45. Interpretable Machine Learning Approach for Nonlinear Control
Invited abstract in session MB-11: Optimal and stochastic optimal control 1, stream Optimal and stochastic optimal control.
Monday, 10:30-12:30Room: B100/5017
Authors (first author is the speaker)
| 1. | Edmondo Minisci
|
| Mechanical and Aerospace Engineering, University of Strathclyde | |
| 2. | Giulio Avanzini
|
| Engineering for Innovation, Università del Salento |
Abstract
The work is about an interpretable machine learning approach for control based on genetic programming with integrated search for continuous coefficients. The method can discover compact, human-readable control laws by combining symbolic expressions with embedded parameter tuning, thus bridging the gap between black-box learning and classical control. The main aim of the work is to demonstrate its potential on textbook cases, including standard systems (e.g., nonlinear oscillators, inverted pendulum) and provide initial results on aeronautical applications, such as stability augmentation of unstable, highly manoeuvrable aircraft. The obtained solutions maintain performance comparable to conventional controllers, while preserving transparency and ease of analysis, and the method enables the treatment of nonlinear systems without reliance on gain scheduling.
Keywords
- Optimal control and applications
- Applications of continuous optimization
- Stochastic optimization
Status: accepted
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