Search

In control system design, it is important to determine the controller type and controller parameters appropriately. PID controllers are mostly preferred in many applications. At the same time, it is known that PID controllers are insufficient to control higher order oscillatory systems with time delay. In such systems, PIDA controller is preferred instead of traditional PID controller. This paper presents a hybrid algorithm based optimization method for the control of higher order oscillatory systems with time delay. A hybrid algorithm combining the advantages of firefly and genetic algorithm is used to determine PIDA controller parameters. In the proposed method, a multi-criteria objective function is suggested by taking the settling and rising time, percent overshoot and steady state error criteria from the time response parameters. Two simulation studies are conducted to evaluate the performance of the proposed method, and the results are compared with some studies from the literature. In addition, the parameter uncertainties of the systems are analyzed and the robustness performance evaluations of the designed controllers are made. It is seen from the results obtained that the proposed method improves the transient and steady state response of higher order oscillatory systems with time delay and offers a fast and effective tuning method. [ABSTRACT FROM AUTHOR]

Recent studies show that the performance of Evolutionary Algorithms often depends on choosing appropriate parameter configurations. Thus, researchers have generally tuned these parameters either looking at the similar research areas in the literature or manually, e.g. Grid Search. However, searching the parameter manually is laborious and timeconsuming; therefore, meta-optimization techniques have become commonly used methods to adjust parameters of an algorithm. These techniques can be classified in two widespread manners: off-line, tuning parameters of an algorithm before the algorithm initiates, and on-line, tuning the parameters while it is working. In this paper, Bipolar Matching Tendency (BMT) algorithm has been chosen as the selection method of a Genetic Algorithm (GA). The new obtained algorithm is named GA-BMT and has been used for the first time as an online metaoptimizer. In addition, the paper utilizes two search algorithms (Grid Search, Coarse to Fine Search) with three meta-optimization methods (Standard GA, Particle Swarm Optimization, GA-BMT) to investigate the best parameter settings of the Standard GA for 17 test functions, and offers a comparative work by comparing their results. Furthermore, non-parametric statistical tests, Friedman and Wilcoxon Signed Rank, were performed to demonstrate the significance of the results. Based on the all results that achieved, GA-BMT presents a reasonable achievement. [ABSTRACT FROM AUTHOR]