martindoff
Follow
-
DC-NN-MPC Public
Computationally tractable learning-based nonlinear tube MPC using difference of convex neural network dynamic approximation
-
Radial-basis-TMPC Public
Learning-based robust tube based MPC of nonlinear systems via difference of convex radial basis functions
-
VTOL-aircraft-MPC Public
Computationally efficient nonlinear robust tube-based MPC algorithm for VTOL aircraft in urban air mobility scenarios.
-
DC-Deep-Neural-Network Public
Learn dynamical systems as a difference of convex functions (DC) using a feedforward Neural Network (NN) architecture with DC structure. The resulting model learns the dynamics f in DC form as foll…
deep-learning neural-network feedforward-neural-network convex difference-of-convex-functions dc-program dc-decompositionPython UpdatedOct 21, 2023 -
DC-RNN Public
Learn dynamical systems as a difference of convex functions (DC) using a Recurrent Neural Network (RNN) architecture with DC structure. Example on the coupled tank problem.
deep-learning neural-network recurrent-neural-networks dynamical-systems difference-of-convex-function difference-of-convex-functions dc-programPython UpdatedOct 20, 2023 -
Cpp-linear-algebra-library Public
A lightweight linear algebra library for rapid prototyping of C++ applications.
C++ UpdatedAug 15, 2023 -
-
bare-metal-stm32h743-HIL Public
A Hardware-In-the-Loop (HIL) simulation example for the stm32h743 microcontroller. The chip is interfaced with Simulink running on a laptop via a serial link (UART). To demonstrate the HIL simulati…
-
freeRTOS-stm32h7-Cpp Public
A simple blink program showing the use of freeRTOS with C++ for a stm32h743 microcontroller
-
bare-metal-stm32h7-uart Public
This example shows how to establish a serial communication between the stm32h743 microcontroller and a laptop using the UART peripherals.
-
bare-metal-stm32h7 Public
A bare metal implementation of a blinky example for the stm32h743 with a simple makefile and without using any HAL libraries
-
DC-TMPC Public
DC-TMPC: A tube-based MPC algorithm for systems that can be expressed as a difference of convex functions.