Pneumatic Artificial Muscle Actuator Research Articles

Encrypted Simultaneous Control of Joint Angle and Stiffness of Antagonistic Pneumatic Artificial Muscle Actuator by Polynomial Approximation

Encrypted Simultaneous Control of Joint Angle and Stiffness of Antagonistic Pneumatic Artificial Muscle Actuator by Polynomial Approximation

Improving Exoskeleton Functionality: Design and Comparative Evaluation of Control Techniques for Pneumatic Artificial Muscle Actuators in Lower Limb Rehabilitation and Work Tasks

Improving Exoskeleton Functionality: Design and Comparative Evaluation of Control Techniques for Pneumatic Artificial Muscle Actuators in Lower Limb Rehabilitation and Work Tasks

Posture Estimation by Clustering Pressure Information and Control Implementation for Pneumatically Driven Gait-Assistive Robot

Pneumatic artificial muscles (PAMs) are light and soft, and are expected to be applied to gait assistance robots with multiple actuators on the human body. The PAMs can be used as not only actuators, but also sensors to detect the gait phase by using their deformable bodies whose internal pressure changes in response to the wearer’s gait. However, conventional methods to detect the gait phase by PAMs have targeted single point detection in a gait phase and used for only ON/OFF control of the PAM actuators. In this study, we proposed an algorithm to estimate the postural state of the wearer, especially the state of both hip joints, from the internal pressure information of the PAMs with a small amount of calculation by using clustering method, and succeeded in controlling the PAMs’ pressure continuously based on this algorithm. The effectiveness of the proposed control method was verified through gait-assistive experiments using a treadmill. We measured the electromyogram of the adductor longus muscle under 3 subjects and a one-sided significant difference test was performed. As a result, we confirmed significant differences at the 1% significance level for 2 subjects and at the 10% significance level for the remaining subject, allowing us to evaluate the effectiveness of the proposed PAM control strategy.

A Review on the Development of Pneumatic Artificial Muscle Actuators: Force Model and Application

Pneumatic artificial muscles (PAMs) are soft and flexible linear pneumatic actuators which produce human muscle like actuation. Due to these properties, the muscle actuators have an adaptable compliance for various robotic platforms as well as medical applications. While a variety of possible actuation schemes are present, there is still a need for the development of a soft actuator that is very light-weight, compact, and flexible with high power-to-weight ratio. To achieve this, the development of the PAM actuators has become an interesting topic for many researchers. In this review, the development of the different kinds of PAM available to date are presented along with manufacturing process and the operating principle. The various force models for artificial muscle presented in the literature are broadly reviewed with the constraints. Furthermore, the applications of PAM are included and classified based on the fields of biorobotics, medicine, and industry, along with advanced medical instrumentation. Finally, the needful improvements in terms of the dynamics of the muscle are discussed for the precise control of the PAMs as per the requirements for the applications. This review will be helpful for researchers working in the field of robotics and for designers to develop new type of artificial muscle depending on the applications.

Entrainment During Human Locomotion Using a Lightweight Soft Robotic Hip Exosuit (SR-HExo)

A gait entrainment study was conducted using a lightweight soft robotic hip exosuit (SR-HExo) that can apply perturbations at the hip joint during treadmill walking. Periodic mechanical perturbations were applied by flat fabric Pneumatic Artificial Muscle (ff-PAM) actuators starting at a subject’s preferred gait frequency and increasing up to 15% higher in 3% increments. Anterior hip flexion perturbations and posterior hip extension perturbations were tested in two separate experiments. All 11 healthy participants showed successful entrainment in all 12 experimental conditions (i.e., from preferred gait frequency to 15% higher in both flexion and extension perturbation directions). This study confirmed that there exists a single stable point attractor during gait entrainment to unilateral, unidirectional hip perturbations, which is consistent with previous ankle studies. Phase-locking was consistently observed around toe-off phase of the gait cycle (GC). Group averaged results showed gait synchronization with extension perturbations occurred earlier in the gait cycle (around 50% GC where the hip angle reaches maximum extension) than with flexion perturbations (just after 60% GC where the transition from maximum hip extension towards hip flexion occurs). Other gait entrainment characteristics (specifically, success rate of entrainment, basin of entrainment, and transient response) observed in this study posits the potential of the SR-HExo for entrainment-based gait training in rehabilitative contexts.

