Search Results (62)

Biomimetic quadruped robots, inspired by the musculoskeletal systems of animals, employ pneumatic artificial muscles (PAMs) as compliant actuators to achieve flexible, efficient, and adaptive locomotion. This study focuses on a pneumatic artificial muscle (PAM)-driven biomimetic leg joints system. First, its kinematic and dynamic models are established. Next, to address the challenges posed by the strong nonlinearities and complex time-varying uncertainties inherent in PAMs, an adaptive robust control algorithm is proposed by employing the Udwadia controller. Rigorous theoretical analysis of the adaptive robust control algorithm is verified via the Lyapunov stability method. Finally, numerical simulations and hardware experiments are conducted on the PAM-driven biomimetic leg joints system under desired trajectories, where the adaptive robust control algorithm is systematically compared with three conventional control algorithm to evaluate its control performance. The experimental results show that the proposed controller achieves a maximum tracking error of within 0.05 rad for the hip joint and within 0.1 rad, highlighting its strong potential for practical deployment in real-world environments. Full article

Pneumatic artificial muscles (PAMs) are inherently compliant and relatively safe. They are widely used in applications where human beings and robots interact closely, such as service robots or medical robots. However, PAMs are constrained by bulky pumps and valve control systems, limiting their mobility, portability, and practical applications. In this research, a novel type of artificial muscle, namely Twisting Tube Artificial Muscle (TTAM), is presented. In a TTAM design, fluid (pressurized air in this research) is contained inside an elastic tube (constrained by a braiding). By twisting the tube from one end, the fluid inside the twisted part will be extruded to the untwisted part, resulting in a pressure increase inside the untwisted part. Both the twisted and untwisted parts will thus contract. Modeling and experimental characterization of the TTAM are conducted. In an experimental test at 100 kPa initial air pressure, after a 6π twisting angle, the internal pressure of a prototype TTAM is increased to 219 kPa, and the largest contraction force of the TTAM was up to 200 N. A novel antagonistic robotic joint actuated by two TTAMs is developed as a sample application. Full article

Pneumatic Artificial Muscles (PAMs) are soft actuators that mimic the contractile behavior of biological muscles through fluid-driven deformation. Originating from McKibben’s 1950s braided design, PAMs have evolved into a diverse class of actuators, offering high power-to-weight ratios, compliance, and safe human interaction, with applications spanning rehabilitation, assistive robotics, aerospace, and adaptive structures. This review surveys recent developments in actuation mechanisms and applications of PAMs. Traditional designs, including braided, pleated, netted, and embedded types, remain widely used but face challenges such as hysteresis, limited contraction, and nonlinear control. To address these limitations, researchers have introduced non-traditional mechanisms such as vacuum-powered, inverse, foldable, origami-based, reconfigurable, and hybrid PAMs. These innovations improve the contraction range, efficiency, control precision, and integration into compact or untethered systems. This review also highlights applications beyond conventional biomechanics and automation, including embodied computation, deployable aerospace systems, and adaptive architecture. Collectively, these advances demonstrate PAMs’ expanding role as versatile soft actuators. Ongoing research is expected to refine material durability, control strategies, and multifunctionality, enabling the next generation of wearable devices, soft robots, and energy-efficient adaptive systems. Full article

Chronic low back pain (cLBP) is a pervasive and debilitating condition that can result in motor control deficits and often leads to opioid dependence. Conventional rehabilitation approaches generally rely on internally driven tasks, which fail to capture adaptive motor responses to external perturbations. This study focuses on the design and evaluation of a pneumatic-actuated active balance board integrating pneumatic artificial muscles (PAMs), electromyography (EMG), and inertial measurement units (IMUs) to assess seated postural control responses. With PAM-powered perturbations, the balance board introduces controlled challenges to evaluate postural control dynamics and motor adaptation. EMG sensors monitor muscle activity in key postural muscles, while IMU systems track movement responses. The system was evaluated through an experimental trial with 15 healthy participants performing balance tasks on both a passive and active balance board. The active balance board’s effectiveness is assessed using signal processing techniques, including root mean square (RMS) analysis, Fast Fourier Transform (FFT), autoregressive (AR) modeling, and the Welch t-test. Experimental trials were conducted with healthy participants to establish baseline performance. Results demonstrate that the active balance board successfully induces adaptive motor responses, with higher EMG activation levels compared to passive boards. Frequency-domain analyses confirm significant differences in muscle activation patterns, supporting the hypothesis that external perturbations enhance postural control retraining. The pneumatic-actuated balance board presented in this study represents a novel approach to postural control assessment that may be applied in future rehabilitation studies involving individuals with cLBP, addressing the limitations of traditional methods. Future research will focus on clinical trials with cLBP patients to further evaluate its therapeutic efficacy and long-term benefits in rehabilitation. Full article

