Pneumatic Artificial Muscles Research Articles

Enhancing postural stability in musculoskeletal quadrupedal locomotion through tension feedback for CPG-based controller

A central pattern generator (CPG)-based controller enhances the adaptability of quadrupedal locomotion, for example, by controlling the trunk posture. The conventional CPG-based controllers with attitude control often utilized the posture angle as feedback information. However, if the robot's body is as soft as a musculoskeletal structure, it can detect the over-tilting of the trunk based on proprioceptive information of the muscles. In general, proprioceptive information such as muscle tension changes more rapidly than posture angle information. Therefore, a feedback loop based on proprioceptive information has great potential to respond to sudden disturbances that occur during locomotion over uneven terrain. In this research, we proposed a CPG-based controller utilizing the tension of soft pneumatic artificial muscles (PAMs). Musculoskeletal quadruped robots driven by PAMs are so soft, which prevents over-tilting of the trunk because the soft leg acts like a suspension. In addition, tension, one of the proprioceptive information of PAMs, exhibits high sensitivity to changes in trunk posture because the soft body's motion easily is affected by over-tilting of the trunk. To validate the efficacy of the proposed controller, we conducted numerical simulations with a simple quadruped model and experiments with a musculoskeletal quadruped robot. As a result, the tension feedback is not effective for posture stabilization on flat terrain whereas it is effective on uneven terrain. Moreover, the tension feedback improved the running velocity over uneven terrain. These results will enhance the locomotion capability of musculoskeletal quadruped robots, advancing their practical application.

Controller design and experimental validation of walking for a musculoskeletal bipedal lower limb robot based on the spring-loaded inverted pendulum model

In the study of PAM (McKibben-type pneumatic artificial muscle)-driven bipedal robots, it is essential to investigate whether the intrinsic properties of the PAM contribute to achieving stable robot motion. Furthermore, it is crucial to determine if this contribution can be achieved through the interaction between the robot’s mechanical structure and the PAM. In previous research, a PAM-driven bipedal musculoskeletal robot was designed based on the principles of the spring-loaded inverted pendulum (SLIP) model. The robot features low leg inertia and concentrated mass near the hip joint. However, it is important to note that for this robot, only the design principles were based on the SLIP model, and no specialized controller was specifically designed based on the model. To address this issue, based on the characteristics of the developed robot, a PAM controller designed also based on the SLIP model is developed in this study. This model-based controller regulates ankle flexion PAM to adjust the direction of the ground reaction force during robot walking motion. The results indicate that the proposed controller effectively directs the leg ground reaction force towards the center of mass during walking.

Design and Applications of High Force Generation in 3D-Printed Pneumatic Artificial Muscles

Increasing the force generation and scalability of soft pneumatic actuators poses significant challenges in robotics applications, especially where high load capacities and precise control are required. This paper presents UniBPAM, a novel, scalable soft pneumatic actuator optimized through 3D printing to enhance force generation. By analyzing design parameters, such as the number of sides, height, and origami factor, UniBPAM achieved a 21.48% increase in force generation. Scalable across diameters from 36 to 144 mm, the actuator demonstrated the ability to lift up to 57 kg with 84.9% strain at larger scales. Additionally, the BiBPAM variant enables bidirectional actuation in robotic joints via motion control, which is realized through PID systems, making it suitable for various robotic applications.

