2021/12/24 by Santiago Sanchez-Escalonilla, Sanchez-Escalonilla, Santiago, Rodolfo Reyes‐Báez +3 · 2 citations
Biochemistry, Genetics and Molecular Biology · Engineering · Physics and Astronomy · #ATP Synthase and ATPases Research #Control and Stability of Dynamical Systems #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Optimization and Control (math.OC) #Robotics (cs.RO) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2112.12999
openalex publication_date 2021/12/24 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
In this work we exploit the universal approximation property of Neural Networks (NNs) to design interconnection and damping assignment (IDA) passivity-based control (PBC) schemes for fully-actuated mechanical systems in the port-Hamiltonian (pH) framework. To that end, we transform the IDA-PBC method into a supervised learning problem that solves the partial differential matching equations, and fulfills equilibrium assignment and Lyapunov stability conditions. A main consequence of this, is that the output of the learning algorithm has a clear control-theoretic interpretation in terms of passivity and Lyapunov stability. The proposed control design methodology is validated for mechanical systems of one and two degrees-of-freedom via numerical simulations.