Pneumatic Artificial Muscle Actuator Research Articles

Dynamic Modeling and Motion Control of a Cable-Driven Robotic Exoskeleton With Pneumatic Artificial Muscle Actuators

This paper presents the design, dynamic modeling and motion control of a novel cable-driven upper limb robotic exoskeleton for a rehabilitation exercising. The proposed four degree-of-freedom robotic exoskeleton, actuated by pneumatic artificial muscle actuators, is characterized by a safe, compact, and lightweight structure, complying with the motion of an upper limb as close as possible. In order to perform a passive rehabilitation exercise, the dynamic models were developed by the Lagrange formulation in terms of quasi coordinates combined with the virtual work principle, and then the adaptive fuzzy sliding mode control was designed for the rehabilitation trajectory control. Finally, rehabilitation experiments were conducted to validate the prototype of upper limb robotic exoskeleton and the controller design.

Stable Control of Force, Position, and Stiffness for Robot Joints Powered via Pneumatic Muscles

This paper proposes a novel controller framework for antagonistically driven pneumatic artificial muscle (PAM) actuators. The proposed controller can be stably configured in both torque-stiffness control and position-stiffness control modes. Three contributions are sequentially presented in constructing the framework: 1) A PAM force feedback controller with guaranteed stability is synthesized in a way so as to contend with nonlinear PAM characteristics; 2) a mathematical tool is developed to compute reference PAM forces, for a given set of desired joint torque and joint stiffness inputs; and 3) on top of the torque controller, a position control scheme is implemented and its stability analysis is given in the sense of Lyapunov. In order to test the controller framework, an extensive set of experiments are conducted using an actuator that is constructed using two antagonistically coupled PAMs. As a result, the actuator exhibits satisfactory tracking performances concerning both torque–stiffness control and position–stiffness control modes.

A bundled staggering patterned pneumatic muscle actuator for improved working efficiency

Recently many new actuators have been developed in order to overcome the limitations of conventional actuators. The pneumatic artificial muscle actuator (PMA) is one of the typical examples. Although many studies have been conducted on the single PMA, there are still many limitations of the single PMA operation. The typical problems are low efficiency and limited force output. In order to overcome these limitations, the parallel band PMA (PPMA) consisting of a bundle of PMAs has been developed instead of the single PMA. However, due to the simple actuator driving principle, the parallel PMA causes another problem such as an interference between each PMAs placed side by side. In this paper, we proposed a new bundled staggering patterned pneumatic artificial muscle actuator (SPMA) to resolve the interference problem. The SPMA consists of a bundle of staggered pattern actuators that ensures the large actuation force with a small form factor. To verify the performance of the proposed SPMA, output force and operating range have been compared to those of the existing PPMA by conducting simulation and experiments.

Dynamic analysis of pneumatic artificial muscle (PAM) actuator for rehabilitation with principal parametric resonance condition

In this present work, the dynamic behavior of a pneumatic artificial muscle (PAM) has been studied by varying different muscle parameters and air pressure into it. The governing equation of motion has been derived using Newton’s law of motion to study the various responses in the system at principal parametric resonance condition. The temporal equation of motion contains various nonlinear parameters with forced and nonlinear parametric excitation. Then, the second-order method of multiple scales is used to find the approximate solutions and to study the dynamic stability and bifurcations of the system. The results are found to be in good agreement with the solutions obtained by solving the temporal equation of motion numerically. The instability regions by varying different system parameters have been plotted. The time responses and phase portraits have been plotted to study the system behavior with the nonlinearity. The influences of the different system parameters in the amplitude for the muscle have also been studied with the help frequency responses. In order to verify the solution, the basin of attraction has also been plotted. The obtained results will be very useful for designing the desired PAM use in different rehabilitation robotics and exoskeleton system.