Novel Compliant Control of a Pneumatic Artificial Muscle Driven by Hydrogen Pressure Under a Varying Environment

In this article, a pneumatic artificial muscle (PAM) based on a metal hydride (MH) is considered for a compact compliant actuator. It is suitable for broad applications of the human–robot interaction (HRI). To address the problem of the HRI represented by a varying environment, a compliant control is introduced. In fact, the bottlenecks of improving the performance in the compliant control of the PAM actuator are: an inherent nonlinear dynamics of a PAM, the parametric and nonlinear uncertainties influenced by a varying environment, and an additional high dimension introduced by an MH employed as a driving force for the PAM. We propose a learning-based adaptive robust control (LARC) framework to tackle these challenges. A Bayesian learning technique deals with the parameter adaptation for the adaptive control. The effectiveness of the LARC has been examined in extensive experiments of tracking control.

Detailed Dynamic Model of Antagonistic PAM System and Its Experimental Validation: Sensorless Angle and Torque Control With UKF

This article proposes a detailed nonlinear mathematical model of an antagonistic pneumatic artificial muscle (PAM) actuator system for estimating the joint angle and torque using an unscented Kalman filter (UKF). The proposed model is described in a hybrid state-space representation. It includes the contraction force of the PAM, joint dynamics, fluid dynamics of compressed air, mass flows of a valve, and friction models. A part of the friction models is modified to obtain a novel form of the Coulomb friction depending on the inner pressure of the PAM. For model validation, offline and online UKF estimations and sensorless tracking control of the joint angle and torque are conducted to evaluate the estimation accuracy and tracking control performance. The estimation error is less than 7.91 %, and the steady-state tracking control performance is more than 94.75 %. These results confirm that the proposed model is detailed and could be used as the state estimator of an antagonistic PAM system.

Instability regions of pneumatic artificial muscle actuator subject to direct and parametric excitations

Instability regions of pneumatic artificial muscle actuator subject to direct and parametric excitations

Modeling, Control, and Simulation of a 3-Degrees of Freedom Mechanism Actuated by Pneumatic Artificial Muscles for Upper Limb Prosthesis Application

Abstract This work reports advances in enabling the use of pneumatic artificial muscles (PAM) as an alternative to electromechanical actuators in upper-limb prostheses to take advantage of their low weight, clean, renewable energy source, and force compliance, which is especially suitable for interactions between machines and humans. It describes the model, simulation, and control of a 3-degrees of freedom (DOF) mechanism representing a prosthetic finger using PAM actuators. Its main contribution is the combination and expansion of classic nonlinear models and control methods for robotic systems to allow PAM actuators in the intended application, especially regarding transient effects in the generation of PAM forces. The effectiveness of the proposed mechanism and its associated controller is verified through both numeric simulations and experimental results.

Cyber-secure pneumatic actuator system equipped with encrypted controller and attack detectors

A cyber-secure and flexible actuator is essential for cyber-physical-human systems. The use of wireless communication in such systems improves their usability but adds cybersecurity risks. This study proposes a cyber-secure flexible antagonistic pneumatic artificial muscle (PAM) actuator system. The proposed PAM actuator system has an encrypted proportional-integral controller and a real-time detector for cyberattacks. The controller is concealed in the encrypted control fashion using a homomorphic encryption scheme with security parameter 64 bit, and the control signal is computed over a cipher space. The detector has a threshold to detect significant changes in control inputs caused by cyberattacks such as signal injection and parameter falsification. The experimental results demonstrate that the proposed PAM actuator system achieves the desired control performance and improves the reliability of control operations under cyberattacks. The main contribution of this study is the first implementation of an encrypted controller for a pneumatic actuator to develop a secure PAM actuator with real-time detection of cyberattacks. The results of this study contribute to the future development of safe remote systems that interact with humans in the medical and industrial fields.

Sensorless angle and stiffness control of antagonistic PAM actuator using reference set

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 in the absence of an external force, and clarifies the allowable references for the PAM actuator system. To achieve a sensorless 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 the 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 the reference values by demonstrating that the reference set covers the experimentally obtained trajectories of the angle and stiffness.

Discrete-Time Sliding Mode Control based on Exponential Reaching Law of a Pneumatic Artificial Muscle Actuator

In this paper, a discrete-time sliding mode control (DSMC) approach based on exponential reaching law (ERL) is developed for a pneumatic artificial muscle (PAM) system to achieve high tracking performance and chattering alleviation. The proposed controller is designed based on an exponential term that dynamically adapts to the variation of the switching function, thus guarantees finite steps convergence and chattering phenomenon reduction simultaneously. Moreover, the effectiveness of the designed controller and its stability on a dual actuator PAM-driven system is demonstrated under various conditions. In addition, a leg-gait pattern is set as trajectory tracking for further validation. Finally, experiment results show that the proposed approach is effective and promising for rehabilitation applications.

