Multivariable nonlinear predictive control of cement mills

Magni, Lalo; Bastin, Georges; Wertz, Vincent

doi:10.1109/87.772166

Cited by 46 publications

(29 citation statements)

References 6 publications

(11 reference statements)

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“…It is noted that when there is a change of setpoint for (y f ) or (z) there is only a small deviation in the other loop compared to the other control strategy reported in the literature (Nonlinear robust controller [20], Nonlinear receding horizon (NRH) control [30], linear quadratic control [30], Nonlinear learning control [7], Neural Network based control [21]), also the effect of hardness change does not destabilize the cement mill.…”

Section: Resultsmentioning

confidence: 99%

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Evolutionary Design of Intelligent Controller for a Cement Mill Process

Subbaraj¹,

Anand²

2010

IJCA

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The Knowledge Base of a Fuzzy Logic Controller (FLC) encapsulates expert knowledge and consists of the Data Base (membership functions) and Rule-Base of the controller. Optimization of these Knowledge Base components is critical to the performance of the controller and has traditionally been achieved through a process of trial and error. Such an approach is convenient for FLCs having low numbers of input variables however for greater number of inputs, more formal methods of Knowledge Base optimization are required. Genetic Algorithms (GAs) provide such a method to optimize the FLC parameters. An intelligent multi input multi output (MIMO) control for the cement milling circuit is presented. The FLC is optimized by GA for varying nonlinearity in the plant. The proposed control algorithm was tested on the cement mill simulation model within MATLABTM SimulinkTM environment. Parameters of the simulation model were set up based on the actual cement mill characteristics. The performances of the proposed control technique are compared with various control technique. The results of the control study indicate that the proposed algorithm can prevent the mill from plugging and control the cement mill circuit effectively compared to the other control technique.

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Section: Resultsmentioning

confidence: 99%

“…The proposed approach shows a good performance in building the fuzzy logic controllers for a complex Cement mill process. The performance of our fuzzy controller is tested with cement mill circuit via simulation, and the results are compared with other control techiniques proposed in [7], [20], [21] and [30].…”

Section: Resultsmentioning

confidence: 99%

Evolutionary Design of Intelligent Controller for a Cement Mill Process

Subbaraj¹,

Anand²

2010

IJCA

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“…Assuming that the clinker hardness d is constant, the equilibria of the system y f ; y r ; z are parametrized by the constant inputs u and v y f = u (4) …”

Section: Stability Of Equilibriamentioning

confidence: 99%

“…In [4], a state feedback controller based on this nonlinear model is presented. It is build on a nonlinear predictive control strategy and, as such, is really the extension of the previous linear quadratic channel (LQG) controller of [1].…”

Section: Introductionmentioning

confidence: 99%

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Robust stabilization of a nonlinear cement mill model

Grognard

Jadot

Magni

et al. 2001

IEEE Trans. Automat. Contr.

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Load control of ball mill by a high precision sampling fuzzy logic controller with self‐optimizing

Cao

Zhang

et al. 2008

Asian Journal of Control

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A self-optimizing, high precision sampling fuzzy logic controller for keeping a ball mill circuit working stably and efficiently is proposed in this paper. The controller is based on fuzzy logic control strategy, and a fuzzy interpolation algorithm is presented to improve the control precision. The final output of the controller is calculated through the interpolation calculation of the observation and its neighboring antecedents, and the interpolation weight coefficients are obtained according to a fuzzy inference algorithm. In the proposed controller, the sampling control strategy is used to deal with a large delay time and a controller set value which can be adjusted by a self-optimizing algorithm, which can overcome the time-varying characteristic. Simulation results verify that the controller can control the ball mill circuit effectively and have higher control quality. Field service results also verify that the controller can successfully optimize the control of ball mill circuit.

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Multivariable nonlinear predictive control of cement mills

Cited by 46 publications

References 6 publications

Evolutionary Design of Intelligent Controller for a Cement Mill Process

Evolutionary Design of Intelligent Controller for a Cement Mill Process

Robust stabilization of a nonlinear cement mill model

Load control of ball mill by a high precision sampling fuzzy logic controller with self‐optimizing

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