other in simulation on a variety of linear and non-linear systems.

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Section: Non-linear Regression Dynamic Matrix Controlmentioning

confidence: 99%

Section: Non-linear Regression Dynamic Matrix Controlmentioning

confidence: 99%

Section: Non-linear Regression Dynamic Matrix Controlmentioning

confidence: 99%

“…Using the resulting S k (ũ), a dynamic matrix A can be evaluated at each time step [6] using the inputũ…”

Section: Non-linear Regression Dynamic Matrix Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%