Numerical Investigation on Vibration Performance of Flexible Plates Actuated by Pneumatic Artificial Muscle

This paper theoretically introduced the feasibility of changing the vibration characteristics of flexible plates by using bio-inspired, extremely light, and powerful Pneumatic Artificial Muscle (PAM) actuators. Many structural plates or shells are typically flexible and show high vibration sensitivity. For this reason, this paper provides a way to achieve active vibration control for suppressing the oscillations of these structures to meet strict stability, safety, and comfort requirements. The dynamic behaviors of the designed plates are modeled by using the finite element (FE) method. As is known, the output force vs. contraction curve of PAM is nonlinear generally. In this present finite element model, the maximum forces provided by PAM in different air pressure are adopted as controlling forces for applying for the plate. The non-linearity between the output force and displacement of PAM is avoided in this study. The dynamic behaviors of plates with several independent groups of controlling forces are observed and studied. The results show that the natural frequencies of the plate can be varying and the max amplitude decreases significantly if the controlling forces are applied. The present work also demonstrates the potential of the PAM actuators as valid means for damping out the vibration of flexible systems.

Initial-Rectification Barrier Iterative Learning Control for Pneumatic Artificial Muscle Systems With Nonzero Initial Errors and Iteration-Varying Reference Trajectories

Pneumatic artificial muscle actuators possess great potential in compliant rehabilitation devices since they are flexible and lightweight. The inherent high nonlinearities, uncertainties, hysteresis and time-varying characteristics in pneumatic artificial muscle systems brings much challenge for accurate system modeling and controller design. The angle tracking problem based on iterative learning control technology is considered in this work. This research proposes a new initial-rectification adaptive iterative learning control scheme for a pneumatic artificial muscle-actuated device with nonzero initial errors and iteration-varying reference trajectories. A barrier Lyapunov function is used to deal with the constraint requirement. A new initial rectification construction method is given to solve the nonzero initial error problem. Nonparametric uncertainties in the system are approximated by using a neural network, whose optimal weight is estimated by using difference learning method. As the iteration number increases, the system states of angle and angular velocity can accurately track the reference trajectories over the whole interval, respectively. In the end, the simulation results show excellent trajectory tracking performance of the iterative learning controller even if the reference trajectories are non-repetitive over the iteration domain.

Numerical and Experimental Study of a Flexible Trailing Edge Driving by Pneumatic Muscle Actuators

A static aeroelastic analysis of the flexible trailing edge is conducted to calculate the deformed shape, aerodynamic coefficients and corresponding driving pressure. A physical flexible trailing edge model is manufactured using a honeycomb structure, which is measured based on binocular vision. The quadratic response surface method is adopted to establish the pneumatic artificial muscle actuator model. The wire-pulley transmission model is built to identify the existence of equivalent forces and produce the equivalent forces as the substitute of actuation force. A finite element model of the flexible trailing edge is established, which is validated by the test data. A nonlinear relationship is found between the driving pressure and deflection angle. The pressure needed to bear the structural stiffness is found to be much larger than that of the aerodynamic load. With the increase in pressure, the magnitude of the lift coefficient increases less. However, the magnitude of the drag coefficient increases more with the increase in pressure under 0.2 MPa. When the driving pressure exceeds 0.2 MPa, the relationship between them is nearly linear.

Modified Stiffness-Based Soft Optical Waveguide Integrated Pneumatic Artificial Muscle (PAM) Actuators for Contraction and Force Sensing

This article presents design, fabrication, characterization, and experimental verification of a new sensing method (soft optical sensing) for pneumatic artificial muscle (PAM) actuators. Soft optical waveguides are designed and fabricated using the proposed modified stiffness concept for soft sensors. The design of the sensor allows us to measure both contraction and force of a PAM actuator under loaded conditions. This proposed method makes it possible to have a highly sensitive actuator system with linear contraction sensing. The sensorized PAMs are supposed to work in high-pressure applications with the ability to be used repeatedly without significant degradation in signal quality. To characterize and validate the proposed method, two PAM actuators make use of two separately designed test systems to plot and model the characteristic curves. Finally, the validation for control is provided using measured sensor data as feedback to the controller.

