Pneumatic Artificial Muscle Manipulator Research Articles

Advanced force control of the 2-axes PAM-based manipulator using adaptive neural networks

SUMMARYThis paper proposes a detailed investigation on the new neural-based feed-forward PID direct force control (FNN-PID-DF) approach applied to a highly nonlinear 2-axes pneumatic artificial muscle (PAM) manipulator in order to ameliorate its force output performance. Founded on the novel inverse neural NARX model dynamically identified to learn well all nonlinear characteristics of the contact force dynamics of the 2-axes PAM-based manipulator, the novel proposed neural FNN-PID-DF force controller is innovatively implemented in order to directly force control the 2-axes PAM robot system used as a rehabilitation device subjected to internal systematic interactions and external contact force deviations. The performance of the experimental tests has proven the advantages and merits of the new force control method compared to the classical PID force control method. The new neural FNN-PID-DF force controller guides the wrist/hand of subject/patient to successfully generate the predefined desired force values.

New approach of sliding mode control for nonlinear uncertain pneumatic artificial muscle manipulator enhanced with adaptive fuzzy estimator

A new enhanced adaptive fuzzy sliding mode control approach is proposed in this article with its good availability for application in control of a highly uncertain nonlinear two-link pneumatic artificial muscle manipulator. Stability demonstration of the robust convergence of the closed-loop pneumatic artificial muscle manipulator system based on a novel enhanced adaptive fuzzy sliding mode control is experimentally proved using Lyapunov stability theorem. Obtained result confirms that the new enhanced adaptive fuzzy sliding mode control method, applied to the two-link uncertain nonlinear pneumatic artificial muscle manipulator system, is fully investigated with better robustness and precision than the standard sliding mode control and fuzzy sliding mode control techniques.

G1500301 パッシブダイナミック制御による2リンク空気圧人工筋マニピュレータの持ち上げ制御

G1500301 パッシブダイナミック制御による2リンク空気圧人工筋マニピュレータの持ち上げ制御

Modeling and control of a pneumatic artificial muscle manipulator joint – Part I: Modeling of a pneumatic artificial muscle manipulator joint with accounting for creep effect

An antagonistically actuated pair of pneumatic muscle actuators has recently become an interesting basic joint unit for developing humanoid robots or robotic manipulators in general. This basic unit exhibits some advantageous characteristics, such as high compliance, high power-weight ratio, and high volume-to-weight ratio. However, hysteresis and muscle creep are significant drawbacks that limit the performance of this manipulator unit.This paper presents a new approach to model the hysteresis, also taking into account the muscle creep, of a basic antagonistic manipulator joint constructed by a pair of Festo fluidic muscles. The experimental results show that the manipulator hysteresis has the same behavior as that found in the individual muscles. This behavior is well described by the Maxwell-slip model. The creep effect is factorized and incorporated into the constraint torque model, resulting in a stationary extracted hysteresis loop. The time-varying behavior, due to creep, is transformed into a time-invariant one, and only one identified set of Maxwell-slip model parameters is proven to be adequate. This model performs well in the prediction of the torque–angle hysteresis, not only with an arbitrary angular trajectory at any isobaric condition, but also at any moment in the creep history of the unit.

Construction of a nonlinear dynamic characteristic model of pneumatic artificial rubber muscle manipulator using the magnetorheological (MR) brake

An artificial rubber muscle as an actuator was paid attention in this study because the manipulator was found to be safe to human when it comes in contact with human. However, this actuator vibrates easily with a late response because of the applied air pressure. Then, the magnetorheological brake that uses the magnetorheological fluid with an early response is built into the joint and controls the vibration. In this article, we have grasped the dynamic characteristics of a manipulator by the construction of a model for improvement in the control performance of the magnetorheological brake. Furthermore, the simulation was performed using the model, and efficient braking of the magnetorheological brake was examined.

Modeling and Adaptive Self-Tuning MVC Control of PAM Manipulator Using Online Observer Optimized with Modified Genetic Algorithm

In this paper, the application of modified genetic algorithms (MGA) in the optimization of the ARX Model-based observer of the Pneumatic Artificial Muscle (PAM) manipulator is investigated. The new MGA algorithm is proposed from the genetic algorithm with important additional strategies, and consequently yields a faster convergence and a more accurate search. Firstly, MGA-based identification method is used to identify the parameters of the nonlinear PAM manipulator described by an ARX model in the presence of white noise and this result will be validated by MGA and compared with the simple genetic algorithm (GA) and LMS (Least mean-squares) method. Secondly, the intrinsic features of the hysteresis as well as other nonlinear disturbances existing intuitively in the PAM system are estimated online by a Modified Recursive Least Square (MRLS) method in identification experiment. Finally, a highly efficient self-tuning control algorithm Minimum Variance Control (MVC) is taken for tracking the joint angle position trajectory of this PAM manipulator. Experiment results are included to demonstrate the excellent performance of the MGA algorithm in the NARX model-based MVC control system of the PAM system. These results can be applied to model, identify and control other highly nonlinear systems as well.

