Abstract:

Bionic joint is a fundamental element of quadruped bionic robot realizing high-speed and complicated movement such as flexible jumping, running and so on. In allusion to the problem that existing bionic joint driven by pneumatic muscles actuators is difficult to realize high precision position control under the setting stiffness, a new method of antagonistic bionic joint position/stiffness control based on fuzzy neural network compensation control is proposed. According to joint driving torque and setting stiffness, joint position/stiffness’s calculating model is built to compute theoretical pressure of pneumatic muscles actuators. According to joint’s output position and actual pressure of pneumatic muscles actuators, joint output stiffness’s calculation model is built to compute actual output stiffness of bionic joint. The fuzzy neural network compensation control structure, composed of fuzzy neural network compensation controller, PID controller and fuzzy neural network identifier, is adopted to realize high precision position control of bionic joint. Taking MAS type pneumatic muscles actuators produced by FESTO Company as controlled object, the experimental study on joint position/stiffness control is carried out to compare the position control precision of PID control and fuzzy neural network compensation control, and explore the stiffness dynamic response of bionic joint. Experimental results show that: the position control precision of fuzzy neural network compensation control method is obviously superior to that of PID control method, position control precision is increased from 3° to 0.6°, and both of control methods are able to follow the joint’s setting stiffness well, stiffness tracking precision is within 1 N?m/rad.

Key words: bionic joint, experimental verification, fuzzy neural network compensation control, position/stiffness control, pneumatic muscles actuators