A Virtual Structure Formation Guidance Strategy for Multi-Parafoil Systems

Self Cite

The powered parafoil system is obtained by adding the propeller thrust to the unpowered parafoil system, and has coupling and nonlinear characteristics, which make its precise control more difficult than that of the unpowered parafoil system. To achieve the trajectory tracking control of the powered parafoil system in the field of precision airdrop, a mathematical model of the 6-DOF of the powered parafoil system is established first. Then, a new trajectory tracking strategy is proposed, which can overcome the limitations of the traditional guidance-based trajectory tracking strategy. We design lateral, longitudinal, and velocity controllers on the basis of the motion characteristics of the powered parafoil system. Then, we adopt the widely used PID control strategy in engineering. In response to the difficult of the PID controller parameter tuning of the powered parafoil system, we achieve the PID controller parameter tuning with the ecosystem particle swarm optimization (ESPSO) of the swarm intelligence optimization algorithm. The effectiveness of the algorithm is verified by simulation experiments. Results show that the proposed algorithm can obtain high trajectory tracking accuracy even when a deviation in the initial state and a random gust wind disturbance in the outside world occur regardless of the method of the multiphase homing or the optimal control homing adopted. The proposed trajectory tracking strategy also has strong robustness and adaptability.

“…In addition to the propeller thrust effect, the powered parafoil system can also be controlled by the symmetrical flaps deflection (27) where the expression of L is…”

Section: B System Dynamicsmentioning

confidence: 99%

6-DOF Modeling and 3D Trajectory Tracking Control of a Powered Parafoil System

Zhao

Yao

et al. 2020

Self Cite

“…Owing to its large payload, long endurance and simple operation, UPPV is considered safer and longer-range than the UAV. With the rapid development of AI technology, UPPV can be used in military and civil fields in the future [1]- [2]. For example, equipment and supplies can be accurately delivered to the target location over a long distance, especially for large-scale disaster rescue and escort missions [3].…”

Section: Introductionmentioning

confidence: 99%

Vision-Assisted Landing Method for Unmanned Powered Parachute Vehicle Based on Lightweight Neural Network

Zhang

et al. 2021

With the development of airdrop technology, the intelligence degree of unmanned powered parachute vehicles (UPPVs) need to be improved. To achieve the accurate landing of UPPVs in complex environments, a landing runway recognition model based on a deep learning algorithm is trained and five actual flight tests are conducted. A six-degree-of-freedom (6-DOF) mathematical model of an unmanned powered parachute vehicle is established, and a landing runway offset controller is designed. The lightweight landing runway recognition model was trained by combining the YOLOv4 framework and the lightweight neural network MobileNet-V3 (Large) and validated in various scenarios. The runway recognition model was transplanted into the airborne image processor, and an unmanned powered parachute vehicle test platform was built for actual flight testing. The test results showed that the comprehensive accuracy of the runway recognition was 97.81% during visual landing and the offset correction was completed within 15s. INDEX TERMS Unmanned powered parachute vehicle; visual landing; YOLOv4; lightweight neural network; offset controller.

“…Some artificial intelligence technologies have also been used to solve the LFC problems of MASs, such as reinforcement learning [21] and neural networks [22]. A formation guidance strategy based on a virtual structure and an optimal multiphase homing trajectory planning method by using simulated annealing algorithm are proposed in [23].…”

Section: Introductionmentioning

confidence: 99%

Group Leader-Following Consensus Control for Heterogeneous Multi-Agent Systems With Time Delays

Tan

Liang

2021

In this paper, the problem of group leader-following consensus is discussed for high-order heterogeneous multi-agent systems(HMASs) with multiple leaders and time delays. The four situations are considered: autonomous leaders, active leaders with controllers, and accepting their own information with/without time delays. Different protocols are designed for different situations. Under the corresponding protocols, HMASs can achieve group leader-following consensus without the limitation of in-degree balance. And the networked predictive scheme(NPS) actively compensating for time delays is used to predict current information of agents. Besides, the results are extended to multiple groups case. Finally, the simulation results show that, based on the NPS, the state trajectory of each agent in the time-delay case is very close to that in no time-delay case.