Search

Due to the merits of high flexibility, large driving force, and light weight, the pneumatic artificial muscle (PAM) has been widely applied in robot. However, the inherent asymmetric hysteresis nonlinear characteristics affect the control performance of PAM seriously. This paper presents an improved generalized Prandtl–Ishlinskii (GPI-M) model for the asymmetric hysteresis characterization of PAM, which adopts the simplified Magic Formula (SMF) as the envelop function instead of hyperbolic tangent function (GPI) or arc tangent function (GPI-A). With regard to the effect of parameters identification, the Levenberg-Marquardt (LM) algorithm, genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and differential evolution (DE) algorithm are studied, which shows the LM algorithm is far superior to the other three intelligent algorithms in terms of computational efficiency and identification accuracy. Furthermore, a crosswise comparative study on three models and two envelop functions investigating modeling accuracy reveals that the newly introduced GPI-M model exhibits better performance than the modified symmetric generalized Prandtl-Ishlinskii (MSGPI) model and the GPI-A model presented previously. Meanwhile, the influence of the operator numbers on the modeling accuracy of three models is discussed, which finds that the GPI-M model possess the best accuracy with the least operator numbers. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

Soft Actuator Based on a Novel Variable Stiffness Compound Extensor Bending-Pneumatic Artificial Muscle (CEB-PAM): Design and Mathematical Model [ABSTRACT FROM AUTHOR]

With the excellent characteristic of intrinsic compliance, pneumatic artificial muscle can improve the interaction comfort of wearable robotic devices. This paper resolves the safety tracking control problem of a pneumatically actuated lower limb exoskeleton system. A single-parameter adaptive fuzzy control strategy is proposed with high control precision and full state constraints for the safe gait training tasks. Based on the barrier Lyapunov function, all signals in the closed-loop system can be bounded in finite time, which guarantees the deviation of the exoskeleton's moving trajectory within a bounded range. Furthermore, with the proposed single-parameter adaptive law, the computational burden and the complexity of the control are reduced significantly. Finally, numerical simulations, no-load tracking experiments, and passive and active gait training experiments with healthy subjects validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper describes the design, construction and experimental testing of a single-joint manipulator arm actuated by pneumatic artificial muscles (PAMs) for the tasks of transporting and sorting work pieces. An antagonistic muscle pair is used in a rotational sense to produce a required torque on a pulley. The concept, operating principle and elementary properties of pneumatic muscle actuators are explained. Different conceptions of the system realizations are analyzed using the morphological-matrix conceptual design framework and top-rated solution was practically realized. A simplified, control-oriented mathematical model of the manipulator arm driven by PAMs and controlled with a proportional control valve is derived. The model is then used for a controller design process. Fluidic muscles have great potential for industrial applications and assembly automation to actuate new types of robots and manipulators. Their characteristics, such as compactness, high strength, high power-to-weight ratio, inherent safety and simplicity, are worthy features for advanced manipulation systems. The experiments were carried out on a practically realized manipulator actuated by a pair of muscle actuators set into an antagonistic configuration. The setup also includes an original solution for the subsystem to add work pieces in the working space of the manipulator. [ABSTRACT FROM AUTHOR]

