PID controller has been frequently applied to various application areas because of mathematical definition and overall performance which is greatly depended on accuracy of the control parameters. Therefore, conventional and evolutionary algorithms have introduced to find these parameters as tuning methods. In present evolutionary algorithms have frequently used in PID tuning applications. However, the tuning performance greatly depends on the evaluation function. This study focuses on design of a particle swarm optimization (PSO) based PID controller by using 8 different fitness (evolution) functions. The performance of the optimized controllers is compared with respect to following criteria: Overshoot, Undershoot, Rise Time, Settling Time, and Steady State Error. To compare the proposed optimized controller, a DC-DC buck converter is selected as a test bed. Firstly, the parameters are optioned in the simulation environment, and then the optimized controllers are compared in the hardware circuit.
Abstract. Multi-objective optimization with more than three objectives has become one of the most active topics in evolutionary multiobjective optimization (EMO). However, most existing studies limit their experiments up to 15 or 20 objectives, although they claimed to be capable of handling as many objectives as possible. To broaden the insights in the behavior of EMO methods when facing a massively large number of objectives, this paper presents some preliminary empirical investigations on several established scalable benchmark problems with 25, 50, 75 and 100 objectives. In particular, this paper focuses on the behavior of the currently pervasive reference point based EMO methods, although other methods can also be used. The experimental results demonstrate that the reference point based EMO method can be viable for problems with a massively large number of objectives, given an appropriate choice of the distance measure. In addition, sufficient population diversity should be given on each weight vector or a local niche, in order to provide enough selection pressure. To the best of our knowledge, this is the first time an EMO methodology has been considered to solve a massively large number of conflicting objectives.
Gravitational Search Algorithm (GSA) is a novel optimization algorithm developed recently. Hence, it has not yet been applied for determination of the optimized parameters of microstrip patch antennas. Therefore, in this study, GSA has been applied for calculation of the length and width of the rectangular patch antenna. These parameters of rectangular patch antenna have been obtained under various resonant frequencies, substrate permittivity and thickness of the antenna.
Proportional-Integral-Derivative (PID) control is the most common method applied in the industry due to its simplicity. On the other hand, due to its difficulties, parameter tuning of the PID controllers are usually performed poorly. Generally, the design objectives are obtained by adjusting the controller parameters repetitively until the desired closed-loop system performance is achieved. This allows researchers to use more advanced and even some heuristic methods to achieve the optimal PID parameters. This paper focuses on application of the chaos embedded particle swarm optimization algorithm (CPSO) for PID controller tuning, and demonstrates how to employ the CPSO method to find optimal PID parameters in details. The method is applied to optimal PID parameter tuning for three typical systems with various ordered, and comparisons with the conventional PSO and the Ziegler-Nichols methods are performed. The numerical results from the simulations verify the performance of the proposed scheme.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.