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.

For multiple input-multiple output (MIMO) systems, the most common control strategy is the linear quadratic regulator (LQR) which relies on state vector feedback. Despite this strategy gives very good result, it still has trial and error procedure to select the values of its weight matrices which plays a important role in reaching to the desiered system performance. In order to overcome this problem, the Genetic algorithm is used. The design of genetic algorithm based linear quadratic regulator (GA-LQR) utilized Integral time absolute error (ITAE) as a cost function for optimization. The propsed procedure is implemented on a linear model of gas turbine to control the generator spool’s speed and the output power.

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