基于粒子群优化技术和线性二次型调节器的磁悬浮系统控制器设计与优化

A. A. Abbas, H. Ammar, M. Elsamanty
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引用次数: 1

摘要

磁悬浮系统是一个实际的例子,它面临着一些非线性行为。这样的系统需要特殊类型的控制器参数来考虑准确的结果。在本文中,整定的过程是利用状态反馈(SF)控制器方法确定系统极点并使其远离不稳定区域。利用LQR控制器对得到的系统参数进行了估计。由于期望的目标是最小化系统行为中的重要参数,如稳态误差、系统的稳定时间、系统的上升时间和系统的超调量,因此优化技术被用于最小化需要优化的参数的代价函数,以达到更可靠的性能。采用粒子群优化算法(PSO)进行调谐。系统工作点电流为0.61 A,线圈表面与平衡金属球之间的距离为6mm,结果表明,采用LQR控制器会产生约33%的稳态误差和约20%的超调误差。采用粒子群优化技术对控制器参数的稳态误差小于6.5%,超调率为4%。
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Controller Design and Optimization of Magnetic Levitation System (MAGLEV) using Particle Swarm optimization technique and Linear Quadratic Regulator (LQR)
Magnetic Levitation System is one of practical examples which faces some nonlinearities behavior. Such systems require special types of controller parameters consideration for accurate results. In this paper, the process of tuning is to determine the system poles and getting them away from the instability region using state feedback (SF) controller methodology. The resulted controllable system parameters are estimated using LQR controller. Since the desired goal is to minimize vital parameters in the system behavior like the steady state error, settling time, raising time of the system and system overshoot, optimization techniques have been used to minimize cost function of the parameters which need to be optimized and reach for more reliable ones for better performance. Particle swarm optimization (PSO) has been used for tuning process. System operation points should be 0.61 A for electric current and 6 mm distance between coil surface and balanced metal ball, results show that using LQR controller will cause about 33% error percentage as steady state error and about 20% overshoot. Using PSO optimization technique for controller parameters will produce less steady state error of 6.5% with 4% overshoot percentage.
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