论文标题

基于强大建模的气动人造肌肉驱动的仿生关节的轨迹跟踪控制

Trajectory Tracking Control of the Bionic Joint Actuated by Pneumatic Artificial Muscle Based on Robust Modeling

论文作者

Wang, Yang, Zhang, Qiang, Xiao, Xiao-hui

论文摘要

为了简单有效地实现由单个气动人造肌肉(PAM)驱动的仿生关节的轨迹跟踪控制,根据可靠的建模方法提出了级联的控制策略。首先,比例方向控制阀的输入电压与PAM的内部驾驶压力之间的关系在分析上表示为非线性模型。其次,PAM的驱动压力输入与仿生接头的角度位置输出之间的非线性关系被描述为二阶线性时间不变模型(LTI),并伴有参数扰动(等效地),然后该模型的参数通过可靠的建模方法鉴定出来。然后,基于两个模型(非线性模型和LTI模型)建立了混合模型,并与之相对应,开发了级联的控制器,其外环是针对循环塑形设计过程(LSDP)设计的角度位置跟踪的H-infinite控制器,并且内部环为非线式控制式驾驶式控制式置换式控制式置型备件的驾驶式控制式置换式置换式置换式置型置型置换式控制器。最后,实验是在90度的关节旋转范围内完成的,并且工作频率上限为1.25 rad/s。以及带有开发的级联控制器轨迹的关节给定的参考轨迹,其稳态误差小于2%。结果表明,在相对较低的工作频率的情况下,使用所提出的策略,高度非线性系统的轨迹跟踪控制非常有效。

To simply and effectively realize the trajectory tracking control of a bionic joint actuated by a single pneumatic artificial muscle (PAM), a cascaded control strategy is proposed based on the robust modeling method. Firstly, the relationship between the input voltage of the proportional directional control valve and the inner driving pressure of PAM is expressed as a nonlinear model analytically. Secondly, the nonlinear relationship between the driving pressure input of PAM and the angular position output of the bionic joint is described as a second-order linear time-invariant model (LTI) accompanied by parametric perturbations, equivalently, and then the parameters of the model are identified by the robust modeling method. Then, a hybrid model is established based on the two models (the nonlinear model and the LTI model) and corresponding to it, a cascaded controller is developed, the outer loop of which is an H-infinite controller for the angular position tracking designed by loop-shaping design procedure (LSDP) and the inner loop is a nonlinear controller based on the feedback linearization theory for the PAM driving pressure control. Finally, the experiment is accomplished within the joint rotation range of 90 degrees and with the working frequency upper bound of 1.25 rad/s. And the joint with the developed cascaded controller tracks given reference trajectories with steady-state errors smaller than 2%. Results show that the trajectory tracking control of a highly nonlinear system is highly efficient using the proposed strategy in the case of relatively low work frequency.

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