This article proposes a mobile robot that is fully constructed and actuated by utilising a pneumatic artificial muscle (PAM). Several features are shown in this design including softness, lightweight, and shape change. The robot that has been designed in this article is constructed with four identical contraction actuators: one pair for steering and moving forward and the second pair for shape-shifting. The proposed mobile robot has the ability to move forward, steer, and shapeshift to navigate through narrow paths. The kinematic for the system is provided for the area of the robot by using the shoelace formula by utilising a Pixy camera to track the coordinates of the four vertices. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this article, an interval type-2 fuzzy disturbance observer (IT2FDO) is proposed for the trajectory tracking control of a flexible joint actuated by a pneumatic artificial muscle and a torsion spring. Interval type-2 fuzzy sets are introduced into the fuzzy disturbance observer (FDO) to enhance the fuzzy approximation capability. A novel adaptive law that updates all the parameters of the FDO, including the control representative values, the center vectors, and the width vectors, is developed. A new control strategy incorporating the proposed IT2FDO and Takagi–Sugeno (T–S) fuzzy model-based control is designed for the trajectory tracking task. With the newly designed adaptive law, the stability of the IT2FDO and that of the closed-loop system are guaranteed. Simulation studies verify the effectiveness of the proposed IT2FDO, and real-world experiments demonstrate the robustness of the proposed control strategy to the external disturbance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper presents a new trajectory tracking control strategy for a novel lower-limb rehabilitant robot, AirGait, which is a parallel mechanism with three degrees of freedom and is actuated with four pneumatic artificial muscles (PAMs). Compared with the existing control methods, the feature of this approach is that it combines the feedforward/feedback (FF) controller based on the length-pressure hysteresis compensation of PAMs with a joint space control method on the trajectory tracking control of a parallel mechanism. For the controller design, the inverse and forward kinematic formulas of AirGait are developed firstly based on the structure analysis. Then, a lower-limb kinematic model of humans is developed and the human-like trajectory of time is obtained by using a Fourier series method. Finally, the control scheme is introduced and the trajectory tracking control of AirGait with two reference input signals is presented. The comparative experimental results indicate that the performance of this control approach is strong and this robot holds great promise for assisting lower-limb locomotion.

This article proposes a mobile robot that is fully constructed and actuated by utilising a pneumatic artificial muscle (PAM). Several features are shown in this design including softness, lightweight, and shape change. The robot that has been designed in this article is constructed with four identical contraction actuators: one pair for steering and moving forward and the second pair for shape-shifting. The proposed mobile robot has the ability to move forward, steer, and shapeshift to navigate through narrow paths. The kinematic for the system is provided for the area of the robot by using the shoelace formula by utilising a Pixy camera to track the coordinates of the four vertices.

This article describes the dynamics of a manipulator with two degrees of freedom, while the dynamic model of the manipulator's arm is derived using Lagrangian formalism, which considers the difference between the kinetic and potential energy of the system. The compiled dynamic model was implemented in Matlab, taking into account the physical parameters of the manipulator and friction term. Physical parameters were exported from the 3D CAD model. A scheme (model) was compiled in the Simulink, which was used for the subsequent validation process. The outputs of the validations were compared with measured data of joint angles from the system (expected condition) obtained by using gravity tests. For obtaining better results were parameters of the model optimizing by using the Trust Region Algorithm for Nonlinear Least Squares optimization method. Therefore, the aim of the research described in the article is the comparison of the model with the parameters that come from CAD and its improvement by estimating the parameters based on gravitational measurements. The model with estimated parameters achieved an improvement in the results of the Normal Root Mean Square Error compared to the model with CAD parameters. For link 1 was an improvement from 28.49% to 67.93% depending on the initial joint angle, and for link 2, from 63.84% to 66.46%. [ABSTRACT FROM AUTHOR]

Pneumatic artificial muscles (PAMs) have been widely used in actuation of medical devices due to their intrinsic compliance and high power-to-weight ratio features. However, the nonlinearity and time-varying nature of PAMs make it challenging to maintain high-performance tracking control. In this article, a high-order pseudopartial derivative-based model-free adaptive iterative learning controller (HOPPD-MFAILC) is proposed to achieve fast convergence speed. The dynamics of PAM is converted into a dynamic linearization model during iterations; meanwhile, a high-order estimation algorithm is designed to estimate the pseudopartial derivative component of the linearization model by only utilizing the input and output data in previous iterations. The stability and convergence performance of the controller are verified through theoretical analysis. Simulation and experimental results on PAM demonstrate that the proposed HOPPD-MFAILC can track the desired trajectory with improved convergence and tracking performance. [ABSTRACT FROM AUTHOR]

