Abstract
The properties of pneumatic artificial muscle (PAM) with excellent power-to-weight ratio and natural compliance made it useful for healthcare engineering applications. However, it has undesirable hysteresis effect in controlling a robotic manipulator. This behavior is quasistatic and quasirate dependent which changed with excitation frequency and external force. Apart from this, it also inherits frictional presliding behavior with nonlocal memory effect. These nonlinearities need to be compensated to achieve optimal performance of the control system. Even though an inverse modeling of PAM has limited application, it is important on certain control system implementation that requires the solution to the inverse problem. In this paper, the inverse modeling of PAM in the form of activation pressure was proposed. This activation pressure model was derived according to static pressure and extracted hysteresis components from pressure/length hysteresis. The derivation of the static pressure model follows the phenomenological-based model of third-order polynomial. It is capable of characterizing the nonlinear region of PAM at low and high pressure. The derivation of extracted hysteresis model follows the mechanism of dynamic friction. In this principle, the activation pressure model was dependent on regression coefficient of the static pressure model and dynamic friction coefficients of the extracted hysteresis model. The regression constants of these coefficients were characterized from the hysteresis dataset by using model parameter identification and the particle swarm optimization (PSO) method. The result from model simulation shows the root mean square error (RMSE) value of less than 10% error was evaluated at various excitation frequencies and external forces. This inverse modeling of PAM implemented a simple approach, but it should be useful in control design applications such as rehabilitation robotics, biomedical system, and humanoid robots.
Highlights
Introduction e configuration ofpneumatic artificial muscle (PAM) enables its pneumatic power to be converted into mechanical power with high power-toweight ratio compared to rigid actuators
The activation pressure model was dependent on regression coefficient of the static pressure model and dynamic friction coefficients of the extracted hysteresis model. e regression constants of these coefficients were characterized from the hysteresis dataset by using model parameter identification and the particle swarm optimization (PSO) method. e result from model simulation shows the root mean square error (RMSE) value of less than 10% error was evaluated at various excitation frequencies and external forces. is inverse modeling of PAM implemented a simple approach, but it should be useful in control design applications such as rehabilitation robotics, biomedical system, and humanoid robots
10 −2.19E − 06 9.55E − 04 −9.42E − 03 3.51 E + 01 data profile of static pressure and the extracted hysteresis dataset obtained from the model parameter and system identification method. e random wave signal of the independent variable obtained from the hysteresis dataset at various excitation frequencies and external forces was used for the model simulation of the proposed activation pressure model. is random wave signal generates various contracting pattern of cyclic contraction and expansion
Summary
Received 3 June 2020; Revised 31 July 2020; Accepted 27 November 2020; Published 9 December 2020. E first part involved evaluation of the proposed activation pressure model based on quasistatic, quasirate, and nonlocal memory behaviors It was performed using the random wave input signal of the independent variable obtained from the hysteresis dataset at various excitation frequencies and external forces. E mathematical formulation of the proposed static pressure model uses a polynomial regression of third order, as shown in equation (2), where yi is the dependent variable vector, εi is the independent variable vector, and βn 0,1,2,3 is the parameter vector It has been reported in the literature that the second-order polynomial is enough to characterize the nonlinear region of PAM; to consider the nonlinear region at low- and high-pressure activation, thirdorder polynomial function is required. The polynomial model has not been estimated based on the identified parameters (primary data)
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