An approach to uncertainty compensation using a neural network for multi-manipulator system control

An approach to uncertainty compensation using a multilayer feedforward neural network in multi-manipulator system control is proposed. The proposed approach is developed by formulating the dynamics of the multi-manipulator system in the constrained motion framework. The error-backpropagation algorithm is employed for neural network learning. The teaching signal for neural network learning is derived by analyzing the stability of the closed-loop system. It is shown that if the neural network learns to generate the proper compensating signal, then the constrained motion of the multi-manipulator system tracks the desired motion asymptotically; as a consequence, the desired forces can be achieved. Computer simulations are conducted to verify the proposed approach