Adaptive event-triggered robust tracking control of soft robots

TL;DR

This paper presents an adaptive event-triggered control approach for soft robots that ensures robust tracking performance despite uncertainties and disturbances, with proven finite-time stability.

Contribution

It introduces a novel switching function and an adaptive backstepping control strategy with event-triggering, enhancing robustness and efficiency in soft robot control.

Findings

Effective tracking control under uncertainties demonstrated

Finite-time stability of the control scheme proven

Case study confirms practical applicability

Abstract

Soft robots manufactured with flexible materials can be highly compliant and adaptive to their surroundings, which facilitates their application in areas such as dexterous manipulation and environmental exploration. This paper aims at investigating the tracking control problem for soft robots under uncertainty such as unmodeled dynamics and external disturbance. First, we establish a novel switching function and design the compensated tracking error dynamics by virtue of the command filter. Then, based on the backstepping methodology, the virtual controllers and the adaptive logic estimating the supremum of uncertainty impacts are developed for synthesizing an event-triggered control strategy. In addition, the uniformed finite-time stability certification is derived for different scenarios of the switching function. Finally, we perform a case study of a soft robot to illustrate the effectiveness of the proposed control algorithm.