Non-Parametric Neuro-Adaptive Control

We develop a learning-based algorithm for the control of autonomous systems governed by unknown, nonlinear dynamics toward trajectory tracking. Most existing algorithms either assume certain parametric forms for the unknown dynamic terms or resort to unnecessarily large control inputs in order to provide theoretical guarantees. The proposed algorithm addresses these drawbacks by integrating neuralnetwork-based learning with adaptive control. More specifically, the algorithm learns a controller, represented as a neural network, using training data that correspond to a collection of system parameters and reference trajectories. These parameters and trajectories are derived by varying the nominal parameters and the reference trajectory, respectively. It then incorporates this neural network into an online closed-form adaptive control law in such a way that the closed-loop system tracks the reference trajectory. The proposed algorithm does not use any a priori information on the unknown dynamic terms or any approximation schemes. Comparative computer simulations demonstrate the effectiveness of the proposed algorithm.