This paper presents a simple and fast design and implementation for a soft robot arm. The proposed continuum arm has been built by a single self-bending contraction actuator (SBCA) with two-fingers soft gripper. Because of the valuable advantages of the pneumatic artificial muscle (PAM), this continuum arm provides a high degree of safety to individuals. The proposed soft robot arm has a bending behaviour of more 180° at 3.5 kg, while, its weight is 0.7 kg. Moreover, it is designed to assist the people by reducing the number of backbends and that leads to a decrease in the possibility of lower back pain. [ABSTRACT FROM AUTHOR]

The biped robot with heterogeneous legs (BRHL) greatly facilitates the development of intelligent lower-limb prosthesis (ILLP). In the BRHL, the remaining leg of the amputee is simulated by an artificial leg, which provides the bionic leg with the precise gait following trajectory. Therefore, the artificial leg must closely mimic the features of the human leg. After analyzing the motion mechanism of the human knee, this paper designs a four-link bionic knee in light of the coexistence of rolling and sliding between the femur, the meniscus and the tibia. Drawing on the driving mechanism of leg muscles, two pneumatic artificial muscles (PAMs) were adopted to serve as the extensor and flexor muscles on the thigh. The two PAMs move in opposite direction, driving the knee motions in the artificial leg. To overcome the complexity of traditional PAM modelling methods, the author set up a PAM feature test platform to disclose the features of the PAMs, and built static and dynamic nonlinear mathematical models of the PAMs based on the test data. Next, a proportional-integral-derivative (PID) closed loop controller and sliding mode controller was designed for the bionic knee, referring to the kinetics equation of the knee. Through experimental simulation, it is confirmed that the proposed controller can accurately control the position of the four-link bionic knee, and that the designed bionic knee and PAM driving mode are both correct. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Highlights • Nonlinear dynamics of a pneumatically actuated artificial muscle is investigated. • The system is subjected to simultaneous simple and principal parametric resonances. • The critical system parameters are determined by stability and bifurcation analysis. • Safe operating range of the system parameters are found for various applications. Abstract 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. [ABSTRACT FROM AUTHOR]

In this paper, a new adaptive fuzzy sliding mode controller (AFSMC) is proposed for single-input single-output (SISO) nonlinear systems with uncertainties and external disturbances. An adaptive fuzzy model is used to approximate the unknown and uncertain features of a nonlinear system. Furthermore, the fuzzy model parameters are optimally identified with a differential evolution algorithm. The novel AFSMC algorithm is designed using sliding mode control. The adaptive fuzzy law is adaptively generated with constraints based on Lyapunov stability theory to guarantee the asymptotic stability of the closed-loop nonlinear uncertain SISO system. Experimental results are presented to demonstrate that the proposed AFSMC provides a robust and simple approach to effectively control the highly nonlinear uncertain SISO systems. [ABSTRACT FROM AUTHOR]

This article describes the dynamics of a manipulator with two degrees of freedom, while the dynamic model of the manipulator’s arm is derived using Lagrangian formalism, which considers the difference between the kinetic and potential energy of the system. The compiled dynamic model was implemented in Matlab, taking into account the physical parameters of the manipulator and friction term. Physical parameters were exported from the 3D CAD model. A scheme (model) was compiled in the Simulink, which was used for the subsequent validation process. The outputs of the validations were compared with measured data of joint angles from the system (expected condition) obtained by using gravity tests. For obtaining better results were parameters of the model optimizing by using the Trust Region Algorithm for Nonlinear Least Squares optimization method. Therefore, the aim of the research described in the article is the comparison of the model with the parameters that come from CAD and its improvement by estimating the parameters based on gravitational measurements. The model with estimated parameters achieved an improvement in the results of the Normal Root Mean Square Error compared to the model with CAD parameters. For link 1 was an improvement from 28.49% to 67.93% depending on the initial joint angle, and for link 2, from 63.84% to 66.46%.

