Exploiting Dynamic Similarity for Direct Transfer of MPC-based Policies

An Efficient Differentiable Model Predictive Control for UAV Attitude Regulation

A novel learning algorithm called Efficient Differentiable Model Predictive Control (EDMPC) is proposed, which significantly improves computational efficiency by integrating an optimization-driven inference step within the backpropagation process.

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

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

This letter develops a time-parallel policy-gradient framework for discrete-time nonlinear control-affine systems and proves that, for any finite horizon T, the state solver recovers the exact trajectory from any initialization in at most T iterations.

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

Numerical results demonstrate that HypEMBER consistently improves training stability and sample efficiency, while achieving superior robustness to uncertainties affecting both the system dynamics and the available observations, in comparison with state-of-the-art RL methods.

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...