A Systematic Design Strategy for Antagonistic Joints Actuated by Artificial Muscles

Dirven, Steven; McDaid, Andrew

doi:10.1109/tmech.2017.2766637

Cited by 10 publications

(3 citation statements)

References 19 publications

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“…With the rapid development of robot technology, the engineering and scientific community are eagerly looking forward to the advent of actuators and electromechanical system with better performance [1]. Pneumatic artificial muscle(PAM) is a kind of soft tubular actuator possessing many advantages, such as rapid response, low cost, high power weight ratio, etc [2]- [4]. These above-mentioned merits propels the increasing attention and wide application of PAM actuators in the field of bionic robots, exoskeleton and rehabilitation robots.…”

Section: Introductionmentioning

confidence: 99%

Error Tracking-Based Neuro-Adaptive Learning Control for Pneumatic Artificial Muscle Systems With Output Constraint

Zhu,

Yan

2023

IEEE Access

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Pneumatic muscle actuators are widely used in the manufacture of bionic robots and rehabilitation medical equipment. However, due to complicated inherent nonlinearities, time-varying characteristics and uncertainties, it is still a challenge to carry out the accurate dynamic modeling and controller design for PAM systems. To address above issues, we propose an error tracking-based neuro-adaptive iterative learning control scheme to get satisfactory non-uniform angle trajectory tracking performance. First, the error-tracking method is used to overcome the nonzero initial state error in iterative learning controller design for the PAM system. Second, a difference-learning neural network is utilized to compensate for unknown uncertainties in the PAM system dynamics. Moreover, a barrier Lyapunov function is applied to design controller so as to restrict the the difference between system out error and the desired error trajectory within the preset bound during each iteration. And the stability of the closed-loop system is proven theoretically by using Lyapunov synthesis. Finally, simulation results demonstrate the effectiveness of the proposed control scheme.

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Section: Introductionmentioning

confidence: 99%

Error Tracking-Based Neuro-Adaptive Learning Control for Pneumatic Artificial Muscle Systems With Output Constraint

Zhu,

Yan

2023

IEEE Access

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“…These studies experimentally determined a reference in joint stiffness, while they did not clarify an admissible reference set for joint stiffness and angle. Clarification of the admissible information is essential for the practical implementation and systematic design of PAM actuators for applications such as antagonistic force maximization [19]. The use of sensors such as an encoder and a force sensor can provide efficient tracking performance of a PAM actuator.…”

Section: Introductionmentioning

confidence: 99%

Sensor-less Angle and Stiffness Control of Antagonistic PAM Actuator Using Reference Set

Shin¹,

Kogiso²

2021

Preprint

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This paper proposes a simultaneous control method for the angle and stiffness of the joint in an antagonistic pneumatic artificial muscle (PAM) actuator system using only pressure measurements, and clarifies the allowable references for the PAM actuator system. To achieve a sensor-less control, the proposed method estimates the joint angle and contraction forces using an unscented Kalman filter that employs a detailed model of the actuator system. Unlike previous control methods, the proposed method does not require any encoder and force sensor to achieve angle and stiffness control of the PAM actuator system. Experimental validations using three control scenarios confirm that the proposed method can control the joint angle and stiffness simultaneously and independently. Moreover, it is shown that a reference admissible set can be used as an indicator to establish reference values by demonstrating that the reference set covers the experimentally obtained trajectories of the angle and stiffness.

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“…After decades of investigating particular PMA-driven systems, the development of more systematic concepts for integrating PMAs in robotic systems is one of the latest interest of PMA research. In [9] a general design concept for antagonistic PMA-driven systems is given. This paper gives an excellent guidance on how to combine PMAs with respect to their static force map.…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for a Systematic Integration of Pneumatic-Muscle-Actuator-Driven Joints into Robotic Systems Via a Torque Control Interface

et al. 2018

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In this paper, two different torque control approaches for PMA-driven (PMA = Pneumatic muscle actuator) revolute joints are presented and tested. In previous work controllers for PMA-driven robots are typically customized for the use on a specific robotic system. In contrast, the proposed controllers define a general control interface for every robot that is actuated by PMA-driven joints. It will be shown that controlling the torque of a PMA-driven joint enables the use of standard robotic motion control frameworks, because the torque represents the natural input of the robotic equation of motion. Therefore, both proposed torque control approaches are interconnecting PMAs and their challenging characteristics on the one hand and “conventional” motion control strategies for robots on the other hand. After a detailed discussion of two different torque control approaches, we show that a torque controller handles all characteristics and dynamics of a PMA-driven joint internally, which implies that only its bandwidth and its static torque characteristic must be taken into account for the design of the outer motion control loop. This feature simplifies the integration of PMA-driven joints in robotic systems enormously, as will be demonstrated by a design of a cascade-structured, flatness-based motion controller for an exemplary robot with one degree of freedom.

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A Systematic Design Strategy for Antagonistic Joints Actuated by Artificial Muscles

Cited by 10 publications

References 19 publications

Error Tracking-Based Neuro-Adaptive Learning Control for Pneumatic Artificial Muscle Systems With Output Constraint

Error Tracking-Based Neuro-Adaptive Learning Control for Pneumatic Artificial Muscle Systems With Output Constraint

Sensor-less Angle and Stiffness Control of Antagonistic PAM Actuator Using Reference Set

A Novel Framework for a Systematic Integration of Pneumatic-Muscle-Actuator-Driven Joints into Robotic Systems Via a Torque Control Interface

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