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

TL;DR

This paper introduces RAMP-Net, a physics-informed neural network-based robust adaptive MPC for quadrotors, which improves trajectory tracking accuracy under uncertainties by combining physics-based and data-driven learning.

Contribution

RAMP-Net uniquely integrates physics-informed neural networks with MPC to handle both parametric and non-parametric uncertainties in quadrotor control.

Findings

Significant reduction in tracking errors (up to 61.5%) compared to state-of-the-art methods.

Effective handling of uncertainties improves quadrotor trajectory tracking.

Demonstrated robustness and adaptability in simulated environments.

Abstract

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% to 43.2% and 8.04% to 61.5% reduction in tracking errors for speeds ranging from 0.5 to 1.75 m/s compared to two SOTA regression based MPC methods.