Hysteresis Compensation and Trajectory Tracking Control Model for Pneumatic Artificial Muscles

The optimum performance position control of pneumatic artificial muscles (PAM) is restricted by their in-built hysteresis and nonlinearity. The hysteresis is usually depicted by a phenomenological model, while the model mentioned above always only describes the hysteresis phenomenon under certain conditions. Thus, the universality of the compensator is due to its weakness in handling disparate outside conditions. Our research employs the FN–QUPI (feedforward neural network–quadratic unparallel Prandtl–Ishlinskii) model to depict the phenomenon of pressure-displacement hysteresis in PAMs. This model has high-precision expression and generalization ability for the PAM hysteresis phenomenon. According to this, an inverse model of the QUPI operator is established as a feedforward control while combining with the feedback control of incremental PID-type iterative learning. The results show that due to the hysteresis of PAM, the compound control of feedforward control and iterative learning has better tracking performance than the ordinary PID compound control in terms of convergence rate and stability. According to the mean absolute error (MAE) and root mean square error (RMSE) of the tracking process, it can be seen that the control model can achieve accurate nonlinear compensation, and the control system shows excellent robustness to different input signals.

Self‐Sensing Artificial‐Muscle‐Empowered Humanlike Perception, Interaction, and Positioning

Bioinspired soft and hybrid robots provide a promising solution to developing robots that can interact and collaborate with humans safely and effectively. Bellow‐like pneumatic artificial muscles can produce biological muscle‐like contraction with comparable response time and force output, but closed‐loop control has been a challenge without an effective length sensing solution. Herein, a self‐sensing artificial muscle (SSAM), which can sense its own length regardless of the external loadings and driving pressures, is proposed. Empowered by a seamlessly integrated mutual‐inductance‐based length sensor (MILS), the SSAM can sense its length change as small as 0.01 mm (0.012% of the initial liength 80 mm) in a wide range. The working principle of the MILS is analyzed theoretically to provide a design guideline. An SSAM‐based earthworm‐like locomotion robot is developed with the capability of knowing its real‐time body length change at different gaits. An artificial arm driven by one SSAM is also developed and demonstrated with humanlike capabilities of loading perception, interactive and responsive movements, and accurate positioning. This work provides a promising solution to develop muscle‐driven hybrid robotic systems with embodied intelligence for skilled manipulation and sophisticated human–machine interactions.

Adaptive Control Using Radial Basis Function Neural Networks for Pneumatic Artificial Muscle Systems

This study introduces a novel adaptive controller employing neural networks, particularly radial basis function (RBF) algorithms, to enhance the control performance of pneumatic artificial muscle (PAM)-based systems. The proposed controller seeks to address the nonlinearities and hysteresis inherent in PAM-based systems by integrating neural approximation. Experimental testing and comparisons with conventional controllers are conducted using an antagonistic configuration of PAMs. The results illustrate the precision and reliability of the proposed controller, suggesting potential for future advancements in trajectory tracking control of PAM-based systems.

Restoring Model of a Pneumatic Artificial Muscle with Structure Parameters: Analysis and Identification

Restoring Model of a Pneumatic Artificial Muscle with Structure Parameters: Analysis and Identification

Design and Implementation of a 3 Degrees of Freedom Robotic Arm Powered by Pneumatic Artificial Muscle

The development of a 3-DOF robotic arm powered by pneumatic artificial muscles is represented as an innovative approach in the field of robotics, with the advantages of lightweight and flexible design being combined with the power and control benefits of pneumatic actuation. The design process, challenges, and solutions that were encountered in the development of the robotic arm are outlined in this paper, with an emphasis on its potential applications in industrial and research settings being highlighted. Additionally, the robotic arm’s modular design is enabled to allow easy customization and scalability, making it possible for the arm to be tailored to a wide range of tasks, from precise laboratory work to more robust industrial applications.

A Piecewise Particle Swarm Optimisation Modelling Method for Pneumatic Artificial Muscle Actuators

