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

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

Automated summary

This paper proposes controlling the variable velocity of the drive crank of a four-bar mechanism to achieve desired output motions. A neural network is trained using data from the kinematic model to estimate the reference trajectory for the crank velocity. The control law is designed using feedback linearization and a Proportional-Integral-Derivative (PID) controller. Simulation results validate the effectiveness of the proposed scheme for three variable speed profiles.