Existing pneumatic artificial muscle (PAM) static geometrical models based on the principle of virtual work provide only approximate force predictions since they neglect the effects of volume change and radial bladder deformation work loss. In this study, we propose an improved geometrical static model called the Accurate Volume and Bladder Deformation Loss (AVBDL) model. This model introduces a physically consistent calculation of muscle volume at different contractions and pressures and incorporates a new way of describing work losses due to radial deformation of the bladder. The hyperelastic properties of the bladder were experimentally characterized and modeled using the Mooney–Rivlin formulation. The AVBDL model was validated against experimental data from four types of pneumatic muscles and compared with three established analytical models. Results show that the AVBDL model significantly improves force prediction accuracy, achieving a normalized root mean square (NRMS) error of 6.7–16.4%, compared to 20–68% for existing models. Due to its analytical transparency, reduced error, and broad applicability, the AVBDL model provides a robust basis for accurate simulation and control of pneumatic artificial muscles. Full article

To enable the practical application of passive back-support exoskeletons employing pneumatic artificial muscles (PAMs) in tasks such as agricultural work, we evaluated their assistive effectiveness in a half-squatting posture with a staggered stance. In this context, assistive force profiles were adjusted according to body posture to achieve more effective support. The targeted assistive force profile was designed to be continuously active from the standing to the half-squatting position, with minimal variation across this range. The assistive force profile was developed based on a PAM contractile force model and implemented using a cam mechanism. The effectiveness of assistance was assessed by measuring body flexion angles and erector spinae muscle activity during lifting and carrying tasks. The results showed that the assistive effect was greater on the side with the forward leg. Compared to the condition without exoskeleton assistance, the conventional pulley-based system reduced muscle activity by approximately 20% whereas the cam-based system achieved a reduction of approximately 30%. Full article

Pneumatic artificial muscles (PAMs) consist of an elastomeric bladder wrapped in a Kevlar braid. When inflated, PAMs expand radially and contract axially, producing large axial forces. PAMs are often utilized for their high specific work and specific power, as well as their ability to produce large axial displacements. Although the axial behavior of PAMs is well studied, the radial behavior has remained underutilized and is poorly understood. Modeling was performed using a force balance approach to capture the effects that bladder strain and applied axial load have on the anchoring force. Radial expansion testing was performed to validate the model. Force due to anchoring was recorded using force transducers attached to sections of aluminum pipe using an MTS servo-hydraulic testing machine. Data from the test were compared to the predicted anchoring force. Radial expansion in large-diameter (over 50.8 mm) PAMs was then used in worm-like robots to create anchoring forces that allow for a peristaltic wave, which creates locomotion through acrylic pipes. By radially expanding, the PAM presses itself into the pipe, creating an anchor point. The previously anchored PAM then deflates, which propels the robot forward. Modeling of the radial expansion forces and anchoring was necessary to determine the pressurization required for proper anchoring before slipping occurs due to the combined robot and payload weight. Full article

Multi-fingered dexterous hands provide superior dexterity in complex manipulation tasks due to their high degrees of freedom (DOFs) and biomimetic structures. Inspired by the anatomical structure of human tendons and muscles, numerous robotic hands powered by pneumatic artificial muscles (PAMs) have been created to replicate the compliant and adaptable features of biological hands. Nonetheless, PAMs have inherent nonlinear and hysteresis behaviors that create considerable challenges to achieving real-time control accuracy and stability in dexterous hands. In order to address these challenges, this paper proposes a hierarchical control architecture that employs a fuzzy PID strategy to optimize the nonlinear control of pneumatic artificial muscles (PAMs). The FPGA-based hardware integrates a multi-channel digital-to-analog converter (DAC) and a multiplexed acquisition module, facilitating the independent actuation of 20 PAMs and the real-time monitoring of 20 joints. The software implements a fuzzy PID algorithm that dynamically adjusts PID parameters based on both the error and the error rate, thereby effectively managing the nonlinear behaviors of the hand. Experimental results demonstrate that the designed control system achieves high precision in controlling the angle of a single finger joint, with errors maintained within ±1°. In scenarios involving multi-finger cooperative grasping and biomimetic motion demonstrations, the system exhibits excellent synchronization and real-time performance. These results validate the efficacy of the fuzzy PID control strategy and confirm that the proposed system fulfills the precision and stability requirements for complex operational tasks, providing robust support for the application of PAM-driven multi-fingered dexterous hands. Full article