Highlights • Pneumatic artificial muscle-based devices can be used safely in the MRI setting. • They do not induce any significant decrease in the SNR of fMRI data. • Such devices are suitable to generate passive movements of different body parts. • They allow to map the contralateral primary sensorimotor cortex in healthy subjects. • The increase in BOLD signal is lower as compared to analogous active movements. Abstract Background Two major concerns with respect to task-based motor functional magnetic resonance imaging (fMRI) are inadequate participants' performance as well as intra- and inter-subject variability in execution of the motor action. New method This study validates the use of an MRI-compatible stimulator based on a pneumatic artificial muscle (PAM) for block-design fMRI mapping of the primary sensorimotor (SM1) cortex in a series of fifteen right-handed healthy subjects. The PAM stimulator elicits computer-controlled timely and reproducible passive movements of fingers/toes. Participants performed comparable active and passive PAM-induced flexion-extensions of the index fingers. Results Passive movement of the right index finger and passive alternating right and left index finger movement resulted in a significant increase in blood-oxygen-level-dependent (BOLD) signal in contralateral SM1 cortex in 14/15 and 15/15 subjects respectively. Similar networks were recruited by active and passive index finger movements. However, at the group level, active movement induced significantly higher increases in BOLD signal than passive movement in contralateral SM1 cortex (p < 0.05 Family Wise Error [FWE] corrected), supplementary motor area (p < 0.001 uncorrected), ipsilateral cerebellum (p < 0.001 uncorrected), and bilateral putamina (p < 0.001 uncorrected). Comparison with existing method(s) As compared to the several MRI-compatible robotic devices for computer-controlled passive movement of the fingers that were introduced in the past decades, the proposed PAM-based stimulator is smaller, handier, and easier to use in the MRI setting. Conclusions PAM-based stimulators can be reliably used for passive sensorimotor fMRI mapping in healthy subjects. Using this approach, bilateral SM1 cortices can be mapped accurately during a single 6-min block-design fMRI protocol. [ABSTRACT FROM AUTHOR]

In this paper dynamics of an artificial muscle (PAM) - mass system is investigated. During shortening or lengthening of the PAM the position of the mass connected with PAM is varying. The aim of the paper is to consider the positioning of mass. The force produced by PAM is experimentally investigated and mathematically described. In the paper the influence of the pressure in the PAM on the stability of positioning of the mass is investigated. The steady state solution is given in the form of Lambert W-function. In the paper analytical procedure for solving differential equation of motion of PAM – mass system is introduced. The solution of the nonlinear differential equation is assumed to be periodic with time variable amplitude and phase. In the paper a numerical example is considered. The analytically obtained approximate solution is compared with numerical one. It is concluded that solutions are in good agreement. One of the most important result of investigation is that, based on the analytical results, we are possible to predict the steady state position of the mass. Besides, if the position is known the prediction of PAM's pressure is plausible. [ABSTRACT FROM AUTHOR]

This paper presents a modified generalized Prandtl–Ishlinskii (MGPI) model for the asymmetric hysteresis characterization of pneumatic artificial muscles (PAMs), which can be considered as a cascade of the superposition of weighted generalized play operators and the superposition of weighted dead-zone operators. Compared with the established hysteresis models, the significance of the MGPI model is that it has a simple expression including a small number of parameters to be identified. Besides, the analytical form of its inverse model is easy to be obtained, which can be applied to the compensation of asymmetric hysteresis of the PAM in real-time. The wiping-out and congruency properties of the proposed model are verified by simulations. Meanwhile, by carrying out the experimental study on length–pressure hysteresis of a PAM, the parameters in the MGPI model are identified from measured data using the Levenberg–Marquardt method. Then, a feedforward hysteresis compensator based on the inverse MGPI model is designed for trajectory tracking control of the PAM. The simulation and experimental results indicate that the proposed model and its inversion are effective to characterize and compensate the length–pressure hysteresis of the PAM. [ABSTRACT FROM AUTHOR]

This paper proposes the novel adaptive neural network (ADNN) compliant force/position control algorithm applied to a highly nonlinear serial pneumatic artificial muscle (PAM) robot as to improve its compliant force/position output performance. Based on the new adaptive neural ADNN model which is dynamically identified to adapt well all nonlinear features of the 2-axes serial PAM robot, a new hybrid adaptive neural ADNN-PID controller was initiatively implemented for compliant force/position controlling the serial PAM robot system used as an elbow and wrist rehabilitation robot which is subjected to not only the internal coupled-effects interactions but also the external end-effecter contact force variations (from 10[N] up to critical value 30[N]). The experiment results have proved the feasibility of the new control approach compared with the optimal PID control approach. The novel proposed hybrid adaptive neural ADNN-PID compliant force/position controller successfully guides the upper limb of subject to follow the linear and circular trajectories under different variable end-effecter contact force levels. [ABSTRACT FROM AUTHOR]

