RAMP-Net: A Robust Adaptive MPC for Quadrotors via Physics-informed Neural Network

by   Sourav Sanyal, et al.

Model Predictive Control (MPC) is a state-of-the-art (SOTA) control technique which requires solving hard constrained optimization problems iteratively. For uncertain dynamics, analytical model based robust MPC imposes additional constraints, increasing the hardness of the problem. The problem exacerbates in performance-critical applications, when more compute is required in lesser time. Data-driven regression methods such as Neural Networks have been proposed in the past to approximate system dynamics. However, such models rely on high volumes of labeled data, in the absence of symbolic analytical priors. This incurs non-trivial training overheads. Physics-informed Neural Networks (PINNs) have gained traction for approximating non-linear system of ordinary differential equations (ODEs), with reasonable accuracy. In this work, we propose a Robust Adaptive MPC framework via PINNs (RAMP-Net), which uses a neural network trained partly from simple ODEs and partly from data. A physics loss is used to learn simple ODEs representing ideal dynamics. Having access to analytical functions inside the loss function acts as a regularizer, enforcing robust behavior for parametric uncertainties. On the other hand, a regular data loss is used for adapting to residual disturbances (non-parametric uncertainties), unaccounted during mathematical modelling. Experiments are performed in a simulated environment for trajectory tracking of a quadrotor. We report 7.8 ranging from 0.5 to 1.75 m/s compared to two SOTA regression based MPC methods.


page 1

page 6


KNODE-MPC: A Knowledge-based Data-driven Predictive Control Framework for Aerial Robots

In this work, we consider the problem of deriving and incorporating accu...

Safe and Fast Tracking Control on a Robot Manipulator: Robust MPC and Neural Network Control

Fast feedback control and safety guarantees are essential in modern robo...

Extreme Theory of Functional Connections: A Physics-Informed Neural Network Method for Solving Parametric Differential Equations

In this work we present a novel, accurate, and robust physics-informed m...

Iterative Semi-parametric Dynamics Model Learning For Autonomous Racing

Accurately modeling robot dynamics is crucial to safe and efficient moti...

Data Set Description: Identifying the Physics Behind an Electric Motor – Data-Driven Learning of the Electrical Behavior (Part II)

A data set was recorded to evaluate different methods for extracting mat...

Constrained Physics-Informed Deep Learning for Stable System Identification and Control of Linear Systems

This paper presents a novel data-driven method for learning deep constra...

Solving Inventory Management Problems with Inventory-dynamics-informed Neural Networks

A key challenge in inventory management is to identify policies that opt...

Please sign up or login with your details

Forgot password? Click here to reset