Genetic Algorithms in Applns. [appl. math] by R. Popa

By R. Popa

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The Japan Society of Mechanical Engineers, Vol. 71, No. 701, pp. 206-213 (in Japanese). Whitney, D. ; Lozinski, C. A. & Rourke, J. M. (1986). Industrial Robot Forward Calibration Method and Results. J. Dyn. Syst. Meas. , Vol. 108, No. 1, pp. 1-8. 2 Model Predictive Controller Employing Genetic Algorithm Optimization of Thermal Processes with Non-Convex Constraints Goran Stojanovski and Mile Stankovski Ss Cyril and Methodius University, Skopje, Faculty of Electrical Engineering and Information Technologies, Institute of Automation and System Engineering Republic of Macedonia 1.

The controller tuning greatly affects the control system properties, such as robustness to disturbances and noise, performance and robustness to delays (Mansour, 2011). For example, figure 2 shows a block diagram of a simple cascade PID control of a plant. If a mathematical model of a plant can be derived, then it is possible to apply various design techniques for determining parameters of the controller that will meet the transient and steady-state specifications of the closed-loop system. However, if the plant is so complicated that its mathematical model cannot be easily obtained, then analytical approach to the design of PID controller is not possible (Ogata, 2002).

4. 3 Fitness function Fitness is a numeric value assigned to each member of a population to provide a measure of the appropriateness of each candidate solution (Goldberg, 2002). Fitness functions used in genetic PID controllers are generally based upon the error between the actual and predicted solutions. However, better solutions are achieved according to decreasing the error signal. Enhancing Control Systems Response Using Genetic PID Controllers 45 The overall operation of a genetic PID controller can be better explained through the flowchart shown in Figure 2.

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