A two-joint coupling nonlinear system driven by pneumatic artificial muscles is introduced in this paper. A sliding mode controller with extended state observer is proposed to cope with nonlinearities and disturbances for the two-joint coupling nonlinear system. In addition, convergence of the extended state observer is presented and stability analysis of the closed-loop system is also demonstrated with the sliding mode controller. Lastly, some experiments are carried out to show the reality effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A better understanding of how actuator design supports locomotor function may help develop novel and more functional powered assistive devices or robotic legged systems. Legged robots comprise passive parts (e.g., segments, joints and connections) which are moved in a coordinated manner by actuators. In this study, we propose a novel concept of a hybrid electric-pneumatic actuator (EPA) as an enhanced variable impedance actuator (VIA). EPA is consisted of a pneumatic artificial muscle (PAM) and an electric motor (EM). In contrast to other VIAs, the pneumatic artificial muscle (PAM) within the EPA provides not only adaptable compliance, but also an additional powerful actuator with muscle-like properties, which can be arranged in different combinations (e.g., in series or parallel) to the EM. The novel hybrid actuator shares the advantages of both integrated actuator types combining precise control of EM with compliant energy storage of PAM, which are required for efficient and adjustable locomotion. Experimental and simulation results based on the new dynamic model of PAM support the hypothesis that combination of the two actuators can improve efficiency (energy and peak power) and performance, while does not increase control complexity and weight, considerably. Finally, the experiments on EPA adapted bipedal robot (knee joint of the BioBiped3 robot) show improved efficiency of the actuator at different frequencies. [ABSTRACT FROM AUTHOR]

In this paper, a new approach for modeling the static force characteristic of Festo pneumatic muscle actuators (PMAs) will be presented. The model is physically motivated and therefore gives a deeper understanding of the Festo PMA. After introducing the new model, it will be validated through a comparison to a measured force map of a Festo DMSP-10-250 and a DMSP-20-300, respectively. It will be shown that the error between the new model and the measured data is below 4.4% for the DMSP-10-250 and below 2.35% for the DMSP-20-300. In addition, the quality of the presented model will be compared to the quality of existing models by comparing the maximum error. It can be seen that the newly introduced model is closer to the measured force characteristic of a Festo PMA than any existing model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With outstanding flexibility, pneumatic artificial muscles (PAMs) have shown unique advantages in human–robot interaction environments. However, asymmetric hysteresis nonlinearities, unknown transient or persistent time-varying disturbances, etc., seriously degrade control performance/robustness of PAM arms, and increase actuation failure risks. Meanwhile, existing disturbance rejection methods either rarely handle matched/unmatched disturbances, or require known disturbance dynamics. These defects limit the disturbance rejection ability in practical applications. To this end, a new adaptive output feedback control method is proposed in this article, which achieves efficient positioning and tracking control of PAM arms. Without requiring a priori knowledge of inverse plant models and disturbance upper bounds, the proposed method further enhances robustness against uncertainties and disturbances. Specifically, a generalized Bouc–Wen hysteresis loop is used to describe the asymmetric dynamic force of PAMs. Also, a new adaptive law is designed to compensate for the linearized parameters online. In particular, by applying a modified state observer to derive the equivalent-input-disturbance estimation solution, this article firstly realizes state observation, parameter estimation, and unknown transient/persistent external disturbance rejection simultaneously. The rigorous stability analysis demonstrates that the tracking errors converge to small neighborhoods of the origins. Finally, hardware experimental results with comparisons validate the effectiveness and robustness superiority of the proposed method. • Adaptive motion control with hysteresis compensation of nonlinear PAM arms. • Equivalent input disturbance design to handle matched and unmatched disturbances. • A new adaptive law for linearized hysteresis parameters with error excitation terms. • Convergence analysis for the adaptive and tracking errors at the equilibrium points. • Hardware experiments with comparisons to verify control performance and robustness. [ABSTRACT FROM AUTHOR]

