# Linear Quadratic Optimal Control System Design Using Evolutionary Algorithms

# Linear Quadratic Optimal Control System Design Using Evolutionary Algorithms

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Introduction

In designing of many systems and solving the problems, we need to choose an answer between some possible answers as an optimal response. But because of the wide range of answers, all of them cannot be tested and then this test should be performed stochastically. On the other hand, this stochastic procedure should lead to the best answer [1].

Because of its simple implementation in engineering problems, it has been paid special attention on linear quadratic optimal control theory. Linear quadratic optimal control is significant for modern control theory and it can be implemented easily for engineering applications and it is the basic theory of other control techniques. However, in a special case which the cost function is a linear quadratic function, the o...

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...o. 5, pp: 1322- 1325, 2011.

[11] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int.Conf. Neural Networks, vol. IV, Perth, Australia, 1995, pp. 1942-1948.

[12] Eberhart, R. C., and Kermedy, J. “A New Optimizer Using Particles Swarm Theory,” Proc. Sixth International Symposium on Micro Machine and Human Science(Nagoya, Japan), IEEE Service Center, Pkcataway, NJ, 39-43, 1995.

[13] X. Xiong and Z. Wan , "The simulation of double inverted pendulum control based on particle swarm optimization LQR algorithm," IEEE International Conference on Software Engineering and Service Sciences (ICSESS), pp. 253- 256, 2010.

[14] M. Marinaki, Y. Marinakis, G. E. Stavroulakis, "Vibration control of beams with piezoelectric sensors and actuators using particle swarm optimization," Expert Systems with Applications, vol. 38, no. 6, pp. 6872- 6883, 2011.

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