Interpolation based predictive control

Rossiter, J.A.; Kouvaritakis, B.; Bacic, Marko

doi:10.23919/acc.2004.1383609

Cited by 20 publications

(27 citation statements)

References 12 publications

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“…Note that various simplifications are known to work well in the process control case [11,19,8,23,18]. For example,…”

Section: Application Of Receding Horizon Control To Mine Planningmentioning

confidence: 99%

“…Receding horizon optimisation has become a very successful strategy in real time control problems [11,19,8,23,18]. The basic idea is that one solves a fixed horizon optimisation problem to compute a sequence of predicted inputs over some prediction horizon (say T time steps) but one only implements (or stores) the first step.…”

Section: Application Of Receding Horizon Control To Mine Planningmentioning

confidence: 99%

“…Specifically, we define the mine state as a collection of pit depths at a certain number of surface locations and we represent the evolution of this state via a dynamic model that uses mining action as control input. We then use receding horizon optimal control [11,19,8,23,18] to solve the mine planning problem under constraints. This leads to several advantages, some of which are explained in the main body of the paper.…”

Section: Introductionmentioning

confidence: 99%

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Receding horizon control applied to optimal mine planning

et al. 2006

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In this paper we show that the problem of optimal mine planning can be cast in the framework of receding horizon control. Traditional formulations of this problem have cast it in the framework of mixed integer linear programming. In this paper, we present an alternative formulation of the mine planning problem using the "language" of control engineering. We show that this alternative formulation gives rise to new insights which have the potential to lead to improved computational procedures. The advantages are illustrated by an example incorporating many practical features of an actual mine planning problem.

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“…Note that various simplifications are known to work well in the process control case [11,19,8,23,18]. For example,…”

Section: Application Of Receding Horizon Control To Mine Planningmentioning

confidence: 99%

Section: Application Of Receding Horizon Control To Mine Planningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Receding horizon control applied to optimal mine planning

et al. 2006

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“…MPC has influenced not only the directions of development of industrial control systems but also research in this field (Brdyś and Tatjewski, 2005;Henson, 1998;Maciejowski, 2002;Morari and Lee, 1999;Qin and Badgwell, 2003;Rossiter, 2003;Tatjewski 2007).The most important advantage of MPC algorithms is the fact that they have the unique ability to take into account constraints imposed on process inputs (manipulated variables) and outputs (controlled variables) or state variables, which usually determine the quality, economic efficiency and safety of production. Furthermore, the MPC technique is very efficient in multivariable process control.…”

Section: Introductionmentioning

confidence: 99%

A Family of Model Predictive Control Algorithms With Artificial Neural Networks

Ławryńczuk¹

2007

International Journal of Applied Mathematics and Computer Science

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This paper details nonlinear Model-based Predictive Control (MPC) algorithms for MIMO processes modelled by means of neural networks of a feedforward structure. Two general MPC techniques are considered: the one with Nonlinear Optimisation (MPC-NO) and the one with Nonlinear Prediction and Linearisation (MPC-NPL). In the first case a nonlinear optimisation problem is solved in real time on-line. In order to reduce the computational burden, in the second case a neural model of the process is used on-line to determine local linearisation and a nonlinear free trajectory. Single-point and multi-point linearisation methods are discussed. The MPC-NPL structure is far more reliable and less computationally demanding in comparison with the MPC-NO one because it solves a quadratic programming problem, which can be done efficiently within a foreseeable time frame. At the same time, closed-loop performance of both algorithm classes is similar. Finally, a hybrid MPC algorithm with Nonlinear Prediction, Linearisation and Nonlinear optimisation (MPC-NPL-NO) is discussed.

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“…The DMC algorithm is a standard one in industrial applications (Blevins et al, 2003;Rossiter, 2003;Tatjewski, 2007). Rossiter (2003) claims that it is the most popular algorithm in industrial applications, and Blevins et al (2003) remark that "Most MPC implementations to date use step response models proven in DMC applications." It is so despite the fact that step response models can be applied only to open-loop stable plants.…”

Section: Introductionmentioning

confidence: 99%

Effective Dual-Mode Fuzzy DMC Algorithms with On-Line Quadratic Optimization and Guaranteed Stability

Marusak¹,

Tatjewski²

2009

International Journal of Applied Mathematics and Computer Science

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Dual-mode fuzzy dynamic matrix control (fuzzy DMC-FDMC) algorithms with guaranteed nominal stability for constrained nonlinear plants are presented. The algorithms join the advantages of fuzzy Takagi-Sugeno modeling and the predictive dual-mode approach in a computationally efficient version. Thus, they can bring an improvement in control quality compared with predictive controllers based on linear models and, at the same time, control performance similar to that obtained using more demanding algorithms with nonlinear optimization. Numerical effectiveness is obtained by using a successive linearization approach resulting in a quadratic programming problem solved on-line at each sampling instant. It is a computationally robust and fast optimization problem, which is important for on-line applications. Stability is achieved by appropriate introduction of dual-mode type stabilization mechanisms, which are simple and easy to implement. The effectiveness of the proposed approach is tested on a control system of a nonlinear plant-a distillation column with basic feedback controllers.

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Interpolation based predictive control

Cited by 20 publications

References 12 publications

Receding horizon control applied to optimal mine planning

Receding horizon control applied to optimal mine planning

A Family of Model Predictive Control Algorithms With Artificial Neural Networks

Effective Dual-Mode Fuzzy DMC Algorithms with On-Line Quadratic Optimization and Guaranteed Stability

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