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 …

This paper discusses the introduction of an integrated Posit Processing Unit (PPU) as an
alternative to Floating-point Processing Unit (FPU) for Deep Neural Networks (DNNs) in …

This manuscript presents a new fuzzy approach applied to Model Predictive Control (MPC).
We propose to re-interpret the table of IF-THEN rules from an explicit MPC solution as an …

In the explicit model predictive control (EMPC), memory increases exponentially with the
number of states, inputs, constraints, and prediction horizons; this often limits its applicability …

In this paper we show how to synthesize simple explicit MPC controllers based on
approximate dynamic programming. Here, a given MPC optimization problem over a finite …

This paper proposes a framework for the high-accuracy, low-precision, and memory-efficient
embedded model predictive control (MPC) using the posit™ numbers and its …

In this article, a robust nonlinear model predictive control (NMPC) scheme with two control
loops is considered and its real‐time execution is guaranteed for a predefined sampling …

Efficient approximations of predictive controllers using neural networks can enable the
deployment of highperformance controllers virtually everywhere. Such approximations can …

This paper concentrates on the fragility margins of discrete-time Piecewise Affine (PWA)
closed-loop dynamics. Starting from the case where the nominal trajectories are controlled …

For explicit model predictive control (EMPC), off-line pre-computed optimal feedback laws
need to be stored in a look-up table for on-line evaluation. The need for memory to store the …