Optimal Energy Shaping via Neural Approximators

DOI10.1137/21M1414279OpenAlexW3118584630MaRDI QIDQ5868542

Atsushi Yamashita, Federico Califano, Hajime Asama, Michael Poli, Stefano Massaroli, Jinkyoo Park

Publication date: 21 September 2022

Published in: SIAM Journal on Applied Dynamical Systems (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2101.05537

zbMATH Keywords

stabilitypassivity-based controlneural approximatorsport-Hamiltonian systems

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Input-output approaches in control theory (93D25)

Related Items (1)

Discovering efficient periodic behaviors in mechanical systems via neural approximators

Uses Software

• TensorFlow
• DiffSharp
• PyTorch
• Adam
• GitHub
• JAX
• FFJORD
• TorchDyn
• NeuralCDE
• torchcde
• SymODEN

Cites Work

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• Local convergence of the steepest descent method in Hilbert spaces
• Control of interactive robotic interfaces. A port-Hamiltonian approach
• A family of embedded Runge-Kutta formulae
• L\(_2\) gain and passivity techniques in nonlinear control.
• Optimal control of port-Hamiltonian systems: a continuous-time learning approach
• Port-Hamiltonian Systems in Adaptive and Learning Control: A Survey
• Modeling and Control of Complex Physical Systems
• Passivity, feedback equivalence, and the global stabilization of minimum phase nonlinear systems
• Energy-Aware Robotics
• Control by Interconnection and Standard Passivity-Based Control of Port-Hamiltonian Systems
• A Stochastic Approximation Method
• Modeling and IPC control of interactive mechanical systems. A coordinate-free approach

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