QPSO-MPC based tracking algorithm for cable-driven continuum robots

Qi Chen; Yanan Qin; Gelun Li

doi:10.3389/fnbot.2022.1014163

QPSO-MPC based tracking algorithm for cable-driven continuum robots

Front Neurorobot. 2022 Oct 14:16:1014163. doi: 10.3389/fnbot.2022.1014163. eCollection 2022.

Authors

Qi Chen¹, Yanan Qin¹, Gelun Li²

Affiliations

¹ School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China.
² Robotics State Key Laboratory, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China.

Abstract

Cable-driven continuum robots (CDCRs) can flexibly travel through narrow space for complex workspace tasks. However, it is challenging to design the trajectory tracking algorithm for CDCRs due to their nonlinear dynamic behaviors and cable hysteresis characteristics. In this contribution, a model predictive control (MPC) tracking algorithm based on quantum particle swarm optimization (QPSO) is designed for CDCRs to realize effective trajectory tracking under constraints. In order to make kinematic analysis of a CDCR, the forward and inverse mapping among actuation space, joint space and work space is analyzed by using the piecewise constant curvature method and the homogeneous coordinate transformation. To improve the performance of conventional MPC for complex tracking tasks, QPSO is adopted in the rolling optimization of MPC for its global optimization performance, robustness and fast convergence. Both simulation and operational experiment results demonstrate that the designed QPSO-MPC presents high control stability and trajectory tracking precision. Compared with MPC and particle swarm optimization (PSO) based MPC, the tracking error of QPSO-MPC is reduced by at least 43 and 24%, respectively.

Keywords: MPC; QPSO; cable-driven continuum robots; control stability; trajectory tracking precision.