Hashim H A, Abido M A
System Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
Comput Intell Neurosci. 2015;2015:704301. doi: 10.1155/2015/704301. Epub 2015 Apr 19.
This paper presents a comparative study of fuzzy controller design for the twin rotor multi-input multioutput (MIMO) system (TRMS) considering most promising evolutionary techniques. These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). In this study, the gains of four fuzzy proportional derivative (PD) controllers for TRMS have been optimized using the considered techniques. The optimization techniques are developed to identify the optimal control parameters for system stability enhancement, to cancel high nonlinearities in the model, to reduce the coupling effect, and to drive TRMS pitch and yaw angles into the desired tracking trajectory efficiently and accurately. The most effective technique in terms of system response due to different disturbances has been investigated. In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed.
本文针对双转子多输入多输出（MIMO）系统（TRMS），考虑最具前景的进化技术，开展了模糊控制器设计的对比研究。这些技术包括引力搜索算法（GSA）、粒子群优化算法（PSO）、人工蜂群算法（ABC）和差分进化算法（DE）。在本研究中，已使用上述技术对TRMS的四个模糊比例微分（PD）控制器的增益进行了优化。开发这些优化技术的目的是确定用于增强系统稳定性的最优控制参数，消除模型中的高度非线性，降低耦合效应，并高效且准确地将TRMS的俯仰角和偏航角驱动到期望的跟踪轨迹。研究了在不同干扰下，就系统响应而言最有效的技术。在这项工作中，观察到就解的质量和收敛速度而言，GSA是最有效的技术。
