Admittance neuro-control of a lifting device to reduce human effort

In this paper, two admittance-based control schemes for a power-assisted lifting device are presented. This device can be used to hoist a heavy object interactively for reducing the operator’s burden. The proposed system integrates an admittance controller with an inner control loop that regulates the velocity of the object. The admittance is the outer loop that establishes the desired relation between the applied force to the object and its velocity. For the adaptation to a variety of loads, an online learning controller is implemented based on a neural network (NN) with backpropagation training. The overfitting of the NN is resolved with weight decay to decrease the oscillations around the equilibrium point. Alternatively, a gain scheduling PID controller is designed for the inner loop, which measures the object weight and tunes the gains with predefined rules. The performance of these two adaptation methods is demonstrated on an experimental setup and the results illustrate that better generalization can be achieved with the NN.