Benjamin Karg
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do-mpc: Towards FAIR nonlinear and robust model predictive control Open
Over the last decades, model predictive control (MPC) has shown outstanding performance for control tasks from various domains. This performance has further improved in recent years with advanced MPC schemes for nonlinear systems under unc…
Probabilistic performance validation of deep learning‐based robust NMPC controllers Open
Solving nonlinear model predictive control problems in real time is still an important challenge despite of recent advances in computing hardware, optimization algorithms and tailored implementations. This challenge is even greater when un…
Stability and feasibility of neural network-based controllers via output range analysis Open
Neural networks can be used as approximations of several complex control schemes such as model predictive control. We show in this paper which properties deep neural networks with rectifier linear units as activation functions need to sati…
Efficient Representation and Approximation of Model Predictive Control Laws via Deep Learning Open
We show that artificial neural networks with rectifier units as activation functions can exactly represent the piecewise affine function that results from the formulation of model predictive control (MPC) of linear time-invariant systems. …
Deep Learning-Based Model Predictive Control for Resonant Power Converters Open
Resonant power converters offer improved levels of efficiency and power\ndensity. In order to implement such systems, advanced control techniques are\nrequired to take the most of the power converter. In this context, model\npredictive con…
A deep learning-based approach to robust nonlinear model predictive control Open
Dealing with uncertainties is one of the most challenging issues that prevent nonlinear model predictive control (NMPC) from being a widespread reality. Many different robust schemes have been presented recently, such as multi-stage NMPC, …