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

10 Aug 2022  ·  Yang Wang, Qiang Zhang, Xiao-hui Xiao ·

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.

PDF Abstract
No code implementations yet. Submit your code now

Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods


No methods listed for this paper. Add relevant methods here