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

Pneumatic artificial muscles (PAMs) are a class of pneumatic drives that have received considerable attention for applications related to bio-inspired robotics. Nevertheless, servo control of PAMs is challenging due to the compressibility and nonlinear flow characteristics of air, hysteresis behaviour as well as uncertainties present. In this paper, positioning of an antagonistically paired PAM with mass flow rate of compressed air regulated by a 5/3-way proportional directional valve and driven by a dynamical adaptive backstepping-sliding mode control (DAB-SMC) scheme is investigated. Implemented for the first time on a PAM-driven actuating system, derivation of this model-based nonlinear control scheme is presented first followed by experimental evaluation. Positioning performance is studied using a sinusoidal trajectory with tracking frequencies of 0.05, 0.1, 0.2 and 0.5 Hz, and a multiple-step polynomial input having step sizes of 0.7°, 1.4°, 2.9° and 5.7°. Over various operating conditions, average root mean square error value of 0.16° and steady-state error value of 0.04° are achieved for position tracking and regulating, respectively. The adaptive LuGre friction observer embedded in the control scheme effectively compensates hysteresis behaviour of the PAMs and helps to improve the performance. The proposed DAB-SMC scheme outperforms the classical SMC scheme by 33% in accuracy. The control scheme has also demonstrated a robust performance towards the uncertainties including loading. In addition, a slight performance compromise in both tracking and regulating tasks was observed, when the 5/3-way proportional valve is replaced by cost-effective 2/2-way pulse width modulation controlled on–off valves. [ABSTRACT FROM AUTHOR]

This paper proposes an advanced position-tracking control approach, referred to as an integrated intelligent nonlinear controller, for a pneumatic artificial muscle (PAM) system. Due to the existence of uncertain, unknown, and nonlinear terms in the system dynamics, it is difficult to derive an exact mathematical model with robust control performance. To overcome this problem, the main contributions of this paper are as follows. To actively represent the behavior of the PAM system using a grey-box model, neural networks are employed as equivalent internal dynamics of the system model and optimized online by a Lyapunov-based method. To realize the control objective by effectively compensating for the estimation error, an advanced robust controller is developed from the integration of the designed networks, and improvement of the sliding mode and backstepping techniques. The convergences of both the developed model and the closed-loop control system are guaranteed by Lyapunov functions. As a result, the overall control approach is capable of ensuring the system's performance with fast response, high accuracy, and robustness. Real-time experiments are carried out in a PAM system under different conditions to validate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Robotic devices intended to assist patients recovering from hip movement disorders face two major problems, namely, difficulty in (1) enhancing self-efforts in gait training by only using a robotic device and (2) reproducing a patient's own coordinated motion during gait training. To solve these problems, the authors have developed a gait training device based on the master-slave system. This device treats the user's healthy limbs as the "master" and the paretic limbs for which the user wishes to achieve normal gait motions as the "slave". An inertial measurement unit is used to detect hip joint angular displacements and the time it takes for each displacement to occur; the hip joint angular displacement on the slave side is controlled by a proportional-integral-derivative (PID) controller. Six healthy persons were equipped with the device and asked to walk along a walkway so that their gait motion and the electromyographic activities in their lower limb muscles could be evaluated. The results of that experiment indicate that gait motion was preserved in the simulated paresis in the subjects' hip muscles, which suggests that the proposed master-slavetype gait training device can enhance users' efforts, and that the PID controller controlling angular displacements of the hip joint is able to reproduce natural gait motions. [ABSTRACT FROM AUTHOR]

In this paper, a new approach for modeling the static force characteristic of Festo pneumatic muscle actuators (PMAs) will be presented. The model is physically motivated and therefore gives a deeper understanding of the Festo PMA. After introducing the new model, it will be validated through a comparison to a measured force map of a Festo DMSP-10-250 and a DMSP-20-300, respectively. It will be shown that the error between the new model and the measured data is below 4.4% for the DMSP-10-250 and below 2.35% for the DMSP-20-300. In addition, the quality of the presented model will be compared to the quality of existing models by comparing the maximum error. It can be seen that the newly introduced model is closer to the measured force characteristic of a Festo PMA than any existing model.

