Stochastic nonlinear model predictive control (SNMPC) allows to directly take model uncertainty into account, e.g., by including probabilistic chance constraints. This paper proposes linear-regression Kalman filtering...
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Stochastic nonlinear model predictive control (SNMPC) allows to directly take model uncertainty into account, e.g., by including probabilistic chance constraints. This paper proposes linear-regression Kalman filtering to perform high-accuracy propagation of mean and covariance information for the nonlinear system dynamics in a tractable approximation of the stochastic optimal control problem. In addition, a tailored adjoint-based sequential quadratic programming (SQP) algorithm is presented to considerably reduce the computational cost and allow a real-time implementation of the resulting SNMPC. The prediction accuracy and control performance of the proposed approach are illustrated on a vehicle control application subject to external disturbances, while highlighting a worst-case computation time of 10 ms for SNMPC which is close to that of deterministic NMPC for this particular case study. Copyright (C) 2021 The Authors.
This paper introduces the Adaptive Multi-Layered Non-Terrestrial Network (AMLT-NTN), an architecture that integrates satellite, High Altitude Platform Stations (HAPS), and Unmanned Aerial Vehicles (UAVs). It leverages...
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ISBN:
(纸本)9798350391961;9798350391954
This paper introduces the Adaptive Multi-Layered Non-Terrestrial Network (AMLT-NTN), an architecture that integrates satellite, High Altitude Platform Stations (HAPS), and Unmanned Aerial Vehicles (UAVs). It leverages a combination of Free-Space Optical (FSO) and Radio Frequency (RF) communications, tailored for specific operational altitudes to enhance connectivity in remote and disaster-stricken regions. The AMLT-NTN tackles the complexities of dynamic power allocation and link selection by incorporating real-time optimization algorithms. This significantly boosted the network's robustness and adaptability to environmental challenges and demand fluctuations. Simulations in OMNeT++ highlighted a quantifiable enhancement, with up to a 30% increase in throughput and a 40% decrease in latency, outstripping conventional NTN. The AMLT-NTN architecture demonstrates unparalleled resilience, consistently delivering high service levels across various conditions. Looking ahead, this research paves the way for integrating emerging communication technologies and scaling the architecture for widespread adoption. The proposed AMLT-NTN offers transformative solutions for rural connectivity and rapid disaster response, thus poised to impact global digital inclusion.
Stochastic nonlinear model predictive control (SNMPC) allows to directly take model uncertainty into account, e.g., by including probabilistic chance constraints. This paper proposes linear-regression Kalman filtering...
详细信息
Stochastic nonlinear model predictive control (SNMPC) allows to directly take model uncertainty into account, e.g., by including probabilistic chance constraints. This paper proposes linear-regression Kalman filtering to perform high-accuracy propagation of mean and covariance information for the nonlinear system dynamics in a tractable approximation of the stochastic optimal control problem. In addition, a tailored adjoint-based sequential quadratic programming (SQP) algorithm is presented to considerably reduce the computational cost and allow a real-time implementation of the resulting SNMPC. The prediction accuracy and control performance of the proposed approach are illustrated on a vehicle control application subject to external disturbances, while highlighting a worst-case computation time of 10 ms for SNMPC which is close to that of deterministic NMPC for this particular case study.
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