Rule-based fuzzy modeling has been recognised as a powerful technique for the modeling
of partly-known nonlinear systems. Fuzzy models can effectively integrate information from …

Challenging optimisation problems, which elude acceptable solution via conventional
methods, arise regularly in control systems engineering. Evolutionary algorithms (EAs) …

In the Proportional-Integral-Derivative (PID) controllers the control signal is a linear function
of: the current control error (the proportional part), the past errors (the integral part) and the …

Decision making and control are two fields with distinct methods for solving problems, and
yet they are closely related. This book bridges the gap between decision making and control …

This paper introduces a new fuzzy control technique, which belongs to the popular family of
control algorithms, called Model Predictive Controllers. The method is based on a dynamic …

In the last years design procedures of urban drainage systems have shifted from end of pipe
design criteria to ambient water quality approaches requiring integrated models of the …

Image processing has been widely used for automation purposes in modern agriculture. The
algorithm development for the image segmentation is the most controversial and …

In recent years, new paradigms have emerged to replace-or augment-the traditional,
mathematically based approaches to optimization. The most powerful of these are genetic …

Fuzzy predictive control integrates conventional model predictive control with techniques
from fuzzy multicriteria decision making, translating the goals and the constraints to …

This paper presents a model predictive control (MPC) strategy based on genetic algorithm to
solve the boiler–turbine control problem. First, a Takagi–Sugeno (TS) fuzzy model based on …