Abstract

This paper proposes an air pressure supply structure for artificial muscles. The main body of the structure comprises a hollow tube, an electromagnet in the outer layer, and a magnetic piston in the inner diameter of the tube. At both ends, a hose interface connects the air inlet of the artificial muscle. Under the action of controlling changes in current, the electromagnet nested in the outer wall causes the movement of the piston by changing the force between the electromagnet and the magnetic piston and by changing the law of air pressure in the tube. Because the inside of the tube is a closed space, the movement of the magnetic piston in the tube causes a change in the volume of the gas at both ends, thus forming pressure differences of different sizes and directions. Therefore, this air supply, with specific oscillatory characteristics, can be used to produce the desired movement of artificial muscles. Through system modeling, theoretical analysis, and simulation experiments of the connected pressure supply structure, we verified that the system has inherent characteristics similar to a spring damping structure. In view of the inherent characteristics of this kind of structure, this paper introduces the trend of input and output changes by considering the deviation value, details how to improve the traditional neural network PID control algorithm, and discusses the intelligent optimization of controller parameters. Simulation results show that the improved control method can effectively overcome the nonlinear and coupling characteristics of the system, and the gas supply structure can provide a continuous pressure supply curve of an arbitrary waveform and a frequency within a certain amplitude range. The designed air supply structure was applied to a quadruped robot, using its oscillating characteristics to generate rhythmic movement. Compared with the traditional pressure control method, the piston was driven to produce reciprocating motion by fully exploiting the energy stored in the compressed gas, so as to reduce the external energy input and reduce the comprehensive energy consumption of the system. In addition, the control algorithm improved in this paper can meet diverse pressure requirements for driving artificial muscles. Moreover, the independent control of leg support force and stiffness can be realized by combining it with the antagonistic joints. This structure can be widely used in the pressure supply of outdoor robot artificial muscle.

Highlights

  • The pneumatic artificial muscle (PAM) has been widely studied in various fields because of its low weight, small volume, high flexibility, and good biocompatibility

  • Yin et al [2] simulated an artificial muscle calf joint model designed by a natural neuromuscular model and showed that it successfully performed over 92% of the muscle activation that was naturally made by human participants

  • Park et al [4] designed a wearable robot device assisting in anklefoot rehabilitation, which utilized four PAMs to assist in

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Summary

Introduction

The pneumatic artificial muscle (PAM) has been widely studied in various fields because of its low weight, small volume, high flexibility, and good biocompatibility. This paper proposes an artificial muscle air pressure supply structure with a low weight, small volume, and convenient assembly This structure is based on the principle of a communication device; it can control the air pressure of two muscles at the same time, and it can make full use of the energy stored by compressed gas to greatly reduce the comprehensive energy consumption of the system. Anh et al [21] proposed a new adaptive neural network control algorithm considering the influence of the internal coupling effect and the external end-effect contact force It improved the compliance force/position output performance of a highly nonlinear tandem PAM robot. Based on the bionic antagonistic joint structure of the leg robot, the pressure supply method and control algorithm proposed in this paper were used to simulate the torque and stiffness control effect to verify the application prospect of the model. The influences of each structural parameter on the inherent characteristics of the model are simulated and analyzed, and the method to select the model parameters according to the actual application is given

Connector structural design
Network training
Findings
Conclusion
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