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MULTIPLE ARX MODELBASED AIRFUEL RATIO PREDICTIVE CONTROL FOR SI ENGINES
"... Abstract: In this article the predictive control is suggested to control the injection fuel pulse width in such a manner that the airfuel ratio deviates as little as possible from the stoichiometric ratio during the transients of the engine. The applied control strategy is based on the knowledge of ..."
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Abstract: In this article the predictive control is suggested to control the injection fuel pulse width in such a manner that the airfuel ratio deviates as little as possible from the stoichiometric ratio during the transients of the engine. The applied control strategy is based on the knowledge of an internal model of the airpath, predicting the change of the air flow through cylinders, and consequently, setting the prediction profile of the desired values of the objective function. The second modeled subsystem of the fuelpath is an explicit component of the objective function where the amount of the fuel is a function of the control action. It was demonstrated by simulation that multiple model predictive control has the potential to compete with standard lookup table strategies. Thus with further research in the predictive control of airfuel ratio, cleaner exhausts may be expected. Keywords: Airfuel ratio, multiple model predictive control, nonlinear parameter varying system 1.
Optimizing Nuclear Reactor Operation Using Soft Computing Techniques
"... Summary. The strict safety regulations for nuclear reactor control make it difficult to implement new control techniques such as fuzzy logic control (FLC). FLC however, can provide very desirable advantages over classical control, like robustness, adaptation and the capability to include human exper ..."
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Summary. The strict safety regulations for nuclear reactor control make it difficult to implement new control techniques such as fuzzy logic control (FLC). FLC however, can provide very desirable advantages over classical control, like robustness, adaptation and the capability to include human experience into the controller. Simple fuzzy logic controllers have been implemented for a few nuclear research reactors, among which the Massachusetts Institute of Technology (MIT) research reactor [1] in 1988 and the first Belgian reactor (BR1) [2] in 1998, though only on a temporal basis. The work presented here is a continuation of earlier research on adaptive fuzzy logic controllers for nuclear reactors at the SCK•CEN [2, 3, 4] and [5] (pp 65–82). A series of simulated experiments has been carried out using adaptive FLC, genetic algorithms (GAs) and neural networks (NNs) to find out which strategies are most promising for further research and future application in nuclear reactor control. Hopefully this contribution will lead to more research on advanced FLC in this domain and finally to an optimised and intrinsically safe control strategy.
SCK•CEN
"... This report presents the results of an investigation of different soft computing techniques for application in nuclear reactor control. All research and tests have been carried out using a computer simulation of a demonstration model, consisting of an emptying water tank which is refilled by two con ..."
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This report presents the results of an investigation of different soft computing techniques for application in nuclear reactor control. All research and tests have been carried out using a computer simulation of a demonstration model, consisting of an emptying water tank which is refilled by two controlled flows. This demo provides a safe and representative test bed, because despite of it’s simplicity it provides a highly nonlinear control problem. Starting with a standard fuzzy logic controller, different techniques have been applied to improve the performance and robustness and to generalize the controller design process. Rule base adaptation using only two simple guiding rules and membership optimization using genetic algorithms have resulted in a promising method to design highperformance controllers for industrial applications. To enable offline adaptation and optimization, which would be needed due to safety regulations, plant modeling using neural networks has been investigated. Although this technique matches best with the characteristics of the other methods applied (problem independence, no mathematical formulation is needed, high
Article Nonlinear Predictive Control of a Hydropower System Model
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1 Optimisation of Fuzzy Predictive Functional Control using coordination techniques
"... Abstract — Predictive functional control (PFC), belongs to the family of predictive control techniques. It has been demonstrated as a powerful algorithm for controlling process plant. In this paper, PFC strategy is extended to nonlinear processes. The predictive functional control is combined with a ..."
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Abstract — Predictive functional control (PFC), belongs to the family of predictive control techniques. It has been demonstrated as a powerful algorithm for controlling process plant. In this paper, PFC strategy is extended to nonlinear processes. The predictive functional control is combined with a fuzzy model of the process and formulated in the state space domain. The process model is decomposed into subsystems each described by a fuzzy rules. In controller design, prediction errors and control energy is minimised trough a two layered iterative optimization process. The lower layer finds local control policies for each sub system. The objective of the upper layer is to find a near optimum for the overall system trough coordinating the subsystems. The performance of the FPFC approach is demonstrated trough an example of CSTR process. Index Terms—Fuzzy identification, predictive control, decompositioncoordination, nonlinear systems. I.