Force reflection in a pneumatic artificial muscle actuated haptic system

This paper presents a systematic technique guideline for a haptic-enabled device driven by a pneumatic artificial muscle (PAM) actuator. The proposed haptic system that can serve as a simulator or rehabilitation device was built with a steel string fixed at one end and the other end connected to a control stick. While operating the control stick, a force reflection to the human user can be experienced by human tactile sensation or kinesthetic sensation. To improve the realism presence of the haptic force, three haptic control architectures have been considered for the force feedback features. The corresponding adaptive fuzzy sliding mode controllers were then designed to compensate for the structure and parametric uncertainties to enhance the fidelity feeling of the reflection force. Maneuver tests were conducted for the proposed haptic architectures and controllers. Comparisons were made through the force tracking results.

Nonlinear dynamics of a parametrically excited pneumatic artificial muscle (PAM) actuator with simultaneous resonance condition

In this present work, the pneumatic artificial muscle is modelled as a single degree of freedom system and the governing nonlinear equation of motion has been derived to study the responses of the system under simultaneous simple and principal parametric resonance conditions. The equation has been developed considering the pneumatic muscle force as a function of the static and the time varying pressure, dimensions and material properties of the artificial muscle. The second order method of multiple scales has been used to find the approximate solutions and to study the dynamic stability and bifurcations of the system. The influences of the various system parameters in the amplitude for the muscle have also been studied with the help time responses, regions of parametric instability and frequency responses. The applications of the present study are illustrated through numerical examples. This work will help the designer or researchers working in this field to determine the safe operating range of the system parameters for different applications of pneumatic artificial muscle.

Fabrication, Characterization and Control of Knit-Covered Pneumatic Artificial Muscle

This paper presents the design, fabrication, characterization, and validation of three kinds of knit-covered pneumatic artificial muscle (called k-PAM) actuators, in which three different knits are made by braiding silver-plated (conductive) yarn and non-conductive yarns with different stitch methods. After a successful characterization process, these k-PAM actuators are able to provide feedback information on actuator length corresponding to the applied pressure. A complete fabrication method is proposed to make the actuator work for higher pressures (up to 300 kPa or more). Furthermore, since the force generated by the actuator is decoupled from the external force, ultimately, it can be directly used to measure both the length and the force. This paper contains the detailed characterization processes, the experimental validations of the sensing ability, and the several characteristics of the k-PAM actuators. It is expected that the k-PAM actuator can be used directly for robotic applications in higher pressure conditions, while the semi-permanent conductive knit cover provides the actuator with durability in highly repetitive situations.

Chordwise implementation of pneumatic artificial muscles to actuate a trailing edge flap**Portions of this paper were presented at the AHS 70th Annual Forum, Montréal, Québec, Canada, May 20–22, 2014.

This work describes the theoretical design and experimental validation of a rotorcraft-specific trailing edge flap powered by pneumatic artificial muscle actuators. The actuators in this work are co-located outboard on the rotor blade with the flap and arranged with a chordwise orientation where diameter and length restrictions can severely limit the operating range of the system. Techniques for addressing this configuration, such as introducing a bias contraction and mechanism optimization, are discussed and a numerical optimization is performed for an actuation system sized for implementation on a medium utility helicopter rotor. The optimized design achieves ±10° of deflection at 1/rev, and maintains at least ±2° half peak-to-peak deflection out to 10/rev, indicating that the system has the actuation authority and bandwidth necessary for both primary control and vibration/noise reduction.

Development of anthropomorphic robotic hand driven by Pneumatic Artificial Muscles for robotic applications

This paper presents the design and fabrication of a three-fingered anthropomorphic robotic hand. The fingers are driven by tendons and actuated by human muscle-like actuators known as Pneumatic Artificial Muscle (PAM). The proposed design allows the actuators to be mounted outside the hand where each finger can be driven by one PAM actuator and six indirectly interlinked tendons. With this design, the three-fingered hand has a compact size and a lightweight with a mass of 150.25 grams imitating the human being hand in terms of size and weight. The hand also successfully grasped objects with different shapes and weights up to 500 g. Even though the number of PAM actuators equals the number of Degrees of Freedom (DOF), the design guarantees driving of three joints by only one actuator reducing the number of required actuators from 3 to 1. Therefore, this hand is suitable for researches of robotic applications in terms of design, cost and ability to be equipped with several types of sensors.

