Learning Based Model Predictive Control for Quadcopters with Dual Gaussian Process

TL;DR

This paper introduces a dual Gaussian process model predictive control method for quadcopters, combining long-term and short-term GPs to enhance trajectory tracking and adapt online efficiently.

Contribution

It proposes a novel DGP structure with recursive online updates, improving real-time adaptation and control performance of quadcopters.

Findings

Enhanced trajectory tracking performance demonstrated in simulations

Efficient online model updates improve adaptability

Dual GP structure balances long-term memory and short-term compensation

Abstract

An important issue in quadcopter control is that an accurate dynamic model of the system is nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian process (GP) based estimation is an effective tool to learn unknown dynamics from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control strategy that improves the performance of a quadcopter during trajectory tracking. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learned knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. Furthermore, a novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation in an efficient way. Numerical simulations are used to demonstrate the effectiveness of the proposed strategy.