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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 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]

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]