Publication

Neural Process Model Predictive Control

2026

Abstract

Neural Processes (NPs) can model distributions over functions, like Gaussian Processes, but with the great flexibility and evaluation speed of neural networks. They can be trained purely on data and without prior knowledge. Yet, their architecture allows for a certain degree of interpretability and modular use. We use an NP to model the dynamics in a nonlinear Model Predictive Control (MPC) scheme. After meta-training the NP on data from various system instances, deployment to an unseen system is highly efficient in terms of data and computation. To demonstrate the effectiveness of our NP-MPC scheme for real-time adaptive control, we deploy it to a physical two-axis pendulum experiment requiring short sampling times.

Official source

https://infoscience.epfl.ch/entities/publication/60b437a9-7fc3-458f-ab40-d4ce702d5de2

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2026

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/60b437a9-7fc3-458f-ab40-d4ce702d5de2

About this result

Related concepts (32)

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Recurrent neural network

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

Neural network

A neural network can refer to a neural circuit of biological neurons (sometimes also called a biological neural network), a network of artificial neurons or nodes in the case of an artificial neural network. Artificial neural networks are used for solving artificial intelligence (AI) problems; they model connections of biological neurons as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed.

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