Adaptive vision-based control of a motor-toggle mechanism: Simulations and experiments

The dynamic motion of an adaptive visual servoing feedback-controlled punching machine, which is made up by a toggle mechanism driven by a permanent magnet (PM) synchronous servomotor, is studied numerically and experimentally. First, Hamilton's principle, Lagrange multiplier, geometric constraints and partitioning method are employed to derive its dynamic equations for numerical simulations. To satisfy the demand of the machine performance, adaptive controller by using stability analysis with inertia-related Lyapunov function is designed to control the slider responses. Different from the previous studies, the vision servo system of a non-contact measurement of a charge-coupled device (CCD) camera is employed to measure the shape patterns as output states instead of using the expensive linear scale or encoder of the motor-mechanism system. Finally, the stable and robust control performances of an adaptive controller by the Lyapunov stable theory for a motor-toggle mechanism with external disturbances are proposed. A combination of the visual servoing measurement system and the motion control system are established experimentally for the motor-toggle mechanism system. From the agreements between numerical simulations and experimental results, it is demonstrated that the proposed controller, by use of machine vision, is robust to external disturbances for a punching machine system.