Adaptive model-based neural network control

A form of adaptive model-based control is proposed and experimentally evaluated. An adaptive model-based neural network controller (AMBNNC) uses multilayer perceptron artificial neural networks to estimate the payload during high-speed manipulator motion. The payload estimate adapts the feedforward compensator to unmodeled system dynamics and payload variation. The neural nets are trained through repetitive training on trajectory tracking error data. The AMBNNC was experimentally evaluated on the third link of a PUMA-560 manipulator. Tracking performance was evaluated for a wide range of payload and trajectory conditions and compared to a nonadaptive model-based controller. The superior tracking accuracy of the AMBNNC demonstrates the potential of the technique