Modeling the Dynamic Characteristics of Pneumatic Muscle

Abstract

A pneumatic muscle (PM) system was studied to determine whether a three-element model could describe its dynamics. As far as the authors are aware, this model has not been used to describe the dynamics of PM. A new phenomenological model consists of a contractile (force-generating) element, spring element, and damping element in parallel. The PM system was investigated using an apparatus that allowed precise and accurate actuation pressure (P) control by a linear servo-valve. Length change of the PM was measured by a linear potentiometer. Spring and damping element functions of P were determined by a static perturbation method at several constant P values. These results indicate that at constant P, PM behaves as a spring and damper in parallel. The contractile element function of P was determined by the response to a step input in P, using values of spring and damping elements from the perturbation study. The study showed that the resulting coefficient functions of the three-element model describe the dynamic response to the step input of P accurately, indicating that the static perturbation results can be applied to the dynamic case. This model is further validated by accurately predicting the contraction response to a triangular P waveform. All three elements have pressure-dependent coefficients for pressure P in the range 207 < or = P < or = 621 kPa (30 < or = P < or = 90 psi). Studies with a step decrease in P (relaxation of the PM) indicate that the damping element coefficient is smaller during relaxation than contraction.

A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network

“…Due to their highly nonlinear nature and time-varying parameters, PAM robot arms present a challenging nonlinear model problem. Approaches to PAM control have included PID control, adaptive control (Lilly, 2003), nonlinear optimal predictive control (Reynolds et al, 2003), variable structure control (Repperger et al, 1998;Medrano-Cerda et al,1995), gain scheduling (Repperger et al,1999), and various soft computing approaches including neural network Kohonen training algorithm control (Hesselroth et al,1994), neural network + nonlinear PID controller (Ahn and Thanh, 2005), and neuro-fuzzy/genetic control (Chan et al, 2003;Lilly et al, 2003). Balasubramanian et al, (2003a) applied the fuzzy model to identify the dynamic characteristics of PAM and later applied the nonlinear fuzzy model to model and to control of the PAM system.…”

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

“…Many potential applications involve some type of exo-skeletal or link segment configuration that attaches to existing anatomical body-segments [3][4][5][6]. Research into the control and the physical and modeling properties of PAM has been undertaken at the INSA (Toulouse, France) [7], the Bio-Robotics Lab at the University of Washington, Seattle, [8], Human Sensory Feedback (HSF) Laboratory at Wright Patterson Air Force Base [9], and Fluid Power and Machine Intelligence Laboratory (FPMI) at Ulsan University [10][11][12] and so on. This paper addresses the modeling, identification and control of a PAM manipulator actuated by a group of antagonistic PAM pair.…”

Sliding mode control of robotics systems actuated by pneumatic muscles.

“…The properties of a pneumatic muscle system were studied in the Human vertically actuating a mass [41,48].…”

“…are the contractile coefficient, spring coefficient, damping coefficient respectively, which are given in [41] as:…”

“…Now, the closed-loop dynamics can be linearly approximated as: Referring to [41], the maximum load applied to the system is 898/9.8= 91.6Kg, = is (locally) asymptotically stable for the actual nonlinear system (5.6), i.e.,…”