From the Publisher:
Fuzzy Modeling for Control addresses fuzzy modeling from the systems and control engineering point of view. It focuses on the selection of appropriate model structures, on the acquisition of dynamic fuzzy models from process measurements (fuzzy identification), and on the design of nonlinear controllers based on fuzzy models. The main features of the presented techniques are illustrated by means of simple examples. In addition, three real-world applications are described. Finally, software tools for building fuzzy models from measurements are available from the author.

Fuzzy Modeling for Control

Evolutionary algorithms in control systems engineering: a survey

Fuzzy model predictive control of non-linear processes using genetic algorithms

A model predictive control apparatus includes an initial solution generation section, an abnormality handling section, and an objective function adjustment section. The abnormality handling section performs initial solution generation when it is determined that performed optimization is abnormal. The initial solution generation section generates an initial optimal value of a future time series control input in accordance with an initial objective function, without reference to a candidate value of the time series control input. The objective function adjustment section adjusts the current objective function so that the current objective function varies stepwise with time toward the normal objective function when it is determined that the current objective function is different from the normal objective function.

Model predictive control apparatus

Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the control problem is combined by using a decision function from the theory of fuzzy sets. This paper investigates the use of fuzzy decision making (FDM) in model predictive control (MPG), and compares the results to those obtained from conventional MPG. Attention is also paid to the choice of aggregation operators for fuzzy decision making in control. Experiments on a nonminimum phase, unstable linear system, and on an air-conditioning system with nonlinear dynamics are studied. It is shown that the performance of the model predictive controller can be improved by the use of fuzzy criteria in a fuzzy decision making framework.

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C. Onnen

Papers

Genetic algorithms for optimization in predictive control