Inverse Double NARX Fuzzy Modeling for System Identification

In this paper, a novel inverse double nonlinear autoregressive with exogenous input (NARX) fuzzy model is applied to simultaneously model and identify both joints of the prototype two-axis pneumatic artificial muscle (PAM) robot arm's inverse dynamic model. Highly nonlinear features of both joints of the nonlinear manipulator system are identified by the proposed inverse double NARX fuzzy (IDNF) model based on experimental input-output training data. The modified genetic algorithm (GA) optimally generates the appropriate fuzzy if-then rules to perfectly characterize the dynamic features of the two-axis PAM manipulator system. The evaluation of different IDNF models with various ARX model structures will be discussed. For the first time, the nonlinear IDNF model of the two-axis PAM robot arm is investigated. The results show that the nonlinear IDNF model that is trained by GA performs better and has a higher accuracy than the conventional inverse fuzzy model.

Design and implementation of an adaptive recurrent neural networks (ARNN) controller of the pneumatic artificial muscle (PAM) manipulator

This paper presents the design, development and implementation of an adaptive recurrent neural networks (ARNN) controller suitable for real-time manipulator control applications. The unique feature of the ARNN controller is that it has dynamic self-organizing structure, fast learning speed, good generalization and flexibility in learning. The proposed adaptive algorithm focuses on fast and efficient optimization by weighting parameters of inverse recurrent neural models used in the ARNN controller. This approach is employed to implement the ARNN controller with a view to controlling the joint angle position of the highly nonlinear pneumatic artificial muscle (PAM) manipulator in real-time. The performance of this novel proposed controller was found to be superior compared with a conventional PID controller. These results can be applied to control other highly nonlinear systems as well.

Identification of pneumatic artificial muscle manipulators by a MGA-based nonlinear NARX fuzzy model

This study investigates the technique of modeling and identification of a new dynamic NARX fuzzy model by means of genetic algorithms. In conventional identification techniques, there are difficulties such as poor knowledge of the process, inaccurate process or complexity of the resulting mathematical model. All these factors deteriorate the identification performance when dealing with dynamic nonlinear industrial processes. To overcome these difficulties, this paper proposes a novel approach by using a modified genetic algorithm (MGA) combined with the predictive capability of nonlinear ARX (NARX) model for generating the dynamic NARX Takagi–Sugeno (TS) fuzzy model. The MGA algorithm processes the experimental input–output training data from the real system and optimizes the NARX fuzzy model parameters. This is referred to as fuzzy identification, which automatically generates the appropriate fuzzy if-then rules to characterize the dynamic nonlinear features of the real plant. The prototype pneumatic artificial muscle (PAM) manipulator, being a typical nonlinear and time-varying system, is used as a test system for this novel approach. This result shows that, with this MGA-based modeling and identification, the novel NARX fuzzy model identification approach to the PAM manipulator achieved highly outstanding performance and high precision as well. The accuracy of the proposed MGA-based NARX fuzzy model proves excellent in comparison with the MGA-based TS fuzzy model and the conventional GA-based TS fuzzy model.

Comparative study of modeling and identification of the pneumatic artificial muscle (PAM) manipulator using recurrent neural networks

The paper deals with the PAM manipulator modeling and identification based on autoregressive recurrent neural networks. For the first time, the most powerful types of neural-network-based nonlinear autoregressive models, namely, NNARMAX, NNOE and NNARX models, will be applied comparatively to the PAM manipulator identification. Furthermore, the evaluation of different nonlinear neural network auto-regressive models of the PAM manipulator with different number of neurons in hidden layer is completely discussed. On this basis, the merits of each identified model of the highly nonlinear PAM manipulator have been analyzed and compared. The results show that the nonlinear NNARX model yields better performance and higher accuracy than the other nonlinear NNARMAX and NNOE model schemes. These results can be applied to model and identify not only the PAM manipulator but also to control other nonlinear and time-varying industrial systems.

