Control Pneumatic Soft Bending Actuator with Feedforward Hysteresis Compensation by Pneumatic Physical Reservoir Computing

2024-09-11

IEEE Robotics and Automation Letters

AI Summary

Pneumatic soft bending actuators suffer from low motion-tracking accuracy and poor environmental adaptability due to inherent hysteresis nonlinearity and actuation delay. Method: This paper proposes a feedforward hysteresis compensation method integrating Physics-based Reservoir Computing (PRC) with T-S fuzzy modeling, wherein a secondary soft actuator serves as a physical reservoir unit with nonlinear mapping capability to generate real-time compensatory signals for the target actuator. Contribution/Results: To the best of our knowledge, this is the first work embedding a physical system directly into a feedforward compensation architecture, achieving an optimal trade-off among modeling accuracy, robustness, and real-time performance. Experiments demonstrate that the proposed method reduces tracking error by 32% and inference latency by 87% compared to a classical Echo State Network (ESN). It enables stable open-loop and closed-loop bending control and exhibits strong robustness against payload variations and external disturbances.

Technology Category

• Machine Learning: Hardware-aware ML
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

The nonlinearities of soft robots bring control challenges like hysteresis but also provide them with computational capacities. This paper introduces a fuzzy pneumatic physical reservoir computing (FPRC) model for feedforward hysteresis compensation in motion tracking control of soft actuators. Our method utilizes a pneumatic bending actuator as a physical reservoir with nonlinear computing capacities to control another pneumatic bending actuator. The FPRC model employs a Takagi-Sugeno (T-S) fuzzy logic to process outputs from the physical reservoir. The proposed FPRC model shows equivalent training performance to an Echo State Network (ESN) model, whereas it exhibits better test accuracies with significantly reduced execution time. Experiments validate the FPRC model's effectiveness in controlling the bending motion of a pneumatic soft actuator with open-loop and closed-loop control system setups. The proposed FPRC model's robustness against environmental disturbances has also been experimentally verified. To the authors' knowledge, this is the first implementation of a physical system in the feedforward hysteresis compensation model for controlling soft actuators. This study is expected to advance physical reservoir computing in nonlinear control applications and extend the feedforward hysteresis compensation methods for controlling soft actuators.

Problem

• Soft Robotic Control
• Motion Control Precision
• Environmental Adaptability

Innovation

• FPRC
• Soft Pneumatic Actuators
• Control Delay