Design of an Internal Model Controller Using Artificial Intelligence Method

This paper explores robust control strategies, specifically concentrating on dynamic systems using intelligent internal model control. It introduces an optimal gain, employing genetic algorithms to enhance both system stability and precision. The application of internal model control parameters is exemplified through a second-order system. The assessment of these techniques involves the use of objective functions, such as Integral Square Error, Integral Absolute Error, Integral Time Absolute Error, Integral of Time-Square Error, and the Dhaou et al. method. A comparative analysis of the results is conducted, placing specific emphasis on overshoot and the number of performance criteria.