This article presents a series of innovative systems developed through student laboratory projects, comprising two autonomous vehicles, a quadrupedal walking robot, an active ankle-foot orthosis, a ball-on-beam balancing mechanism, a ball-on-plate system, and a manipulator arm, all actuated by pneumatic artificial muscles (PAMs). Due to their flexibility, low weight, and compliance, fluidic muscles demonstrate substantial potential for integration into various mechatronic systems, robotic platforms, and manipulators. Their capacity to generate smooth and adaptive motion is particularly advantageous in applications requiring natural and human-like movements, such as rehabilitation technologies and assistive devices. Despite the inherent challenges associated with nonlinear behavior in PAM-actuated control systems, their biologically inspired design remains promising for a wide range of future applications. Potential domains include industrial automation, the automotive and aerospace sectors, as well as sports equipment, medical assistive devices, entertainment systems, and animatronics. The integration of self-constructed laboratory systems powered by PAMs into control systems education provides a comprehensive pedagogical framework that merges theoretical instruction with practical implementation. This methodology enhances the skillset of future engineers by deepening their understanding of core technical principles and equipping them to address emerging challenges in engineering practice. Full article

This paper presents a model predictive controller (MPC) based on dynamic models generated using the Particle Swarm Optimisation method for accurate motion control of a pneumatic artificial muscle (PAM) for application in rehabilitation robotics. The physical compliance and lightweight nature of PAMs make them desirable for use in the field but also introduce nonlinear dynamic properties which are difficult to accurately model and control. As well as the MPC, three other control systems were examined for a comparative study: a particle-swarm optimised proportional-integral-derivative controller (PSO-PID), an iterative learning controller (ILC), and classical PID control. A series of different waveforms were used as setpoints for each controller, including addition of external loading and simulated disturbance, for a system consisting of a single PAM. Based on the displacement error measured for each experiment, the PID controller performed worst with the largest error values and an issue with oscillating about the setpoint. PSO-PID performed better but still poorly compared with the other intelligent controllers, as well as still exhibiting oscillation, which is undesirable in any human–robot interaction as it can heavily impact the comfort and safety of the system. ILC performed well with rapid convergence to steady-state and low-error values, as well as mitigation of loads and disturbance; however, it performed poorly under changing frequency of input. MPC generally performed the best of the controllers tested here, with the lowest error values and a rapid response to changes in setpoint, as well as no required learning period due to the predictive algorithm. Full article

McKibben’s muscle (MKM) is the most adopted among the different types of pneumatic artificial muscles (PAMs) due to its mechanical performance and versatility. Several geometric parameters, including the diameter, thickness, and length of the inner elastic element, as well as functional conditions, such as shortening ratio and feeding pressure, influence the behaviour of this actuator. Over the years, analytical and numerical models have been defined to predict its deformation and developed forces. However, these models are often identified under simplifications and have limitations when integrating new parameters that were not initially considered. This work proposes a hybrid approach between finite element analyses (FEAs) and machine learning (ML) algorithms to overcome these issues. An MKM was numerically simulated as the chosen parameters changed, realizing the MKM dataset. The latter was used to train 27 artificial neural networks (ANNs) to identify the best algorithm for predicting the developed forces. The best ANN was tested on three numerical models and a prototype with a combination of parameters not included in the dataset, comparing predicted and numerical responses. The results demonstrate the effectiveness of ML techniques in predicting the behavior of MKMs while offering flexibility for integrating additional parameters. Therefore, this paper highlights the potential of ML approaches in the mechanical design of MKM according to the field of use and application. Full article