Assist-as-Needed Control of a Robotic Orthosis Actuated by Pneumatic Artificial Muscle for Gait Rehabilitation

Rehabilitation robots are designed to help patients improve their recovery from injury by supporting them to perform repetitive and systematic training sessions. These robots are not only able to guide the subjects’ lower-limb to a designate trajectory, but also estimate their disability and adapt the compliance accordingly. In this research, a new control strategy for a high compliant lower-limb rehabilitation orthosis system named AIRGAIT is developed. The AIRGAIT orthosis is powered by pneumatic artificial muscle actuators. The trajectory tracking controller based on a modified computed torque control which employs a fractional derivative is proposed for the tracking purpose. In addition, a new method is proposed for compliance control of the robotic orthosis which results in the successful implementation of the assist-as-needed training strategy. Finally, various subject-based experiments are carried out to verify the effectiveness of the developed control system.

A nested pneumatic muscle arrangement for amplified stroke and force behavior

This article presents a compact nested architecture to amplify the displacement and forces of pneumatic artificial muscles for potential use in human assistive devices and other robotic applications. The nested architecture consists of several levels in series, and each level is made up of contracting pneumatic muscles, passive force transfer members, and additively manufactured interconnects. The stroke obtained from the nested pneumatic artificial muscle architecture is not always beneficial and is limited by the length-dependent behavior of pneumatic artificial muscles and other practical manufacturing constraints such as the size of the interconnects. Thus, this article studies the effect of the pneumatic artificial muscle length on its stroke using a modified constrained volume maximization formulation, which predicts the actual shape of the deformed pneumatic artificial muscle, and models additional stiffness due to membrane bending. Using this formulation, a framework is presented to optimally design the number of nested levels and individual actuators in each level to obtain a required stroke. Such a system is designed to actuate the human elbow by an angle of 80°, where almost 40% contraction is obtained using custom-manufactured pneumatic artificial muscles inherently capable of contracting upto 17% of its length. The framework can be used to amplify the stroke and forces of any pneumatic artificial muscle actuator and adapt it to different application requirements.

A novel adaptive feed-forward-PID controller of a SCARA parallel robot using pneumatic artificial muscle actuator based on neural network and modified differential evolution algorithm

This paper proposes a novel control system combining adaptively feed-forward neural controller and PID controller to control the joint-angle position of the SCARA parallel robot using the pneumatic artificial muscle (PAM) actuator. Firstly, the proposed inverse neural NARX (INN) model dynamically identifies all nonlinear features of the SCARA parallel PAM robot. Parameters of the inverse neural NARX model are optimized with the modified differential evolution (MDE) algorithm. Secondly, combining the inverse neural NARX model that provides a feed-forward control value from the desired joint position and the conventional PID controller applied to improve the precision and reject the steady state error in the joint position control. Finally, the new adaptive back-propagation (aBP) algorithm, based on Sugeno fuzzy system, proposed for online updating the weight values of the inverse neural NARX model as to adapt well to the disturbances and dynamic variations in its operation. Experimental tests confirmed the performance and merits of the proposed control scheme in comparison with the traditional control methods.

Adaptive evolutionary neural control of perturbed nonlinear serial PAM robot

This paper proposes a novel adaptive joint position control system for a highly nonlinear SCARA serial robot using the pneumatic artificial muscle (PAM) actuator. First the new inverse and forward neural NARX (IFNN) models are proposed as to dynamically identify all nonlinear and hysteresis features of the SCARA serial PAM-based robot. Parameters of the new IFNN model are optimized by the modified differential evolution (MDE) algorithm. Secondly, the new IFNN model is applied in the novel proposed adaptive evolutionary neural IFNN-IMC controller that is applied to improve the precision and to reject the steady-state error in the joint position SCARA serial robot control. Finally, the novel adaptive back-propagation (ABP) algorithm based on fuzzy reasoning is applied for online updating the weight values of the IFNN model which helps the novel proposed adaptive evolutionary neural IFNN-IMC controller adapt well to external disturbances and dynamic variations in its operation. Experimental tests confirmed the performance and advantages of the proposed control scheme in comparison with other nonlinear control methods.

