Neural Network Based Control of Four-Bar Mechanism with Variable Input Velocity

For control applications, the angular velocity of drive crank a four-bar mechanism is traditionally assumed to be constant. In this paper, we propose variable obtain desired output motions for coupler point. To estimate reference trajectory velocity, neural network trained with data from kinematic model. The law designed feedback linearization tracking error dynamics and Proportional–Integral–Derivative (PID) controller. applicability proposed scheme validated through simulations three speed profiles, obtaining excellent results system.