Double-loop frame-based adaptive neural sliding-mode control of single-input 3-DOF flexible-joint manipulator

Abstract

Flexible-joint manipulators (FJMs) have been attracting much attention in recent decades. However, position control and vibration suppression of single-input (SI) multi-degrees-of-freedom (multi($⩾3$)-DOF) FJM is still a high mountain to climb due to the characteristic that some states of such FJMs cannot be directly controlled by the SI. This paper aims to carry out this challenging research. We present a double-loop frame-based adaptive neural sliding-mode control method for an uncertain horizontal SI 3-DOF FJM, i.e. a two-link FJM with only one actuator at the first joint. Since only the actuator angle can be directly controlled, in the inner loop, we design a torque controller so that the actuator angle tracks a time-varying reference actuator angle, designed in real-time by the outer loop. In the outer loop, we consider the reference actuator angle as a virtual control signal and design it based on the dynamic coupling relationships, aiming to indirectly control the first link to its target position and suppress the vibration of both links. Furthermore, to enhance the transient system performance, we plan the input signal of the outer loop (i.e. the target angle) as a smooth reference-trajectory signal. The validity and superiority of this method are demonstrated in simulations.

Keywords:

• Flexible-joint manipulator
• passive joint
• underactuated system
• double-loop frame
• vibration suppression
• neural network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, X. Z. Lai, upon reasonable request.

Additional information

Funding

This work was supported by the Young Scientists Fund of National Natural Science Foundation of China under Grant 62203408, the 111 project under Grant B17040, the Hubei Provincial Natural Science Foundation of China under Grant 2015CFA010, the ‘CUG Scholar’ Scientific Research Funds at China University of Geosciences (Wuhan) under Grant 2022088, and the Program of China Scholarship Council under Grant 202006410094.