Practical controller design for ultra-precision positioning of stages with a pneumatic artificial muscle actuator

This paper presents a practical controller design method for ultra-precision positioning of pneumatic artificial muscle actuator stages. Pneumatic artificial muscle (PAM) actuators are safe to use and have numerous advantages which have brought these actuators to wide applications. However, PAM exhibits strong non-linear characteristics, and these limitations lead to low controllability and limit its application. In practice, the non-linear characteristics of PAM mechanism are difficult to be precisely modeled, and time consuming to model them accurately. The purpose of the present study is to clarify a practical controller design method that emphasizes a simple design procedure that does not acquire plants parameters modeling, and yet is able to demonstrate ultra-precision positioning performance for a PAM driven stage. The practical control approach adopts continuous motion nominal characteristic trajectory following (CM NCTF) control as the feedback controller. The constructed PAM driven stage is in low damping characteristic and causes severe residual vibration that deteriorates motion accuracy of the system. Therefore, the idea to increase the damping characteristic by having an acceleration feedback compensation to the plant has been proposed. The effectiveness of the proposed controller was verified experimentally and compared with a classical PI controller in point-to-point motion. The experiment results proved that the CM NCTF controller demonstrates better positioning performance in smaller motion error than the PI controller. Overall, the CM NCTF controller has successfully to reduce motion error to 3µm, which is 88.7% smaller than the PI controller.

A constrained maximization formulation to analyze deformation of fiber reinforced elastomeric actuators

Fiber reinforced elastomeric enclosures (FREEs) are soft and smart pneumatic actuators that deform in a predetermined fashion upon inflation. This paper analyzes the deformation behavior of FREEs by formulating a simple calculus of variations problem that involves constrained maximization of the enclosed volume. The model accurately captures the deformed shape for FREEs with any general fiber angle orientation, and its relation with actuation pressure, material properties and applied load. First, the accuracy of the model is verified with existing literature and experiments for the popular McKibben pneumatic artificial muscle actuator with two equal and opposite families of helically wrapped fibers. Then, the model is used to predict and experimentally validate the deformation behavior of novel rotating-contracting FREEs, for which no prior literature exist. The generality of the model enables conceptualization of novel FREEs whose fiber orientations vary arbitrarily along the geometry. Furthermore, the model is deemed to be useful in the design synthesis of fiber reinforced elastomeric actuators for general axisymmetric desired motion and output force requirement.

An online gain tuning proxy-based sliding mode control using neural network for a gait training robotic orthosis

In the last decades, wearable powered orthoses have been introduced in the state of the art for lower-limb rehabilitation. Most of these applications are driven by electric motors. Comparing with electric motor actuators, pneumatic artificial muscle (PAM) actuators are compliant because of the elasticity of PAMs. Consequently, for more safety and comfort of lower-limb rehabilitation, a compliant robotic orthosis powered by PAMs is developed. Based on safe control, a new control method, proxy-based sliding mode control (PSMC), was introduced into rehabilitation robotics a few years ago. It combined safety and accuracy of tracking to make it suitable for the safe control of PAM actuators. As the reason of low frequency response of PAM actuators and variable loads caused by different human subjects, the fixed parameters of PSMC makes the tracking performance vary from subject to subject, and lacks robustness. This paper presents a modification of PSMC by using neural network to tune PSMC gains online, and implements both PSMC and modified PSMC control schemes in the robotic orthosis. Experimental results demonstrate that the improved PSMC method performs better on tracking with little degradation on safety for different loads and human subjects.

