On-line tuning strategy for model predictive controllers

Al-Ghazzawi, Ashraf; Esrafili, Ali; Nouh, Adnan; Zafiriou, Evanghelos

doi:10.1016/s0959-1524(00)00033-0

Cited by 98 publications

(71 citation statements)

References 16 publications

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“…Ghazzawi, A et al (2010) introduced an online tuning strategy that will re-tune the controller in real time, based on the predicted closed loop controlled variable response. Acceptable dynamic limits are set on setpoint changes and disturbance rejection.…”

Section: Recent Developments In Dmc Tuningmentioning

confidence: 99%

Using MV overshoot as a tuning metric in choosing DMC move suppression values

Gous¹,

Vaal²

2012

ISA Transactions

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Using MV overshoot as a tuning metric in choosing DMC move suppression values. AbstractTraditionally the tuning of dynamic matrix control (DMC) type multivariable controllers is done by trial and error. The APC engineer chooses arbitrary starting values and tests the performance on a simulated controller. The engineer then either increases the values to suppress movement more, or decreases them to have the manipulated variables move faster. When the controller performs acceptably in simulation, then the tuning is improved during the commissioning of the controller on the plant. This is a time consuming and unscientific exercise and therefore often does not get the required attention. This leads to unacceptable controller behaviour during commissioning and sub-optimal control once commissioning is completed. This paper presents a new method to obtain move suppression factors for DMC type multivariable controllers by using a Nelder Mead search algorithm to find move suppressions that will provide acceptable control behaviour. Acceptable behaviour is described by characterising the dynamic move plan calculated by the controller for each of the manipulated variables.

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Section: Recent Developments In Dmc Tuningmentioning

confidence: 99%

Using MV overshoot as a tuning metric in choosing DMC move suppression values

Gous¹,

Vaal²

2012

ISA Transactions

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“…To achieve that goal, a methodology for adjusting the predefined performance specification will be used. The methodologies for triggering the algorithm and adjusting the performance specification online are presented in detail in (Al-Ghazzawi et al, 2001). …”

Section: Activation Of the Online Adaptationmentioning

confidence: 99%

“…For this purpose, Al-Ghazzawi et al (2001) proposed an online adaptive strategy for constrained MPC. The algorithm utilizes the gradients of the closed-loop response with respect to the parameters to automatically determine online the new values of the parameters that drive the predicted output inside predefined time-domain constraints.…”

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confidence: 99%

On‐line Tuning of Model Predictive Controllers Using Fuzzy Logic

Esrafili¹,

Al-Ghazzawi²

2003

Can J Chem Eng

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This paper addresses the problem of tuning model predictive controllers for good performance. An automatic online tuning strategy is developed to adjust the prediction horizon, P, the diagonal elements of the input weight matrix, Λ, and the diagonal elements of the output weight matrix, Γ. The control horizon is left constant because its relative value with respect to P is more important. The tuning algorithm is based on the fuzzy logic concepts. Predefined fuzzy rules that formulate the general tuning guidelines available in the literature and the performance violation measure in the form of fuzzy sets determine the new tuning parameter values. Therefore, the tuning algorithm is cast as a simple and straightforward mechanism with modest computational requirements. This feature makes it more appealing for online implementation. The effectiveness of the proposed tuning method is tested through simulated implementation on a binary distillation column example and on a non linear CSTR example. The result of the simulations revealed the success of such a method.

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“…They set the conditioning number of the hessian of the DMC control problem equal to 500, which represents a good compromise between performance and robustness, and approximate the system model by first order plus dead time transfer functions, to develop analytical tuning expressions for the entries of matrix R. Liu and Wang (2000) considered the minimization of the sensitivity functions between the tuning parameters and the closedloop performance as the goals of a mixed-integer nonlinear optimization problem. Al-Ghazzawi et al (2001) defined the tuning goals in terms of the closed-loop output constraining envelopes, and a linear approximation of the process dynamics allowed the authors to obtain analytical sensitivity functions for Q y and R for constrained MPCs. When the constraints are active, sensitivity functions are calculated based on the Lagrange multipliers of the active constraints.…”

Section: Introductionmentioning

confidence: 99%

Tuning of Model Predictive Control With Multi-Objective Optimization

Yamashita

Zanin

Odloak

2016

Braz. J. Chem. Eng.

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-Two multi-objective optimization based tuning methods for model predictive control are proposed. Both methods consider the minimization of the error between the closed-loop response and an output reference trajectory as tuning goals. The first approach is based on the ranking of the outputs according to their importance to the plant operation and it is solved by a lexicographic optimization algorithm. The second method solves a compromise optimization problem. The former is designed for systems in which the number of inputs is equal to the number of outputs, while the latter can also be applied to non-square systems. The main contribution is an automated tuning framework based on a straightforward goal definition. The proposed methods are tested on a finite horizon model predictive controller in closed-loop with a 3x3 subsystem of the Shell Heavy Oil Fractionator benchmark system. The simulation results show that the methods proposed here can be a useful tool to reduce the commissioning time of the controller. The methods are compared to an existing multi-objective optimization based tuning approach. The computational time required to run the proposed tuning algorithms is considerably reduced when compared to the existing approach and, moreover, it does not need an a posteriori decision to select a solution from a set of Pareto optimal solutions.

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On-line tuning strategy for model predictive controllers

Cited by 98 publications

References 16 publications

Using MV overshoot as a tuning metric in choosing DMC move suppression values

Using MV overshoot as a tuning metric in choosing DMC move suppression values

On‐line Tuning of Model Predictive Controllers Using Fuzzy Logic

Tuning of Model Predictive Control With Multi-Objective Optimization

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