Non-Parametric Neuro-Adaptive Control Subject to Task Specifications

Understand

We develop a learning-based algorithm for the control of autonomous systems governed by unknown, nonlinear dynamics to satisfy user-specified spatio-temporal tasks expressed as signal temporal logic specifications.

• 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 neural-network-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 tasks.