引用本文:梁定坤,孙宁,吴易鸣,陈轶珩,秦岩丁,方勇纯.采用扰动估计的气动人工肌肉系统非线性控制[J].控制理论与应用,2019,36(11):1912~1919.[点击复制]
LIANG Ding-kun,SUN Ning,WU Yi-ming,CHEN Yi-heng,QIN Yan-ding,FANG Yong-chun.Nonlinear control for pneumatic artificial muscle systems with disturbance estimation[J].Control Theory and Technology,2019,36(11):1912~1919.[点击复制]
采用扰动估计的气动人工肌肉系统非线性控制
Nonlinear control for pneumatic artificial muscle systems with disturbance estimation
摘要点击 2555  全文点击 831  投稿时间：2019-06-28  修订日期：2019-11-28
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DOI编号  10.7641/CTA.2019.90497
  2019,36(11):1912-1919
中文关键词  气动人工肌肉  扰动估计  最小二乘更新律  滑模控制
英文关键词  pneumatic artificial muscle  disturbance estimation  least squares method  sliding mode control
基金项目  国家重点研发计划项目(2018YFB1309000); 国家自然科学基金资助项目(61873134, U1706228); 天津市青年人才托举工程 (TJSQNTJ-2017-02)
作者单位E-mail
梁定坤 南开大学机器人与信息自动化研究所 liangdk@mail.nankai.edu.cn 
孙宁* 南开大学机器人与信息自动化研究所 sunn@nankai.edu.cn 
吴易鸣 南开大学机器人与信息自动化研究所  
陈轶珩 南开大学机器人与信息自动化研究所  
秦岩丁 南开大学机器人与信息自动化研究所  
方勇纯 南开大学机器人与信息自动化研究所  
中文摘要
      气动人工肌肉系统凭借其材质轻便、输出力大及柔顺性好等优势, 其运动控制研究近年来逐渐成为热点问题. 然而, 气动人工肌肉(pneumatic artificial muscle, PAM)系统所固有的特性(如迟滞、蠕变、非线性时变等), 为其控制方法设计与实现带来了挑战. 考虑到实际工作过程中, 系统往往遭受未知干扰的影响, 本文针对气动人工肌肉系统, 提出了一种基于干扰估计的非线性控制策略, 可在系统存在持续不确定干扰的情况下, 在线进行扰动抑制, 实现精确的跟踪控制. 具体而言, 本文先通过模型变换, 将系统不确定性、未建模动态、外部扰动等处理成集总扰动的形式. 随后, 结合自适应更新律及正则化最小二乘算法, 在线估计未知系统参数及扰动; 在精确扰动代数估计的基础上, 通过所提基于干扰估计的非线性控制器, 消除未知扰动对系统造成的影响, 并确保跟踪误差收敛至零. 此外, 经稳定性分析证明了跟踪误差的渐近收敛性. 最后, 通过硬件实验验证了本文方法的有效性及鲁棒性.
英文摘要
      Due to the advantages of lightweight material, large output force, and good flexibility, studies on motion control of pneumatic artificial muscle (PAM) systems have aroused increasing interests in recent years. However, some inherent characteristics (e.g., hysteresis, creep, nonlinear time-varying characteristics) of PAM systems also bring challenges to controller design and implementation. Considering that in practical applications, PAM systems are usually affected by unknown disturbances, this paper proposes a disturbance estimation-based nonlinear control method for PAM systems, which can deal with the lumped disturbance online, and achieve accurate tracking control simultaneously. Specifically, first, on the basis of some model transformation, system uncertainties, unmodeled dynamics, and external disturbances are expressed as a lumped disturbance term. Then, by introducing an adaptive update law and the normalized least squares method, unknown system parameters and disturbances can be estimated online; based on the exact estimation values of disturbances, the proposed method can eliminate the influence of unknown disturbances, and make the tracking error vanish. Finally, the effectiveness and robustness of the proposed method are verified by hardware experiments.