Pneumatic artificial muscles (PAMs) possess compliant properties desirable for certain applications such as prosthetics and robotic structures. However, this compliance along with their inherent nonlinear dynamics make them difficult to accurately model and as such accurately control under certain control architectures. Common approaches to this problem include measuring the actuator’s physical properties and approximating a model based on these parameters or using deep learning methods to train a model with the actuator’s behaviours. This paper introduces an optimisation-based modelling approach based on a particle swarm optimisation (PSO) algorithm using a mass–spring–damper approximation for the PAM, as well as a piecewise modelling method that accounts for nonlinear dynamics. The use of optimisation to estimate model parameters removes the need to measure physical properties, and the three-element approximation allows for fast model generation with low computational complexity and training data requirements. Through multiple tests comparing model behaviour with real PAM motion, the accuracy of these models is confirmed to be promising for future work. Dynamic nonlinearities are properly accounted for using the piecewise modelling method, including both hysteresis and disproportionate input/output relationship across the stroke length of the actuator. Compared with other PAM modelling techniques, this method has improved generation time, lower computational load requirements, and complexity and can be applied to actuators for which the phenomenological mass–spring–damper model is a good approximation.

Development of enhanced force models to analyze the nonlinear hysteresis response of miniaturized pneumatic artificial muscles

Miniaturized pneumatic artificial muscles (MPAMs), designed to replicate natural muscle actuation, offer unique attributes such as a high power-to-weight ratio, flexibility, easy integration, and compactness, making them favourable for many applications. The present paper aims at the development of an accurate semi-analytical force model considering the effect of the bladder material and friction terms to predict the nonlinear force-deformation response of MPAMs during contraction and expansion cycles. Existing force models for MPAMs exhibit limitations to accurately capturing the force-deformation behaviours due to several simplification factors. This study enhances these models by integrating correction terms to accurately address the nonlinearity and frictional effects exhibited by MPAMs. An analysis of the hysteresis loops resulting from the cyclic loading and unloading of MPAMs under specific pressures is undertaken to compare different methodologies in order to determine the most accurate correction terms. To investigate the nonlinear behaviour of MPAMs, the stress-strain relationship of the bladder material and results from force-deformation experimental tests on the entire actuator are considered and for the effect of friction term, theoretical and empirical approaches are investigated. Results suggest that the theoretical force model based on analytical and empirical friction forces, respectively, slightly overestimates and underestimates the force experienced by MPAMs during contraction while slightly underestimate and overestimates during expansion, respectively. A comparative analysis between MPAMs featuring Ecoflex-50 silicone and Ecoflex30 + PDMS mixture as bladder materials has also been conducted to further investigate the effect of bladder materials on their force and contraction outputs under inlet pressures ranging from 0 kPa to 300 kPa. It is shown that the MPAM feauting Ecoflex-50 bladder, exhibits lower dead-band pressure and an overall reduced blocked force in comparison to MPAM with bladder made of Ecoflex30 + PDMS while exhibiting a substantially enhanced free contraction capacity.

Peristaltic Motion Enabled by Pneumatic Artificial Muscles (PAMs) as Structural "Soft-Stiff" Actuators in a Modular Worm-Inspired Robot.

This paper considers the design, manufacture, and testing of a prototype "soft-stiff" worm-inspired robot referred to herein, as the PneumaticallyActuated PeristaLtic Advancing Modular (PALAM) robot. The robot has a modular structure, mimicking the segmented nature of earthworms, and each segment is individually actuated by a set of three pneumatic artificial muscles (PAMs). The PAMs contract when inflated by pressurised air, generating a pulling force and fulfilling the role of biological muscles in the robot. The PAMs are made from the elastomer silicone rubber, which affords the robot flexibility and enables a wide range of real-life applications. A control-system is designed which can inflate any PAM on demand, and hence replicate the peristaltic motion of earthworms in the PALAM robot. Finally, this paper discusses a successful, low-cost, and widely accessible approach for the manufacture of the PAMs utilised herein. The PAMs can be scaled dimensionally and made from different materials with varying mechanical properties and behaviours, meaning that they are suitable for use in a wide range of robotics applications.

