Control electromechanical arms by using a neural circuit

Neural electrical signals forced the muscle tissue to behave effective body gait. When neural activities are measured in a neural circuit, artificial electromechanical arm and leg can be controlled to mimic the movement and even vibration of limbs. In this paper, a simple neural circuit is used to drive an electromechanical arm (EMA) device by activating Ampere’s force via the load circuit adhered to the moving beam, and the load circuit is coupled with the neural circuit for energy conversion. The circuit equations, field energy and moving equation of the beam are obtained for dynamical analysis. Furthermore, two EMAs are coupled via a spring for mimicking the cooperation between two arms or legs during synchronous movement, and then the same electrical signal is used to control the moving states of the coupled EMAs. This processing can describe the synchronous movements of two arms along horizontal linear motion under neural stimuli. Noisy disturbance is applied to detect and predict occurrence of stochastic resonance in the moving beams by calculating signal to noise ratio, and the average Hamilton energy vs. time is effective to predict the emergence of nonlinear stochastic and coherence resonance by approach the average power from physical aspect. The results provide helpful guidance to design complex electromechanical device for behaving complex gaits. That is, neural signals can be used to excite the electromechanical devices as muscles and then the body gaits are controlled effectively.