It is known that PID controller is used in every facet of industrial automation. The application of PID controller span from small industry to high technology industry. The aim of this paper is to design a position controller of a robot arm by selection of a PID parameters using genetic algorithm. The model of a robot arm is considered a third order system. And this paper compares two kinds of tuning methods of parameter for PID controller. One is the controller design by the genetic algorithm, second is the controller design by the Ziegler and Nichols method. It was found that the proposed PID parameters adjustment by the genetic algorithm is better than the Ziegler & Nichols' method. The proposed method could be applied to the higher order system also.
This work shows the design procedure of fractional-order proportional–integral-derivative (FOPID) controllers for an antenna azimuth position system based on genetic algorithm optimization approach. FOPID controllers are considered as a special type of the classical PID controller in which the derivative and integral elements have orders of fractions between zero and one. Therefore, FOPID controllers comprise two additional variables ( μ and λ ) in comparison with the typical PID controller. The FOPID is designed to control the azimuth angle of the antenna. Genetic algorithm (GA) will be employed to find an optimal value for the five FOPID controller’s gains using different type of fitness function including mean square error (MSE), integral square error (ISE) and integral time square error (ITSE). The achieved results exhibit an excellent steady and transient state performance of system, for instant fast settling and rise times as well as low present of peak overshoot.
<span>This work shows the design and tuning procedure of a discrete PID controller for regulating buck boost converter circuits. The buck boost converter model is implemented using Simscape Matlab library without having to derive a complex mathematical model. A new tuning process of digital PID controllers based on identification data has been proposed. Simulation results are introduced to examine the potentials of the designed controller in power electronic applications and validate the capability and stability of the controller under supply and load perturbations. Despite controller linearity, the new approach has proved to be successful even with highly nonlinear systems. The proposed controller has succeeded in rejecting all the disturbances effectively and maintaining a constant output voltage from the regulator.</span>
Linear Quadratic Regulator (LQR) is one of the most interesting control techniques adopted as a control strategy in state feedback. These types of techniques achieve good results but suffer from the problem of trial and error involved in the computation of weight matrices. The trial and error technique leads to hard tuning of the LQR controller parameters. This of course will lead to difficulty in reaching the optimal system performance. The paper attempts to solve the above difficulty via the selection of the LQR weight matrices using Genetic Algorithm GA. This proposed solution will avoid the trail and error involved in the state feedback technique. The proposed solution has been adopted in the design of position controller of a robot arm and the results of computer simulation have shown that the proposed solution fulfill specifications, for minimum overshoot , settling and rising times.
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