Physics-guided neural network and GPU-accelerated nonlinear model predictive control for quadcopter.

This paper presents a physics-guided residual recurrent neural network (PGRRNN) and graphics processing unit (GPU)-accelerated model predictive control (MPC) framework to combat two specific challenges in artificial neural network (ANN)-based nonlinear MPC of high-rate dynamics systems, i.e., low control latency and insufficient model accuracy or generalization ability. Different from traditional ANN models, PGRRNN utilizes approximate physics-based (PB) models (with parameter uncertainty) as a backbone to impose physical constraints/guidance for future state prediction, and reconciles the difference between PB model approximation and data collected from actual systems by propagating their residuals through a multilayer recurrent neural network, hence improving its accuracy and generalization and alleviating data volume requirement. For computing acceleration, both PGRRNN and particle swarm optimization (PSO) are implemented on a GPU platform to make use of its massive parallel processing threads. Numerical experiments for MPC trajectory tracking of a quadcopter are used to examine accuracy and robustness of PGRRNN, and its performance is compared with other ANN models and approximate PB models. PGRRNN outperforms the other models in both ideal and realistic environments, exhibiting 2–3 times lower tracking error than the pure data-driven model. Furthermore, it is demonstrated that GPU-based PSO is able to synthesize control signals at a rate of greater than 50 Hz and can be a promising approach for ANN-based nonlinear MPC. [ABSTRACT FROM AUTHOR]