Exploiting Dynamic Similarity for Direct Transfer of MPC-based Policies

An Efficient Differentiable Model Predictive Control for UAV Attitude Regulation

Data-driven learning optimization, which considers optimization as a means to perform end-to-end learning, is an emerging methodology used to solve large-scale learning and continuous control tasks. These methods provide mathematically tractable solutions with inherent interpretability, but their training can be ineffi...

Inverse optimal control of general nonlinear systems based on adaptive dynamic programming

Parallel Policy-Gradient Methods for Parameter Optimization of Nonlinear Feedback Controllers

Structured feedback controllers provide rigorous stability guarantees, but often require manual parameter tuning to achieve good closed-loop performance. Policy-gradient methods offer a systematic approach to parameter optimization; however, conventional gradient evaluation requires sequential forward state rollout and...

Data-Driven Control Methods for Linear Discrete-Time Singularly Perturbed Systems

This paper presents a synergistic control strategy for discrete-time singularly perturbed systems, where data-driven learning is seamlessly combined with LMI-based synthesis, thereby offering an effective new approach for controlling discrete-time singularly perturbed systems in complex engineering environments.

HypEMBER: Hypernetwork-based Ensemble for Robust Policy Learning of Parametrized Dynamical Systems

In this work we investigate reinforcement learning (RL) as a framework for the robust control of parametrized dynamical systems in presence of measurements and model uncertainties. High-dimensional state spaces, expensive numerical solvers, the partial knowledge of the governing equations, and the dependence on physica...

Engineering Applications of Artificial Intelligence

A systematic assessment framework is presented that compares four prominent DRL controllers with a classical control baseline across a diverse set of applied control problems, including non-minimum phase dynamics, flexible mechanical systems, nonlinear marine control, and aerial robotics, and clarifies the trade-offs b...