A real-time neuro-adaptive controller with guaranteed stability

This paper presents a new model reference adaptive neuro-control scheme using feedforward neural networks with momentum back-propagation (MBP) learning algorithm. Training is done on-line to tune the parameters of the neuro-controller that provides the control signal. Noting that pre-learning is not required and the structure of overall system is very simple and straightforward. No additional controller or robustifying signal is required. Tracking performance is guaranteed via Lyapunov stability analysis. Both tracking error and neural network weights remain bounded. An interesting fact about the proposed approach is that it does not require a NN being capable of reconstructing globally model non-linearities.