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.

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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This paper presents LQR and GA controllers which applied to control the speed of a DCmotor and to maintain the rotation of the motor shaft with particular step response. Inthe state space, the control strategy is the states feedback and the most used techniquesare the LQR. Liner quadratic regulator (LQR) provides an optimal control law for alinear system. It’s a control strategy based on minimizing a quadratic performanceindex. In despite of the good results obtained from these method, the control design isnot a straight forward task due to the trial and error method involved in the definition ofweight matrices. In such cases, may be hard tuning the controller parameters in order toobtain the optimal behavior of the system. In this work, it proposes a states feedbacktechnique in which there are no trial and error processes involved and the control designis carried out to fulfill specifications, for minimize overshoot and minimize settling andrising times. The proposed technique is based on the use a genetic algorithms. Theobtained results show that is possible to design controllers which fulfill designspecifications.