An EMG-Driven Weight Support System With Pneumatic Artificial Muscles

In this paper, we introduce our newly developed biosignal-based vertical weight support system that is composed of pneumatic artificial muscles (PAMs) and an electromyography (EMG) measurement device. By using our developed weight support system, assist force can be varied based on measured muscle activities; most existing systems can only generate constant assist forces. In this paper, we estimated knee and ankle joint torques from measured EMGs using floating base inverse dynamics. Knee and ankle joint estimated torques are converted to vertical forces by the kinematic model of a subject. The converted vertical forces are used as force inputs for the PAM actuator system. To validate our system's control performance, four healthy subjects performed a one-leg squat with his left leg while his right leg was assisted by our proposed system. We used the vertical force estimated from the measured EMG signals as a control input to the weight support system. We compared EMG magnitudes with four different experimental conditions: 1) normal two-leg squat; 2) one-leg squat without the assist system; 3) one-leg squat with EMG-based weight support; and 4) one-leg squat with constant force support. The EMG magnitude with the proposed weight support system was much closer to that with normal two-leg squat than that with one-leg squat without the assist system and than that with one-leg squat with constant force support.

Experimental Validation for Fuzzy Control of Servo Pneumatic Artificial Muscle Driven by Metal Hydride

This paper presents the servo control problem of pneumatic artificial muscle (PAM) powered by hydrogen-based metal hydride (MH) for mechanical servo actuation. Under various operations of a servo PAM actuator driven by hydrogen-based MH, the desired task of actuator is changed following those various operations. A fuzzy adaptive proportional-integral-derivative (PID) controller is proposed to improve the dynamic performance requirements in both transient and steady-state responses. The fuzzy logic part of the proposed controller is employed to be better than a conventional PID for automatic online tuning of the parameters of PID gain under different operating conditions. The nonlinear mathematical model of PAM actuated by hydrogen-based MH is determined as the desired characteristics of closed-loop control for a fuzzy rule base. Position control is examined to illustrate the performance of the proposed controller. The results show that the proposed technique is more suitable and effective in the case of servo actuation.

Nonlinear Control of Robotic Manipulators Driven by Pneumatic Artificial Muscles

Lightweight, compliant actuators are particularly desirable in safety-conscious robotic systems intended for interaction with humans. Pneumatic artificial muscles (PAMs) exhibit these characteristics and are capable of higher specific work than comparably sized hydraulic actuators and electric motors. However, control of PAM-actuated systems has proven difficult due to the highly nonlinear nature of the actuators and the pneumatic systems driving their actuation. This study develops and investigates the performance of three advanced control strategies—sliding mode control, adaptive sliding mode control, and adaptive neural network (ANN) control—each containing a distinct level of a priori model knowledge, to enable smooth and accurate motion tracking of a single degree-of-freedom PAM-actuated manipulator. Originally developed by J.-J. Slotine and R.M. Sanner, the specific controllers employed in this study are significantly modified for application to pneumatically actuated open-chain manipulators with complex nonlinear dynamics. The two adaptive controllers are updated online and require no pretraining step. Several experiments are performed with each controller to evaluate and compare closed-loop tracking performance. Results highlight the dependence of a preferred control strategy on the level of model completeness and quality, and suggest that in most PAM-actuated manipulator scenarios, the ANN controller is preferable because it does not require a model of the pneumatic system or joint mechanism design, which can be difficult and time consuming to characterize, and is robust to changes in PAM actuator characteristics (due to fatigue or replacement).

Modeling of pneumatic artificial muscle using a hybrid artificial neural network approach

Pneumatic Artificial Muscle (PAM) actuator has been widely used in medical and rehabilitation robots, owing to its high power-to-weight ratio and inherent safety characteristics. However, the PAM exhibits highly non-linear and time variant behavior, due to compressibility of air, use of elastic-viscous material as core tube and pantographic motion of the PAM outer sheath. It is difficult to obtain a precise model using analytical modeling methods. This paper proposes a new Artificial Neural Network (ANN) based modeling approach for modeling PAM actuator. To obtain higher precision ANN model, three different approaches, namely, Back Propagation (BP) algorithm, Genetic Algorithm (GA) approach and hybrid approach combing BP algorithm with Modified Genetic Algorithm (MGA) are developed to optimize ANN parameters. Results show that the ANN model using the GA approach outperforms the BP algorithm, and the hybrid approach shows the best performance among the three approaches.