A control architecture to coordinate energy management with trajectory tracking control for fuel cell/battery hybrid unmanned aerial vehicles

Liu, Huiying; Yao, Yongming; Wang, Jie; Qin, Yutong; Li, Tian Yu

doi:10.1016/j.ijhydene.2022.03.036

Cited by 20 publications

(6 citation statements)

References 39 publications

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“…By putting Equations ( 9) and (11) into System (7), we can express the actual system with e k as follows:…”

Section: Tube Mpc Theorymentioning

confidence: 99%

“…Autonomous vehicles can provide more excellent safety benefits and improve traffic efficiency, and they are assumed to be an efficient solution for traffic safety and efficiency issues [6][7][8]. The control layer for automatic vehicles has a significant effect on traffic safety and efficiency issues, and many scholars have devoted themselves to creating effective control methods for automatic vehicles [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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A Tube Linear Model Predictive Control Approach for Autonomous Vehicles Subjected to Disturbances

Chen,

Tian

2024

Applied Sciences

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The path tracking performance of autonomous vehicles is degraded by common disturbances, especially those that affect the safety of autonomous vehicles (AVs) in obstacle avoidance conditions. To improve autonomous vehicle tracking performances and their computational efficiency when subjected to common disturbances, this paper proposes a tube linear model predictive controller (MPC) framework for autonomous vehicles. A bicycle vehicle dynamics model is developed and employed in the tube MPC control design in the proposed framework. A robust invariant set is calculated with an efficient linear programming (LP) method, and it is used to guarantee that the constraints are satisfied under common disturbance conditions. The results show that the computational cost of robust positively invariant sets that are constructed by the LP method is much less than that obtained by the traditional method. In addition, all the trajectories of the tube linear MPC successfully avoided obstacles when under disturbance conditions, but only about 80% of the trajectories obtained with the traditional MPC successfully avoided obstacles under disturbance conditions. The proposed framework is effective.

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“…By putting Equations ( 9) and (11) into System (7), we can express the actual system with e k as follows:…”

Section: Tube Mpc Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Tube Linear Model Predictive Control Approach for Autonomous Vehicles Subjected to Disturbances

Chen,

Tian

2024

Applied Sciences

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“…Liu [132] proposed an energy management strategy based on online DP and hierarchical model predictive control(HMPC). The simulation results revealed that compared to common energy management strategies, DP and hierarchical MPC can increase the flight time of fuel cell drones by 2.69 and 1.27%, respectively.…”

Section: Energy Management Strategymentioning

confidence: 99%

A Review on Key Technologies and Developments of Hydrogen Fuel Cell Multi-rotor Drones

Shen,

Liu,

Zhu

et al. 2024

Preprint

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Multi-rotor drones, a kind of unmanned equipment which is widely used in military, commercial consumption and other fields, has been developed very rapidly in recent years. However, their short flight time has hindered the expansion of their application range. This can be addressed by utilizing hydrogen fuel cells, which exhibit high energy density, strong adaptability to ambient temperature, and no pollution emissions, as the power source. Accordingly, the application of hydrogen fuel cells as the power source in multi-rotor drones is a promising technology that has attracted significant research attention. This paper summarizes the development process of hydrogen fuel cell mul-ti-rotor drones and analyzes the key obstacles that need to be addressed for the further development of hydrogen fuel cell multi-rotor drones, including structural lightweight, hydrogen storage methods, energy management strategies. Additionally, prospects for the future development of hydrogen fuel cell multi-rotor drones are presented.

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“…The results demonstrate that their proposed S-PMP approach can effectively save hydrogen use and keep the battery SOC. Liu et al [19] implemented an online dynamic programming EMS and hierarchical model prediction control for a hybrid FC/battery energy storage system-driven UAV. The results illustrate that the control effect of online solution of dynamic programming is significantly better than that of ordinary EMS, which improves the endurance of the UAV.…”

Section: Introductionmentioning

confidence: 99%

Minimum hydrogen consumption‐based energy management strategy for hybrid fuel cell unmanned aerial vehicles using direction prediction optimal foraging algorithm

Quan

et al. 2023

Fuel Cells

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In hybrid energy storage systems of fuel cell unmanned aerial vehicles (UAVs), achieving energy management while minimizing hydrogen consumption is the main goal for economic aspects and endurance enhancement. The external energy maximization strategy (EEMS) and the equivalent consumption minimization strategy (ECMS) are commonly used energy management strategies. However, they use a gradient descent approach, which converges slowly and does not guarantee the optimal solution. Thus, this paper proposes an optimization method based on a direction prediction optimal foraging algorithm (OFA/DP), which has the advantages of high optimization capability and simple parameter definition. In this study, the hybrid energy storage system comprises fuel cells and lithium‐ion batteries for powering UAVs. To verify the validity of the proposed strategy, it is compared with rule‐based and optimized methods of state machine control, fuzzy logic control based on frequency separation, ECMS, EEMS, and genetic algorithm. The obtained results confirm the superiority of the proposed OFA/DP‐based EEMS method with an efficiency of 88.65% and a minimum hydrogen consumption of 19.06 g. Furthermore, it achieves optimal power distribution and leads to 38.62% minimization in hydrogen consumption.

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A control architecture to coordinate energy management with trajectory tracking control for fuel cell/battery hybrid unmanned aerial vehicles

Cited by 20 publications

References 39 publications

A Tube Linear Model Predictive Control Approach for Autonomous Vehicles Subjected to Disturbances

A Tube Linear Model Predictive Control Approach for Autonomous Vehicles Subjected to Disturbances

A Review on Key Technologies and Developments of Hydrogen Fuel Cell Multi-rotor Drones

Minimum hydrogen consumption‐based energy management strategy for hybrid fuel cell unmanned aerial vehicles using direction prediction optimal foraging algorithm

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