Abstract
Neural ordinary differential equations (Neural ODEs) define continuous time dynamical systems with neural networks. The interest in their application for modelling has sparked recently, spanning hybrid system identification problems and time series analysis. In this work we propose the use of a neural control policy capable of satisfying state and control constraints to solve nonlinear optimal control problems. The control policy optimization is posed as a Neural ODE problem to efficiently exploit the availability of a dynamical system model. We showcase the efficacy of this type of deterministic neural policies in the controlled Van der Pol system. This approach represents a practical approximation to the intractable closed-loop solution of nonlinear control problems.
Ilya Orson Sandoval
Knowledge-driven Autonomous Systems - Neural ODEs and Reinforcement Learning
I am a PhD candidate at Imperial College London, where my research focuses on the intersection of reinforcement learning, differentiable programming and nonlinear optimal control. Curiosity driven, usually by applied mathematics and computer simulations with applications over multiple fields! Previously, I worked in data science and software engineering within the energy and food industries in Mexico. I have a background in theoretical and computational physics.
Dr. Ehecatl Antonio del Rio Chanona
Principal Investigator of OptiML
Antonio del Rio Chanona is the head of the Optimisation and Machine Learning for Process Systems Engineering group based in thee Department of Chemical Engineering, as well as the Centre for Process Systems Engineering at Imperial College London. His work is at the forefront of integrating advanced computer algorithms from optimization, machine learning, and reinforcement learning into engineering systems, with a particular focus on bioprocess control, optimization, and scale-up. Dr. del Rio Chanona earned his PhD from the Department of Chemical Engineering and Biotechnology at the University of Cambridge, where his outstanding research earned him the prestigious Danckwerts-Pergamon award for the best PhD dissertation of 2017. He completed his undergraduate studies at the National Autonomous University of Mexico (UNAM), which laid the foundation for his expertise in engineering.