A trajectory tracking method based on robust model predictive control for a bionic ankle–foot aided by a tensegrity mechanism

Abstract

In this article, a robust model predictive control method is investigated for settling the trajectory tracking problem of a bionic ankle–foot aided by a tensegrity mechanism. In order to achieve adaptive movement of the ankle–foot mechanism, a three-degrees-of-freedom spatial ankle–foot mechanism is designed by tensegrity, which is a spatial grid structure composed of springs and struts. Dynamic analysis is the basis of control algorithm research, and the dynamic model of the mechanism can be established by a Lagrangian equation. Then, a controller is proposed for tracking the trajectory of the ankle–foot mechanism under external disturbances. Combining rolling optimization and feedback correction, the controller can be defined as an optimization problem, by solving which the ankle–foot mechanism can be controlled to track the desired trajectory quickly. Furthermore, stability analysis is an essential part of predictive controller design, which can help to understand the operational mechanism of the control strategy. Numerical results demonstrate that the proposed approach improves trajectory tracking accuracy and avoids mechanism movement problems caused by disturbances.

Keywords:

• Tensegrity
• bionic ankle–foot mechanism
• robot dynamics
• quadratic programming
• trajectory tracking

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data are available from the corresponding author on reasonable request.

Additional information

Funding

The work is supported in part by the National Natural Science Foundation of China [grants 62173048, 61873304, 62106023], and also in part by the Innovation and Entrepreneurship Talent Funding Project of Jilin Province [2022QN04], and also in part by the Changchun Science and Technology Project [grant 21ZY41].