基于改进粒子群优化算法的MFAC参数寻优  

Optimization of MFAC Parameters Based on Improved Particle Swarm Optimization Algorithm

作　　者：冯增喜[1,2] 张聪 李丙辉 FENG Zeng-xi;ZHANG Cong;LI Bing-hui(School of Building Services Science and Engineering,Xi’an University of Architecture and Technology,Xi’an 710055,China;Anhui Key Laboratory of Intelligent Building and Building Energy Conservation,Anhui Jianzhu University,Hefei 230022,China)

机构地区：[1]西安建筑科技大学建筑设备科学与工程学院,陕西西安710055 [2]安徽建筑大学智能建筑与建筑节能安徽省重点实验室,安徽合肥230022

出　　处：《控制工程》2021年第4期766-773,共8页Control Engineering of China

基　　金：国家重点研发计划项目专题(2017YFC0704104-03);陕西省科技厅专项科研项目(2017JM6106);安徽建筑大学智能建筑与建筑节能安徽省重点实验室2018年度开放课题(IBES2018KF08)。

摘　　要：针对无模型自适应控制算法目前存在的参数整定和优化困难问题,提出一种改进的粒子群优化算法来优化控制器参数。该算法通过引入基于逻辑斯谛映射产生的混沌算子,并采用线性减小策略调整惯性权重和加速因子,实现对无模型自适应控制参数η、μ、ρ和λ的自动寻优。以典型非线性系统为控制对象进行了仿真。结果表明,改进的粒子群优化算法具有更好的寻优速度和精度;采用优化参数进行控制后,超调量大幅减小,期望输出阶跃变化或系统模型改变时出现的振荡现象被有效克服,使系统具有更强的抗干扰性和自适应性。Aiming at the difficulties in parameter tuning and optimization of model-free adaptive control(MFAC) algorithms, an improved particle swarm optimization(IPSO) algorithm is proposed to optimize the controller parameters. The algorithm introduces chaotic operators based on logistic map and uses linear reduction strategies to adjust inertia weights and speed factors to achieve automatic optimization of MFAC parameters η, μ, ρ and λ. At the same time, simulations are carried out with typical nonlinear systems as the control objects. The results show that the improved particle swarm optimization algorithm has better optimization speed and accuracy. After the optimization parameters are used for control, the overshoot is greatly reduced, and the oscillation that occurs when the expected output has step changes or the system model changes is effectively overcome, which makes the controlled system have stronger anti-interference and adaptability.

关 键 词：粒子群优化 无模型自适应控制 寻优 逻辑斯谛映射 

分 类 号：TP29[自动化与计算机技术—检测技术与自动化装置]