Control of dynamical systems with neural networks
By: Lucas Böttcher
Potential Business Impact:
Teaches computers to control complex machines.
Control problems frequently arise in scientific and industrial applications, where the objective is to steer a dynamical system from an initial state to a desired target state. Recent advances in deep learning and automatic differentiation have made applying these methods to control problems increasingly practical. In this paper, we examine the use of neural networks and modern machine-learning libraries to parameterize control inputs across discrete-time and continuous-time systems, as well as deterministic and stochastic dynamics. We highlight applications in multiple domains, including biology, engineering, physics, and medicine. For continuous-time dynamical systems, neural ordinary differential equations (neural ODEs) offer a useful approach to parameterizing control inputs. For discrete-time systems, we show how custom control-input parameterizations can be implemented and optimized using automatic-differentiation methods. Overall, the methods presented provide practical solutions for control tasks that are computationally demanding or analytically intractable, making them valuable for complex real-world applications.
Similar Papers
Neural Predictive Control to Coordinate Discrete- and Continuous-Time Models for Time-Series Analysis with Control-Theoretical Improvements
Machine Learning (CS)
Makes computer predictions more accurate over time.
Machine Learning and Control: Foundations, Advances, and Perspectives
Optimization and Control
Explains how AI learns and creates new things.
Stable neural networks and connections to continuous dynamical systems
Numerical Analysis
Makes computer brains harder to trick.