Hysteresis modeling and tracking control for a dual pneumatic artificial muscle system using Prandtl–Ishlinskii model

Lin, Chih-Jer; Lin, Ching-Hsiung; Shen, Yu; Chen, Chun Ta

doi:10.1016/j.mechatronics.2015.03.006

Cited by 96 publications

(55 citation statements)

References 22 publications

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“…However, compared with conventional pneumatic cylinders and motors, PAMs have several advantages, including compactness, inherent compliance, low cost, and high power-to-weight ratio. (14) Moreover, PAMs are highly compliant actuators to manipulate the rehabilitation exoskeleton; thus, they are safer than other actuators such as motors. Compared with other complicated rehabilitation orthosis systems, the main purpose of our design is concentrated on knee rehabilitation.…”

Section: Design Of Pam Actuated Rehabilitation Robotmentioning

confidence: 99%

Pneumatic Artificial Muscle Actuated Robot for Lower Limb Rehabilitation Triggered by Eelectromyography Signals Using Discrete Wavelet Transformation and Support Vector Machines

2017

Sensors and Materials

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In this study, a real-time electromyography (EMG)-triggered controller for a pneumatic artificial muscle (PAM) actuated lower limb rehabilitation robot is proposed. To make the rehabilitation task controllable by the patient's movement intention, a novel trigger controller is designed according to the EMG signals of the patient's muscle. For predicting his or her movement intention in advance, the EMG signals of the patient's muscle are captured and identified to realize the proposed EMG-triggered control. To guarantee the safety and performance of the proposed system, a patient's movement intention must be identified accurately by EMG feature extraction. First, the discrete wavelet transformation (DWT) technique is used to acquire the feature vectors of the EMG signals. The properties of the different feature spaces are taken into consideration, and the optimal multicomponents of features are chosen according to the experimental results. Second, support vector machines (SVMs) are studied to improve the classification performance. Finally, the MyRIO controller is used to implement a closed-loop control system for the rehabilitation robot with the movement-intention trigger control.

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Section: Design Of Pam Actuated Rehabilitation Robotmentioning

confidence: 99%

Pneumatic Artificial Muscle Actuated Robot for Lower Limb Rehabilitation Triggered by Eelectromyography Signals Using Discrete Wavelet Transformation and Support Vector Machines

2017

Sensors and Materials

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“…The P-I model [31][32][33] utilizes the play operator and the density function to describe input-output relationships of the hysteresis. Since hysteresis loops of the piezoelectric positioning system may be asymmetric, using the generalized P-I model, hysteresis nonlinearity v(t) in equation (15) can be represented as…”

Section: G(s)mentioning

confidence: 99%

Parameter identification of hysteresis nonlinear dynamic model for piezoelectric positioning system based on the improved particle swarm optimization method

Xie

Chen

2017

Advances in Mechanical Engineering

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An improved particle swarm optimization method that has better equilibrium characteristic between global search and local search is proposed for parameter identification of hysteresis nonlinear dynamic model for piezoelectric positioning system. In this article, a general electromechanical-based piezoelectric positioning systems' hysteresis nonlinear dynamic model suitable for engineering optimization is introduced, and a parameter identification technique based on improved particle swarm optimization for this dynamic model is presented. For increasing the speed and precision of identification process, the hysteresis effect is described by the generalized Prandtl-Ishlinskii model, and the symmetric solutions of the system are applied to solve the hysteresis nonlinear dynamics equations and then the working process of the improved particle swarm optimization method is given. In order to verify the effectiveness of the proposed method, comparisons between experimental data and simulation data are carried out. The results demonstrate that improved particle swarm optimization method can make the hysteresis nonlinear dynamic model with identified parameters fit the experimental data with higher precision, and since the application of the symmetric solutions, the identification time is greatly reduced. Therefore, the improved particle swarm optimization method is an optional effective technique for identifying the parameters of hysteresis nonlinear dynamic model for the piezoelectric positioning system.

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“…The second class is based on the differential equations to characterize the hysteresis, such as the Duhem model, LuGre model, Bouc-Wen model, etc. In a similar manner, Lin et al [11] investigated the hysteresis modeling and tracking control methods of the PAM by using the BoucWen model, and designed different feedback control schemes for the compensation of hysteresis in order to reduce the tracking errors. Zhao et al [12] applied the Duhem model to characterize the force-length hysteretic behaviour of the PAM, based on which a novel cascade position PID controller was designed to regulate the pressure.…”

Section: Fig 1 the Pneumatic Artificial Musclementioning

confidence: 99%

“…Aschemann and Schindele [14] adopted the generalized BoucWen model, quasi-static Maxwell-slip model, and Prandtl-Ishlinskii model to establish the forcelength hysteresis, and concluded that the first model was the most effective one for the control of a highspeed linear axis actuated by PAMs. Among these studies, the PI model is the most widely used due to the following advantages [11]: (1) the PI model contains a limited number of linear play operators; (2) both the PI model and the inverse PI model have analytical expressions. It must be pointed out that the conventional PI model is unable to describe the asymmetric characteristic of the hysteresis [15] and saturation [16], due to the symmetric property of the linear play operators.…”

Section: Fig 1 the Pneumatic Artificial Musclementioning

confidence: 99%

Hysteresis Modelling and Compensation of Pneumatic Artificial Muscles using the Generalized Prandtl-Ishlinskii Model

Mei

Xie

Liu

et al. 2017

SV-JME

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Hysteresis modeling and tracking control for a dual pneumatic artificial muscle system using Prandtl–Ishlinskii model

Cited by 96 publications

References 22 publications

Pneumatic Artificial Muscle Actuated Robot for Lower Limb Rehabilitation Triggered by Eelectromyography Signals Using Discrete Wavelet Transformation and Support Vector Machines

Pneumatic Artificial Muscle Actuated Robot for Lower Limb Rehabilitation Triggered by Eelectromyography Signals Using Discrete Wavelet Transformation and Support Vector Machines

Parameter identification of hysteresis nonlinear dynamic model for piezoelectric positioning system based on the improved particle swarm optimization method

Hysteresis Modelling and Compensation of Pneumatic Artificial Muscles using the Generalized Prandtl-Ishlinskii Model

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