Development of Pneumatic Artificial Muscle Manipulator with Feedforward and Feedback Control

Development of Pneumatic Artificial Muscle Manipulator with Feedforward and Feedback Control

A new approach for modelling and identification of the pneumatic artificial muscle manipulator based on recurrent neural networks

Pneumatic artificial muscles (PAMs) are widely used in the various fields of medical robots and other industrial applications. As a powerful tool of intelligent control, neural networks nowadays are applied effectively to model, identify, and control highly non-linear systems, including the PAM manipulator. In the current paper a prototype PAM manipulator is modelled through recurrent neural networks (NN) modelling and identification based on experimental input-output training data. The proposed incremental back-propagation (INCBP) algorithm, which yields faster convergence than a conventional back-propagation (BP) algorithm, is applied to train the neural networks. The realization of the INCBP algorithm is given. An evaluation is carried out for different non-linear NN auto-regressive with exogenous input (NNARX) models of a PAM manipulator, using recurrent NN with various input nodes as well as various hidden layer nodes. For the first time, the non-linear NNARX model scheme of the prototype PAM manipulator has been investigated. The results show that the non-linear NNARX model trained by INCBP yields better performance and higher accuracy than the traditional linear ARX model. These results can be applied in modelling, identifying, and controlling not only the PAM manipulator, but also other highly non-linear systems.

Intelligent phase plane switching control of a pneumatic muscle robot arm with Magneto-Rheological Brake

Pneumatic cylinders are one kind of low cost actuation sources which have been applied in industrial and robotics field, since they have a high power/weight ratio, a high-tension force and a long durability. To overcome the shortcomings of conventional pneumatic cylinders, a number of newer pneumatic actuators have been developed such as McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle (PAM) Manipulators. However, some limitations still exist, such as the air compressibility and the lack of damping ability of the actuator bring the dynamic delay of the pressure response and cause the oscillatory motion. In addition, the nonlinearities in the PAM manipulator still limit the controllability. Therefore, it is not easy to realize motion with high accuracy and high speed and with respect to various external inertia loads.

Control of two-axis pneumatic artificial muscle manipulator with a new phase plane switching control method

The use of robots in rehabilitation has become an issue of increasing importance because of the requirement of functional recovery therapy for limbs. A novel pneumatic artificial muscle (PAM) actuator — which has achieved increased popularity for providing safety and mobility assistance to humans performing tasks, as well as providing another advantages such as high strength and power/weight ratio, low cost, compactness, ease of maintenance, cleanliness, readily available, cheap power source, and so on — has been considered during the recent decades for use in a therapy robot, which in particular requires a high level of safety. However, some limitations still exist, such as air compressibility and the lack of damping ability of the actuator to bring the dynamic delay of the pressure response and cause the oscillatory motion. In addition, to aid rehabilitation more efficiently, the robot should adjust its impedance parameters according to the physical condition of the patient For this purpose, the manipulator join is equipped with a Magneto-Rheological Brake (MRB). A new phase plane switching control method using MRB is proposed for tracking sinusoidal waveforms. The effectiveness of the proposed algorithm is demonstrated through an experiment using a fabricated two-axis PAM manipulator. The experiment proves that the stability of the manipulator could be greatly improved using a high gain control without regard to the change of the frequencies of the reference input and the external load condition, and without decreasing the response speed or lowering the stiffness of PAM manipulator.

Intelligent switching control of a pneumatic muscle robot arm using learning vector quantization neural network

Pneumatic cylinders are one of the low-cost actuation sources used in industrial and prosthetic application, since they have a high power/weight ratio, high-tension force and long durability. However, problems with the control, oscillatory motion and compliance of pneumatic systems have prevented their widespread use in advanced robotics. To overcome these shortcomings, a number of newer pneumatic actuators have been developed, such as the McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle (PAM) Manipulators. In this paper, the solution for position control of a robot arm with slow motion driven by two pneumatic artificial muscles is presented. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external load. To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is proposed in this paper. The LVQNN estimates the external load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external working loads.

Nonlinear PID control to improve the control performance of 2 axes pneumatic artificial muscle manipulator using neural network

The application of a robot to rehabilitation has become a matter of great concern because of the requirement of functional recovery therapy of arm or limb. A novel pneumatic artificial muscle (PAM) actuator which has achieved increased popularity to provide the inherent safety and mobility assistance to humans performing tasks and another advantages such as high strength and power/weight ratio, low cost, compactness, ease of maintenance, cleanliness, readily available and cheap power source and so on. However, the complex nonlinear dynamics of the PAM manipulator makes it a challenging and appealing system for modeling and control design. The problems with the time variance, compliance, high hysteresis and nonlinearity of pneumatic systems have made it difficult to realize precise position control. In order to realize satisfactory control performance, the effect of nonlinear factors contained in the PAM manipulator must be considered. The purpose of this study is to improve the control performance of 2 axes PAM manipulator using a nonlinear PID controller. Superb mixture of conventional PID controller and the neural network, which has powerful capability of learning, adaptation and tackling nonlinearity, brings us a novel nonlinear PID controller using neural network. This proposed controller is appropriate for a kind of plants with nonlinearity uncertainties and disturbances. The experiments were carried out in practical 2 axes PAM manipulator and the effectiveness of the proposed control algorithm was demonstrated through the experiments, which suggests its superior performance and disturbance rejection.