Pneumatic artificial muscles (PAMs) consist of an elastomeric bladder wrapped in a helical braid. When inflated, PAMs expand radially and contract axially, producing large axial forces. PAMs are advantageous because of their high specific work and specific power, as well as their ability to produce large axial displacements. The axial and radial behavior of PAMs have been well studied. The torsional response of PAMs have not been explored before. Accurate prediction of the torsional force was desired for use in a bio-inspired worm-like robot capable of using an auger mounted to a PAM to bore out tunnels. Thus, an understanding of torsional response was a key objective. Modeling of the torsional response was performed using a force balance approach, and multiple model variations were considered, such as St. Venant’s torsion, bladder buckling, and asymmetrical braid loading. Torsional testing was performed to validate the model using a custom torsional testing system. Data from the tests was compared to the predicted torsional response. Full article

Pneumatic artificial muscles (PAMs) are flexible actuators that can be contracted or expanded by applying air pressure. They are used in robotics, prosthetics, and other applications requiring flexible and compliant actuation. PAMs are basically designed to mimic the function of biological muscles, providing a high force-to-weight ratio and smooth, lifelike movement. Inflation and deflation of these muscles can be controlled rapidly, allowing for fast actuation. In this work, a continuum mechanics-based model is developed to predict the output parameters of PAMs, like actuation force. Comparison of the model results with experimental data shows that the model efficiently predicts the mechanical behaviour of PAMs. Using the actuation force–air pressure–contraction relation provided by the proposed mechanical model, a dynamic model is derived for a multi-link PAM-actuated robot manipulator. An adaptive fixed-time fast terminal sliding mode control is proposed to track the desired joint position trajectories despite the model uncertainties and external disturbances with unknown magnitude bounds. Furthermore, the performance of the proposed controller is compared with an adaptive backstepping fast terminal sliding mode controller through numerical simulations. The simulations show faster convergence and more precise tracking for the proposed controller. Full article

The widespread application of exoskeletons driven by soft actuators in motion assistance and medical rehabilitation has proven effective for patients who struggle with precise object grasping and suffer from insufficient hand strength due to strokes or other conditions. Repetitive passive flexion/extension exercises and active grasp training are known to aid in the restoration of motor nerve function. However, conventional pneumatic artificial muscles (PAMs) used for hand rehabilitation typically allow for bending in only one direction, thereby limiting multi-degree-of-freedom movements. Moreover, establishing precise models for PAMs is challenging, making accurate control difficult to achieve. To address these challenges, we explored the design and fabrication of a bidirectionally bending PAM. The design parameters were optimized based on actual rehabilitation needs and a finite element analysis. Additionally, a dynamic model for the PAM was established using elastic strain energy and the Lagrange equation. Building on this, an adaptive position control method employing a radial basis function neural network, optimized for parameters and hidden layer nodes, was developed to enhance the accuracy of these soft PAMs in assisting patients with hand grasping. Finally, a wearable soft hand rehabilitation exoskeleton was designed, offering two modes, passive training and active grasp, aimed at helping patients regain their grasp ability. Full article

The parallel driving soft manipulator with multiple extensors and contractile pneumatic artificial muscles (PAMs) is able to operate continuously and has varying stiffness, achieving smooth movements and a fundamental trade-off between flexibility and stiffness. Owing to the hysteresis of PAMs and actuator couplings, the manipulator outputs display coupled hysteresis behaviors with stiffness dependence, causing significant positioning errors. For precise positioning control, this paper takes the lead in proposing a comprehensive model aimed at accurately predicting the coupled hysteresis behavior with the stiffness dependence of the soft manipulator. The model consists of an inherent hysteresis submodule, an actuator coupling submodule, and a stiffness-dependent submodule in series. The asymmetrical hysteresis nonlinearity of the PAM is established by the generalized Prandtl–Ishlinskii model in the inherent hysteresis submodule. The serial actuator coupling submodule is dedicated to modeling the actuator couplings, and the stiffness-dependent submodule is implemented with a fuzzy neural network to characterize the stiffness dependence and other system nonlinearities. In addition, an inverse compensator on the basis of the proposed model is conducted. Experiments demonstrate that this model possesses high accuracy and good generalization, and its compensator is effective in decoupling and mitigating hysteresis coupling of the manipulator. The proposed model and control methods significantly improve the positioning accuracy of the pneumatic soft manipulator. Full article