A better understanding of how actuator design supports locomotor function may help develop novel and more functional powered assistive devices or robotic legged systems. Legged robots comprise passive parts (e.g., segments, joints and connections) which are moved in a coordinated manner by actuators. In this study, we propose a novel concept of a hybrid electric-pneumatic actuator (EPA) as an enhanced variable impedance actuator (VIA). EPA is consisted of a pneumatic artificial muscle (PAM) and an electric motor (EM). In contrast to other VIAs, the pneumatic artificial muscle (PAM) within the EPA provides not only adaptable compliance, but also an additional powerful actuator with muscle-like properties, which can be arranged in different combinations (e.g., in series or parallel) to the EM. The novel hybrid actuator shares the advantages of both integrated actuator types combining precise control of EM with compliant energy storage of PAM, which are required for efficient and adjustable locomotion. Experimental and simulation results based on the new dynamic model of PAM support the hypothesis that combination of the two actuators can improve efficiency (energy and peak power) and performance, while does not increase control complexity and weight, considerably. Finally, the experiments on EPA adapted bipedal robot (knee joint of the BioBiped3 robot) show improved efficiency of the actuator at different frequencies.

This paper investigates the orderly recruitment of pneumatic artificial muscles for efficient torque production in a robotic manipulator. Pneumatic artificial muscles (PAMs) are arranged in a parallel bundle, and independently-controlled “motor units” are employed to imitate the structure and function of human skeletal muscle. Simulated cycling tests are conducted on a model of the robotic manipulator to quantify the benefits of variable recruitment, and experimental testing is performed to validate the simulated predictions. Results reveal a distinct relationship between recruitment and system efficiency. Key factors influencing the value of a variable recruitment strategy include nonlinear PAM bladder elasticity, pneumatic losses, and dissipative forces in the robotic joint. Recruitment guidelines are proposed to maximize efficiency over a range of payload masses. The potential challenges associated with maintaining smooth motion control during discrete transitions in recruitment are also identified and discussed. [ABSTRACT FROM PUBLISHER]

In this paper, a novel MIMO Neural NARX model is used for simultaneously modeling and identifying both joints of the 2-axes PAM robot arm's inverse and forward dynamic model. The highly nonlinear cross effect of both links of the 2-axes PAM robot arm are thoroughly modeled through an Inverse and Forward Neural MIMO NARX Model-based identification process using experimental input-output training data. Consequently the proposed Inverse and Forward Neural MIMO NARX model scheme of the nonlinear 2-axes PAM robot arm has been investigated. The results show that the novel Inverse and Forward Neural MIMO NARX Model trained by Back Propagation learning algorithm yields outstanding performance and perfect accuracy. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a detailed study to investigate the possibility of applying the online tuning gain scheduling MIMO neural dynamic DNN-PID control architecture to a nonlinear 2-axes pneumatic artificial muscle (PAM) robot arm so as to improve its joint angle position output performance. The proposed controller was implemented as a subsystem to control the real-time 2-axes PAM robot-arm system so as to control precisely the joint angle position of the 2-axes PAM robot arm when subjected to system internal interactions and load variations. The results of the experiment have demonstrated the feasibility and benefits of the novel proposed control approach in comparison with the traditional PID control strategy. The proposed gain scheduling neural MIMO DNN-PID control scheme forced both joint angle outputs of 2-axes PAM robot arm to track those of the reference simultaneously under changes of the load and system coupled internal interactions. The performance of this novel proposed controller was found to be outperforming in comparison with conventional PID. These results can be applied to control other highly nonlinear systems. [Copyright &y& Elsevier]