Intelligent phase plane switching control of pneumatic artificial muscle manipulators with magneto-rheological brake

The pneumatic artificial muscle (PAM) is undoubtedly the most promising artificial muscle for the actuation of new types of industrial robots such as rubber actuator and PAM manipulators because it provides these advantages such as high strength and high power/weight ratio, low cost, compactness, ease of maintenance, cleanliness, readily available and cheap power source, inherent safety and mobility assistance to humans performing tasks. However, some limitations still exist, such as the air compressibility and the lack of damping ability of the actuator bring the dynamic delay of the pressure response and cause the oscillatory motion. In addition, the nonlinearities in the PAM manipulator still limit the controllability. Therefore, it is not easy to realize motion with high accuracy and high speed and with respect to various external inertia loads in order to realize a human-friendly therapy robot. To overcome these problems a novel controller which harmonizes a phase plane switching control method (PPSC) with conventional PID controller and the adaptabilities of neural network, is newly proposed. In order to realize satisfactory control performance a variable damper, magneto-rheological brake (MRB), is equipped to the joint of the manipulator. Superb mixture of conventional PID controller and an intelligent phase plane switching control using neural network (IPPSC) brings us a novel controller. The experiments were carried out in practical PAM manipulator and the effectiveness of the proposed control algorithm was demonstrated through experiments, which had proved that the stability of the manipulator can be improved greatly in a high gain control by using MRB with IPPSC and without regard for the changes of external inertia loads.

Performance improvement of pneumatic artificial muscle manipulators using magneto-rheological brake

A novel pneumatic artificial muscle actuator (PAM actuator), which has achieved increased popularity to provide the advantages such as high strength and high power/weight ratio, low cost, compactness, ease of maintenance, cleanliness, readily available and cheap power source, inherent safety and mobility assistance to humans performing tasks, has been regarded during the recent decades as an interesting alternative to hydraulic and electric actuators However, some limitations still exist, such as the air compressibility and the lack of damping ability of the actuator bring the dynamic delay of the pressure response and cause the oscillatory motion Then it is not easy to realize the performance of transient response of pneumatic artificial muscle manipulator (PAM manipulator) due to the changes in the external inertia load with high speed In order to realize satisfactory control performance, a variable damper — Magneto-Rheological Brake (MRB), is equipped to the joint of the manipulator Superb mixture of conventional PID controller and a phase plane switching control method brings us a novel controller This proposed controller is appropriate for a kind of plants with nonhnearity, uncertainties and disturbances. The experiments were carried out in practical PAM manipulator and the effectiveness, of the proposed control algorithm was demonstrated through experiments, which had proved that the stability of the manipulator can be improved greatly in a high gain control by using MRB with phase plane switching control method and without regard for the changes of external inertia loads

Nonlinear PID control to improve the control performance of the pneumatic artificial muscle manipulator using neural network

A novel actuator system which has achieved increased popularity to provide these advantages such as high strength and power/weight ratio, low cost, compactness, ease of maintenance, cleanliness, readily available, cheap power source, inherent safety and mobility assistance to humans performing tasks has been the utilization of the pneumatic artificial muscle (PAM) manipulator, in recent times. However, the complex nonlinear dynamics of the PAM manipulator makes it a challenging and appealing system for modeling and control design. The problems with the time variance, compliance, high hysteresis and nonlinearity of pneumatic systems have made it difficult to realize precise position control with high speed. In order to realize satisfactory control performance, the effect of nonlinear factors contained in the PAM manipulator must be considered. The purpose of this study is to improve the control performance of the PAM manipulator using a nonlinear PID controller. Superb mixture of conventional PID controller and the neural network, which has powerful capability of learning, adaptation and tackling nonlinearity, brings us a novel nonlinear PID controller using neural network. This proposed controller is appropriate for a kind of plants with nonlinearity uncertainties and disturbances. The experiments were carried out in practical PAM manipulator and the effectiveness of the proposed control algorithm was demonstrated through the experiments, which suggests its superior performance and disturbance rejection.

Intelligent Switching Control of Pneumatic Artificial Muscle Manipulator

Problems with the control, oscillatory motion and compliance of pneumatic systems have prevented their widespread use in advanced robotics. However, their compactness, power/weight ratio, ease of maintenance and inherent safety are the factors that could potentially be exploited in sophisticated dexterous manipulator designs. These advantages have led to the development of novel actuators such as the McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle Manipulators. However, some limitations still exist, such as deterioration of the performance of transient response due to the change of the external inertia load in the pneumatic artificial muscle manipulator. To overcome this problem, switching algorithm of control parameter using learning vector quantization neural network (LVQNN) is newly proposed, which estimates the external inertia load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external inertia loads.