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Found 7 papers, 1 papers with code
Mapping back and forth between model predictive control and neural networks
Model predictive control (MPC) for linear systems with quadratic costs and linear constraints is shown to admit an exact representation as an implicit neural network.
Tuning the feedback controller gains is a simple way to improve autonomous driving performance
Often, the feedback control gains are simply passed from paper to paper with little re-tuning taking place, even though the changes to the neural networks can alter the vehicle's closed loop dynamics.
Bounding the difference between model predictive control and neural networks
There is a growing debate on whether the future of feedback control systems will be dominated by data-driven or model-driven approaches.
Cell-Level State of Charge Estimation for Battery Packs Under Minimal Sensing
This manuscript presents an algorithm for individual Lithium-ion (Li-ion) battery cell state of charge (SOC) estimation in a large-scale battery pack under minimal sensing, where only pack-level voltage and current are measured.
Reduced-Order Neural Network Synthesis with Robustness Guarantees
The reduced-order neural network's weights and biases are generated from a convex semi-definite programme that minimises the worst-case approximation error with respect to the larger network.
Structural Identifiability of a Pseudo-2D Li-ion Battery Electrochemical Model
From an inspection of the impedance function, it was shown that this model is uniquely parametrised by 21 parameters, being combinations of the electrochemical parameters like the conductivities and diffusion coefficients.
Robust error bounds for quantised and pruned neural networks
With the rise of smartphones and the internet-of-things, data is increasingly getting generated at the edge on local, personal devices.
