| 引用本文: | 刘乐,刘鹏,王馨,方一鸣.基于龙伯格观测器的感应电机预设性能位置跟踪优化控制[J].控制理论与应用,2023,40(6):1043~1052.[点击复制] |
| LIU Le,LIU Peng,WANG Xin,FANG Yi-ming.Position tracking optimization control of induction motor with prescribed performance based on Luenberger observer[J].Control Theory and Technology,2023,40(6):1043~1052.[点击复制] |
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| 基于龙伯格观测器的感应电机预设性能位置跟踪优化控制 |
| Position tracking optimization control of induction motor with prescribed performance based on Luenberger observer |
| 摘要点击 2058 全文点击 570 投稿时间：2021-12-07 修订日期：2023-05-27 |
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| DOI编号 10.7641/CTA.2022.11194 |
| 2023,40(6):1043-1052 |
| 中文关键词 感应电机 龙伯格观测器 预设性能控制 反步滑模控制 混合智能优化算法 |
| 英文关键词 induction motor Luenberger observer prescribed performance control backstepping sliding mode control hybrid intelligent optimization algorithm |
| 基金项目 国家自然科学基金项目(61803327, 61873226), 河北省自然科学基金项目(F2020203018), 河北省创新能力提升计划项目(22567619H), 河北省高等 学校科学技术研究青年基金项目(QN2022134) |
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| 中文摘要 |
| 考虑暂稳态约束、控制参数优化及参数摄动和负载扰动等对感应电机位置跟踪控制性能的影响, 本文提出 了一种基于龙伯格观测器的预设性能优化控制方法. 首先, 针对电机转子磁链在实际中不可测的问题, 采用龙伯格 观测器对其进行了快速准确的估计. 其次, 基于反步法完成感应电机位置预设性能控制器的设计, 基于变增益指数 趋近律完成感应电机磁链滑模控制器的设计, 通过构造干扰观测器对电机系统中由参数摄动和负载扰动引起的不 确定项进行观测, 实现了对系统给定值准确的跟踪控制. 再次, 将遗传算法(GA)与改进的粒子群优化(IPSO)算法相 结合, 对所设计的控制器参数进行优化整定, 进一步提高了系统的收敛速度和稳态精度. 基于李雅普诺夫稳定性理 论分析表: 所设计的控制器能够保证位置跟踪误差一直处于预设边界内, 且整个闭环系统是全局一致有界稳定的. 最后, 通过仿真和模拟实验对比分析验证了本文所提方法的有效性及在实际电机系统中应用的可行性. |
| 英文摘要 |
| Considering the influences of transient steady-state constraint, control parameter optimization, parameter perturbation and load disturbance on the position tracking control performance of induction motor, a prescribed performance optimization control method is proposed based on the Luenberger observer. Firstly, aiming at the problem that the rotor flux linkage of induction motor is unmeasurable in practice, the Luenberger observer is used to estimate it quickly and accurately. Secondly, the position prescribed performance controller of the induction motor is designed based on the backstepping method, and the flux sliding mode controller of the induction motor is designed based on the variable gain exponential reaching law. Moreover, the disturbance observers are constructed to observe the uncertainties caused by the parameter perturbations and load disturbance in motor system, then the accurate tracking controls for the given values of the system are realized. Thirdly, the genetic algorithm (GA) is combined with the improved particle swarm optimization (IPSO) algorithm to optimize the parameters of the designed controllers, which further improves the convergence speed and steady-state accuracy of the system. The analysis based on the Lyapunov stability theory shows that the designed controllers can ensure that the position tracking error is always within the prescribed boundary, and the whole closed-loop system is globally uniformly bounded and stable. Finally, the effectiveness of the proposed control method and the feasibility of its application in the actual motor system are verified by the comparative researches of simulation and simulated experiments. |
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