Log In Sign Up

Non-Parametric Neuro-Adaptive Control Subject to Task Specifications

by   Christos K. Verginis, et al.

We develop a learning-based algorithm for the control of robotic systems governed by unknown, nonlinear dynamics to satisfy tasks expressed as signal temporal logic specifications. Most existing algorithms either assume certain parametric forms for the dynamic terms or resort to unnecessarily large control inputs (e.g., using reciprocal functions) in order to provide theoretical guarantees. The proposed algorithm avoids the aforementioned drawbacks by innovatively 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 different tasks and robot parameters. It then incorporates this neural network into an online closed-loop adaptive control mechanism in such a way that the resulting behavior satisfies a user-defined task. The proposed algorithm does not use any information on the unknown dynamic terms or any approximation schemes. We provide formal theoretical guarantees on the satisfaction of the task and we demonstrate the effectiveness of the algorithm in a virtual simulator using a 6-DOF robotic manipulator.


page 6

page 8


Non-Parametric Neuro-Adaptive Coordination of Multi-Agent Systems

We develop a learning-based algorithm for the distributed formation cont...

Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees

Learning for control of dynamical systems with formal guarantees remains...

Model-based Reinforcement Learning from Signal Temporal Logic Specifications

Techniques based on Reinforcement Learning (RL) are increasingly being u...

Risk-Awareness in Learning Neural Controllers for Temporal Logic Objectives

In this paper, we consider the problem of synthesizing a controller in t...

Neural Identification for Control

We present a new method for learning control law that stabilizes an unkn...

Neurosymbolic Motion and Task Planning for Linear Temporal Logic Tasks

This paper presents a neurosymbolic framework to solve motion planning p...

Markov Random Geometric Graph (MRGG): A Growth Model for Temporal Dynamic Networks

We introduce Markov Random Geometric Graphs (MRGGs), a growth model for ...