DeepAI
Log In Sign Up

Neural Identification for Control

09/24/2020
by   Priyabrata Saha, et al.
0

We present a new method for learning control law that stabilizes an unknown nonlinear dynamical system at an equilibrium point. We formulate a system identification task in a self-supervised learning setting that jointly learns a controller and corresponding stable closed-loop dynamics hypothesis. The input-output behavior of the unknown dynamical system under random control inputs is used as the supervising signal to train the neural network-based system model and the controller. The method relies on the Lyapunov stability theory to generate a stable closed-loop dynamics hypothesis and corresponding control law. We demonstrate our method on various nonlinear control problems such as n-Link pendulum balancing, pendulum on cart balancing, and wheeled vehicle path following.

READ FULL TEXT

page 1

page 2

page 3

page 4

06/04/2022

Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees

Learning for control of dynamical systems with formal guarantees remains...
09/06/2019

Control of Separable Subsystems with Application to Prostheses

Nonlinear control methodologies have successfully realized stable human-...
05/30/2020

"Closed Proportional-Integral-Derivative-Loop Model" Following Control

The proportional-integral-derivative (PID) control law is often overlook...
07/25/2021

Deep Learning Explicit Differentiable Predictive Control Laws for Buildings

We present a differentiable predictive control (DPC) methodology for lea...
12/06/2019

Relativistic Control: Feedback Control of Relativistic Dynamics

Strictly speaking, Newton's second law of motion is only an approximatio...
06/25/2021

Non-Parametric Neuro-Adaptive Control Subject to Task Specifications

We develop a learning-based algorithm for the control of robotic systems...