Supervised learning algorithms for controlling underactuated dynamical systems

DOI10.1016/j.physd.2020.132621zbMath1489.93083arXiv1909.11119OpenAlexW3036566854MaRDI QIDQ2127407

Bharat Monga, Jeff Moehlis

Publication date: 20 April 2022

Published in: Physica D (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1909.11119

zbMATH Keywords

coupled oscillatorssupervised learningmachine learningbinary classificationbang bang controlunderactuated dynamical systems

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Automated systems (robots, etc.) in control theory (93C85) Existence of optimal solutions belonging to restricted classes (Lipschitz controls, bang-bang controls, etc.) (49J30)

Related Items (4)

Analysis of neural clusters due to deep brain stimulation pulses ⋮ Leveraging deep learning to control neural oscillators ⋮ Phase distribution control of neural oscillator populations using local radial basis function meshfree technique with application in epileptic seizures: a numerical simulation approach ⋮ Optimal Control of Oscillation Timing and Entrainment Using Large Magnitude Inputs: An Adaptive Phase-Amplitude-Coordinate-Based Approach

Cites Work

• Unnamed Item
• Minimum energy desynchronizing control for coupled neurons
• Time optimal control of spiking neurons
• Chemical oscillations, waves, and turbulence
• Failures of adaptive control theory and their resolution
• Neural networks for control systems - a survey
• Bistability in fluctuating environments. Implications in tumor immunology
• Mathematical physiology
• Phase reduction and phase-based optimal control for biological systems: a tutorial
• Optimal phase control of biological oscillators using augmented phase reduction
• A model of desynchronizing deep brain stimulation with a demand-controlled coordinated reset of neural subpopulations
• Starting, stopping, and resetting biological oscillators: in search of optimum perturbations
• Phase distribution control of a population of oscillators
• Canards for a reduction of the Hodgkin-Huxley equations
• From model-based control to data-driven control: survey, classification and perspective
• Optimal Chaotic Desynchronization for Neural Populations
• Machine learning: Trends, perspectives, and prospects
• Phase- and Center-Manifold Reductions for Large Populations of Coupled Oscillators with Application to Non-Locally Coupled Systems
• Dynamics and control of a class of underactuated mechanical systems
• Deterministic Nonperiodic Flow
• Introduction to Applied Nonlinear Dynamical Systems and Chaos
• On the Phase Reduction and Response Dynamics of Neural Oscillator Populations
• Sparse identification of nonlinear dynamics for model predictive control in the low-data limit
• The geometry of biological time.

This page was built for publication: Supervised learning algorithms for controlling underactuated dynamical systems