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An Adaptive Fuzzy Cerebellar Model Articulation Controller via Particle Swarm Optimization

机译:粒子群算法的自适应模糊小脑模型关节控制器

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摘要

In the paper, a Fuzzy Cerebellar Model Arithmetic Controller (FCMAC) is proposed to solve the tracking problems of a class of nonlinear systems. First, the proposed FCMAC via parameters adaptation such that it is able to approximate an ideal controller, and then a robust controller is appended to assure the system stability in the present of approximated error. Third, the redesign of the proposed FCMAC promotes the performances of the closed-loop system. Moreover, to further optimize the redesigned FCMAC, the Particle Swarm Optimization (PSO) is utilized to optimize the parameters of the proposed FCMAC under the requirements of system stability and the defined performance index. From the computer simulation results we can find that the performances of the system are promoted under the proposed control scheme.
机译:为了解决一类非线性系统的跟踪问题,提出了一种模糊小脑模型算法控制器(FCMAC)。首先,通过参数自适应使拟议的FCMAC能够逼近理想控制器,然后附加鲁棒控制器,以确保在出现近似误差时系统的稳定性。第三,提议的FCMAC的重新设计提高了闭环系统的性能。此外,为了进一步优化重新设计的FCMAC,在系统稳定性和定义的性能指标要求下,利用粒子群优化(PSO)来优化所提出的FCMAC的参数。从计算机仿真结果可以发现,在所提出的控制方案下,系统的性能得到了提高。

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