Evolutionary Algorithms in Supervision of Error-Free Control

Evolutionary Algorithms in Supervision of Error-Free Control

Bohumil Sulc (Czech Technical University in Prague, Czech Republic) and David Klimanek (Czech Technical University in Prague, Czech Republic)
DOI: 10.4018/978-1-60566-814-7.ch003
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Evolutionary algorithms are well known as optimization techniques, suitable for solving various kinds of problems (Ruano, 2005). The new application of evolutionary algorithms represents their use in the detection of biased control loop functions caused by controlled variable sensor discredibility (Klimanek, Sulc, 2005). Sensor discredibility occurs when a sensor transmitting values of the controlled variable provides inexact information, however the information is not absolutely faulty yet. The use of discredible sensors in control circuits may cause the real values of controlled variables to exceed the range of tolerated differences, whereas zero control error is being displayed. However, this is not the only negative consequence. Sometimes, sensor discredibility is accompanied with undesirable and hardly recognizable side effects. Most typical is an increase of harmful emission production in the case of combustion control, (Sulc, Klimanek, 2005). We have found that evolutionary algorithms are useful tools for solving the particular problem of finding a software-based way (so called software redundancy) of sensor discredibility detection. Software redundancy is a more economical way than the usual hardware redundancy, which is otherwise necessary in control loop protection against this small, invisible control error occurrence. New results from a long-term tracking residuum trends show that credibility loss can be forecasted. Operators can be warned in advance that the sensor measuring the controlled variable needs to be exchanged. This need can be effectively reflected in maintenance plans. Namely, the standard genetic algorithm and the simulated annealing algorithm have been successfully applied and tested to minimize the given cost function. By means of these algorithms, a newly developed method is able to detect controlled variable sensor discredibility. When applied to combustion processes, production of harmful emissions can be kept within accepted limits. The application of the used evolutionary algorithms inclusive terminology transfer in this application area can serve as an explanatory case study to help readers gain a better understanding of the how the evolutionary algorithms operate.
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The above-mentioned controlled variable sensor discredibility detection represents a specific part of the fault detection field in control engineering. According to some authors (Venkatasubramanian, Rengaswamy, 2003, Korbic, 2004), fault detection methods are classified into three general categories: quantitative model-based methods, qualitative model-based methods, and process history based methods. In contrast to the mentioned approaches, where prior knowledge about the process is needed, for the controlled variable sensor discredibility detection it is useful to employ methods of evolutionary algorithms. The main advantage of such a solution is that necessary information about the changes in controlled variable sensor properties can be obtained with the help of evolutionary algorithms based on the standard process data – this is, in any case, acquired and recorded for the sake of process control.

In order to apply evolutionary algorithms to controlled variable sensor discredibility detection, a cost function was designed as a residual function e defined by the absolute value of difference between the sensor model output (ym) and the real sensor output (yreal),

e =|yrealym|(1)

The design of residual function e has been explained in detail (e.g. in Sulc, Klimanek, 2005).

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