An Adaptive Fast Terminal Sliding Mode Controller of Exercise-Assisted Robotic Arm for Elbow Joint Rehabilitation Featuring Pneumatic Artificial Muscle Actuator

Due to the time-varying nonlinear dynamic, uncertain model and hysteresis characteristics of the pneumatic artificial muscle (PAM) actuator, it is not easy to apply model-based control algorithms for monitoring, as well as controlling, the operation of systems driven by PAM actuators. Hence, the main aim of this work is to propose an intelligent controller named adaptive sliding controller adding compensator (ASC + C) to operate a robotic arm, featuring a pneumatic artificial muscle actuator, which assists rehabilitation exercise of the elbow joint function. The structure of the proposed controller is a combination between the fuzzy logic technique and Proportional Integral Derivative (PID) algorithm. In which, the input of fuzzy logic controller is the sliding surface, meanwhile, its output is the estimated value of the unknown nonlinear function, meaning that the model-based requirement is released. A PID controller works as a compensator with online learning ability and is designed to compensate because of the approximate error and hysteresis characteristic. Additionally, to improve convergence and to obtain stability, a fast terminal sliding manifold is introduced and online learning laws for parameters of the controller are attainted through the stable criterion of Lyapunov. Finally, an experimental apparatus is also fabricated to evaluate control response of the system. The experimental result confirmed strongly the ability of the proposed controller, which indicates that the ASC + C can obtain a steady state tracking error less than 5 degrees and a position response without overshoot.

Novel Design and Modeling of a Soft Pneumatic Actuator Based on Antagonism Mechanism

The soft actuator possesses the characteristics of flexibility, environmental adaptability, and human–machine interaction. Firstly, aiming to resolve the limitation of variable stiffness performance of a traditional pneumatic artificial muscle (PAM) actuator, based on the antagonistic mechanism of extensor and contractor muscles, a novel pneumatic soft actuator coupled of extensor and contractor muscles is proposed in this paper. The actuator can perform the compound action of elongation/contraction, and the stiffness of it can be controlled by adjusting the elongation and contraction forces. Secondly, based on the deformation principle of woven and elastic fabric layers, the mechanical characteristics model of the actuator is established and simulated. The mechanical properties of the actuator are tested under different pressures and deformation displacement and the variable stiffness characteristics of the actuator are verified. Finally, actuators are utilized to manufacture a soft mechanical manipulator, which can achieve variable stiffness in a fixed bending attitude.

A neuroadaptive control method for pneumatic artificial muscle systems with hardware experiments

Pneumatic artificial muscle (PAM) actuators are a kind of biomimetic actuators, which are being widely used in the applications of biomimetic robots and medical auxiliary devices. However, PAM systems usually have high nonlinearities, uncertainties, and time-varying characteristics, which bring challenges for accurate dynamic modeling and controller design. To deal with the above issues, in this paper, a neuroadaptive control method is proposed to handle the system uncertainties and achieve satisfactory tracking performance. First, in order to compensate the unknown nonlinear term involved in the dynamic model of the PAM system online, a three-layer neural network is utilized. Next, by means of the filtered signal, the algebraic loop problem can be solved effectively. Then, based on a sliding mode surface, a nonlinear robust controller is designed. By using the proposed method, the asymptotic convergence of tracking errors of the PAM system is guaranteed, and the tracking errors are always restricted within preset bounds during the control process. Moreover, the stability of the closed-loop system is proven theoretically by utilizing Lyapunov techniques. Finally, a series of hardware experiments are implemented on a self-built PAM testbed to validate the effectiveness and robustness of the proposed neuroadaptive control method.

Numerical Investigation of Nonlinear Dynamics of a Pneumatic Artificial Muscle With Hard Excitation

Abstract In this work, a numerical analysis has been carried out to study the nonlinear dynamics of a system with pneumatic artificial muscle (PAM). The system is modeled as a single degree-of-freedom system and the governing nonlinear equation of motion has been derived to study the various responses of the system. The system is subjected to hard excitation and hence the subharmonic and superharmonic resonance conditions have been studied. The second-order method of multiple scales (MMS) has been used to find the response, stability, and bifurcations of the system. The effect of various system parameters on the system response has been studied using time response, phase portraits, and basin of attraction. In these responses, while the saddle node bifurcation is found in both super and subharmonic resonance conditions, the Hopf bifurcation is found only in superharmonic resonance condition. By changing different system parameters, it has been shown that the response with three periods leads to chaotic response for superharmonic resonance condition. This study will find applications in the design of PAM actuators.