基于鲁棒建模的气动人工肌肉驱动仿生关节的轨迹跟踪控制

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

  • 摘要: 为了简单、高效地实现对由单根气动人工肌肉(pneumatic artificial muscle,PAM)驱动的仿生关节的轨迹跟踪控制,提出了一种基于鲁棒建模方法的级联控制策略.首先,通过解析法建立比例方向控制阀输入电压与 PAM 腔内驱动气压之间的非线性模型.其次,将 PAM 的驱动气压输入和关节角位移输出之间的非线性关系等效为一个含有参数摄动的 2 阶线性时不变模型,并通过鲁棒建模方法辨识模型参数.再者,对应由以上两个模型(即非线性模型和线性时不变模型)组成的混合模型,提出一种级联控制器,其外环是一个采用回路成形设计程序(loop-shaping design procedure,LSDP)法设计的用于角度位置跟踪的 H 控制器,内环是一个基于反馈线性化理论的用于 PAM 腔内驱动气压控制的非线性控制器.最后,在回转角度小于 90° 且工作频率上限小于 1.25 rad/s 的实验条件下,关节对参考轨迹实现了稳态误差小于 2% 的跟踪.本研究表明,在工作频率相对较低的情况下,采用所提策略可以对高度非线性系统实现有效的轨迹跟踪控制.

     

    Abstract: 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 with 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 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° and with the work 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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