The Distributed Parameter Model of an Electro-Pneumatic System Actuated by Pneumatic Artificial Muscles with PWM-Based Position Control

Today, the analysis and synthesis methods of electro-pneumatic systems with position control actuated by pneumatic artificial muscles (PAMs) are quite well known. In these methods, pneumatic artificial muscle is considered as an object with lumped parameters. However, the PAM is an object with distributed parameters, where the pressure, density, and mass flow rate of gas are varied along the bladder length. Thus, in the case of certain design parameters of the pneumatic artificial muscle and certain frequencies of the supply pressure, resonant gas oscillations affected by the wave processes in the bladder may occur. Thereby, in the PWM-driven PAM-actuated system, certain operation frequencies of the control pneumatic valve can cause oscillations of gas in the bladder and in the connected pipeline. These processes could lead to vibrations of the executive device. To solve this practical problem, a distributed parameter model of the PAM that takes into account the pressure fluctuations in the bladder and in the pipeline was elaborated. Also, in this work, a new method in which the wave processes are described by ordinary differential equations instead of partial differential equations was proposed.

Integral sliding mode control for an anthropomorphic finger based on nonlinear extended state observer

Due to the disturbance of couplings, the anthropomorphic finger lacks sufficient stability and accuracy in joint motion control, which further affects the performance of complex grasping and operating for anthropomorphic hands. In order to obtain stable and accurate joint motion control effect, an anthropomorphic finger control strategy is proposed for an anthropomorphic finger driven by pneumatic artificial muscles (PAMs) in this paper. A nonlinear extended state observer (NESO) is presented to observe the disturbance of couplings for the anthropomorphic finger. An integral sliding mode controller (ISMC) is proposed to realize joint motion control and improve steady state performance. The convergences of the NESO and the ISMC are demonstrated by Lyapunov methods. Furthermore, experimental results illustrate the validity of the proposed control strategy.

Development and Application of Shape-Memory Polymer and Alloy Composite Sheets

The authors previously developed a flexible and multi-degree-of-freedom pneumatic artificial rubber muscle that uses shape-memory polymer (SMP) sheets with an embedded electrical heating wire. The bending direction and initial shape of the muscle can be changed by utilizing the large difference in the elastic modulus below and above the glass transition temperature, shape fixity, and shape recovery of SMPs. In this study, to improve performance, we propose a shape-memory composite (SMC) sheet that consists of SMP sheets with an embedded shape-memory alloy (SMA) wire used as an electric heating wire. The mechanical properties of the previously developed SMP sheets and the proposed SMC sheets are compared through shape recovery, bending, and tensile tests using prototypes. The motion of the artificial muscle with various samples attached is evaluated through an isometric test and bending angle measurements. The experimental results confirm that the use of the SMA wire improves the production reproducibility and shape recovery of the SMP sheets without degrading other mechanical properties or actuator performance.

Investigation of arm stabilization with proprioceptive sensory reflex pathways on a 2-DoF musculoskeletal robot actuated by pneumatic artificial muscles

ABSTRACT The proprioceptive sensory reflex (e.g. stretch reflex) plays an important role in perturbed arm stabilization. Neuroscience studies have widely identified the heteronymous spinal pathways in the human arm, which may contribute to stabilized movement. However, their interactions in living systems pose challenges for specific functional investigations through human experiments. Therefore, this paper concentrates on understanding the effects of two heteronymous pathways, specifically the transmission of monoarticular sensory feedback to innervate biarticular muscles, using the robot. We replicate specific reflex pathways on a 2-DoF musculoskeletal robot equipped with pneumatic artificial muscles. By adjusting the weights of pathways, which resemble human motor control, we evaluate our robot arm performance under the forearm and upper arm disturbances. The outcomes validate their effects on regulating biarticular muscle stimulation, resulting in more stable and coordinated end-effectory trajectories. These reflexive pathways provide insights into the mechanisms underlying human arm perturbations and stabilization. Additionally, the proposed bioinspired system with applicable reflex weights holds great potential in the adaptive response of musculoskeletal robots, guiding robotics researchers in controller design.

Preliminary Testing of a Passive Exoskeleton Prototype Based on McKibben Muscles

Upper-limb exoskeletons for industrial applications can enhance the comfort and productivity of workers by reducing muscle activity and intra-articular forces during overhead work. Current devices typically employ a spring-based mechanism to balance the gravitational torque acting on the shoulder. As an alternative, this paper presents the design of a passive upper-limb exoskeleton based on McKibben artificial muscles. The interaction forces between the exoskeleton and the user, as well as the mechanical resistance of the exoskeleton structure, were investigated to finalize the design of the device prior to its prototyping. Details are provided about the solutions adopted to assemble, wear, and regulate the exoskeleton’s structure. The first version of the device weighing about 5.5 kg was manufactured and tested by two users in a motion analysis laboratory. The results of this study highlight that the exoskeleton can effectively reduce the activation level of shoulder muscles without affecting the lumbar strain.

A GAN based PID controller for highly adaptive control of a pneumatic-artificial-muscle driven antagonistic joint

Upper limb prostheses are commonly propelled by pneumatic artificial muscles organized in an antagonistic arrangement. Nonetheless, the control of upper limb prostheses under changing/unknown situations is difficult and necessary for a variety of real-world applications. Adaptive control, learning-based control, and robust control have been studied to deal with such challenges. However, their adaptability is insufficient for prostheses used in daily life, which are exposed to variable task levels, user motor characteristics, and prosthetic features. This paper introduces a highly adaptive controller for the first time based on Generative Adversarial Nets and proportional–integral–derivative controller (G-PID controller). G-PID controller comprises a generator for generating compensation actions to enhance PID responsiveness when controlling the unknown/changing system. Moreover, it incorporates a discriminator that receives responses from both a user-preselected reference system and the compensated changing/unknown system, and simultaneously determines the source of these responses. Through continuous updates, the compensator modifies the response of unknown/changing system to align with the reference system, thereby facilitating adaptive control. The G-PID controller’s effectiveness is evaluated through 1-degree of freedom (DoF) joint and 2-DoF shoulder prostheses in simulation experiments, and further validated in prototype experiments focusing on online learning for unknown and time-varying payload. The results demonstrate its ability to deal with diverse types of unknowns/changes, marking a significant advancement towards incorporating prostheses seamlessly into daily life.

A Comparison of Pneumatic Actuators for Soft Growing Vine Robots.

Soft pneumatic actuators are used to steer soft growing "vine" robots while being flexible enough to undergo the tip eversion required for growth. In this study, we compared the performance of three types of pneumatic actuators in terms of their ability to perform eversion, quasi-static bending, dynamic motion, and force output: the pouch motor, the cylindrical pneumatic artificial muscle (cPAM), and the fabric pneumatic artificial muscle (fPAM). The pouch motor is advantageous for prototyping owing to its simple manufacturing process. The cPAM exhibits superior bending behavior and produces the highest forces, whereas the fPAM actuates fastest and everts at the lowest pressure. We evaluated a range of dimensions for each actuator type. Larger actuators can produce more significant deformations and forces, but smaller actuators inflate faster and can evert at a lower pressure. Because vine robots are lightweight, the effect of gravity on the functionality of different actuators is minimal. We developed a new analytical model that predicts the pressure-to-bending behavior of vine robot actuators. Using the actuator results, we designed and demonstrated a 4.8 m long vine robot equipped with highly maneuverable 60 × 60 mm cPAMs in a three-dimensional obstacle course. The vine robot was able to move around sharp turns, travel through a passage smaller than its diameter, and lift itself against gravity.

Hyperboloidal Pneumatic Artificial Muscle With Braided Straight Fibers

Hyperboloidal Pneumatic Artificial Muscle With Braided Straight Fibers

Soft Wrist Exosuit Actuated by Fabric Pneumatic Artificial Muscles

Soft Wrist Exosuit Actuated by Fabric Pneumatic Artificial Muscles