Formation Control of Multiple Unmanned Aerial Vehicles by Event-Triggered Distributed Model Predictive Control

Cai, Zhihao; Zhou, Hui; Zhao, Jiang; Wu, Kun; Wang, Yingxun

doi:10.1109/access.2018.2872529

Cited by 50 publications

(27 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The control layer controls the UAV to fly according to the planned path, which is the basis of UAV cluster research. By establishing control system frameworks [77]- [80] and designing corresponding controllers [81]- [83]], it is possible to solve the reconstruction of different types of drones in cluster formations [84]- [89], cluster search And tracking [93]- [95]and cluster anti-collision and other aspects [96]- [98]. An overall summary of the classification of the control layer is given in TABLE 4.…”

Section: Control Layermentioning

confidence: 99%

“…Based on the online MPC method and disturbance observer, we combined the formal algorithm with the flight control structure, and implemented a complete framework on the low-power computing unit. Based on the traditional MPC method, [89] presents an event-triggered model predictive control (MPC) scheme, which takes into account the prediction state error and the convergence of the cost function, and at the same time for each local level optimal control problem, developed a no-fly zone strategy based on safety distance and integrated it into the local cost function to make it more computationally efficient.…”

Section: C) Flight and Formation Control Technologymentioning

confidence: 99%

“…Combining the MPC method with the disturbance observer, a complete framework for a low-power airborne computing unit is realized. [89] Event-triggered MPC Model…”

Section: D) Collaborative Search and Tracking Technologymentioning

confidence: 99%

See 2 more Smart Citations

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

View full text Add to dashboard Cite

The dynamic uncertain environment and complex tasks determine that the unmanned aerial vehicle (UAV) system is bound to develop towards clustering, autonomy，and intelligence. In this paper, we present a comprehensive survey of UAV swarm intelligence from the hierarchical framework perspective. Firstly, we review the basics and advances of UAV swarm intelligent technology. Then we look inside to investigate the research work by classifying UAV swarm intelligence research into five layers, i.e., decision-making layer, path planning layer, control layer, communication layer, and application layer. Furthermore, the relationship between each level is explicitly illustrated, and the research trends of each layer are given. Finally, limitations and possible technology trends of swarm intelligence are also covered to enable further research interests. Through this in-depth literature review, we intend to provide novel insights into the latest technologies in UAV swarm intelligence.

show abstract

Section: Control Layermentioning

confidence: 99%

Section: C) Flight and Formation Control Technologymentioning

confidence: 99%

See 1 more Smart Citation

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

View full text Add to dashboard Cite

show abstract

“…In spite of the effectiveness of MPC in UAV formation flight, [9][10][11] it is hard for conventional MPC to deal with the disturbances and uncertainties, since the feedback linearization is no longer exact, then the prediction model is less accurate and possibly leads to the wrong predictions and even instability. In order to overcome the drawbacks, an adaptive MPC strategy is proposed.…”

Section: Adaptive Mpc Using Esomentioning

confidence: 99%

“…8 As mentioned above, MPC has been researched as one of the most popular advanced control schemes applied in UAV formation flight recently. [9][10][11] It is known that the MPC strategy uses the model to predict the future behaviors, then an optimal control decision can be made based on an optimization of the predictions. This "foresee" feature of MPC makes it a potential control approach for UAV formation flight.…”

Section: Introductionmentioning

confidence: 99%

Adaptive model predictive control with extended state observer for multi‐UAV formation flight

Zhang

Sun

Deng

2020

Adaptive Control & Signal

View full text Add to dashboard Cite

This article studies the adaptive model predictive control with extended state observers (ESO) to deal with multiple unmanned aerial vehicles formation flight in presence of external disturbances and system uncertainties. Specifically, to deal with the mismatch of predictive model caused by external disturbances and system uncertainties, ESOs are introduced to estimate the lumped disturbances, where the ultimately bounded property of observer system can be guaranteed by using the Lyapunov stability theorem. With these observations, the distributed adaptive model predictive controller is designed to achieve trajectory tracking and disturbance rejection simultaneously for multiple unmanned aerial vehicles, as well as taking the state and input saturation into account. Moreover, the stability of proposed model predictive controller is analyzed. Finally, the simulation examples are provided to illustrate the validity of the proposed control scheme.

show abstract

Emergency supplies transportation robot trajectory tracking control based on Koopman and improved event‐triggered model predictive control

Zhang,

Tang,

Zhang

et al. 2024

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

In the emergency rescue and disposal of social public emergencies, supply transportation effectively provides a strong supply foundation and realistic conditions. The trajectory tracking control of emergency supplies transportation robot is the key technology to ensure the timeliness of transportation. In this article, the emergency supplies transportation robot is taken as the research object, based on Koopman operator theory, combined with radial basis function (RBF) neural network disturbance observer and adaptive prediction horizon event‐triggered model predictive control (APET‐MPC) algorithm to investigate the purely data‐driven trajectory tracking control problem of emergency supplies transportation robot when the model parameters and models are unknown. Firstly, the Koopman operator is used to establish a high‐dimensional linear model of the robot. Secondly, the RBF neural network disturbance observer is designed to estimate the disturbance during the robot operation and compensate it to the controller. Thirdly, APET‐MPC is used to optimize the trajectory tracking control of the emergency supplies transportation robot to reduce computational complexity. Finally, the performance of the proposed trajectory tracking controller is verified by Carsim/ Simulink joint simulation. The simulation results show that the model established by Koopman operator theory can achieve the high accuracy approximation of the robot. Compared with the MPC trajectory tracking controller, the APET‐MPC trajectory tracking controller based on RBF neural network disturbance observer (RBF‐APET‐MPC) improves the tracking accuracy of the robot and reduces the total triggering times of the system by more than 50%.

show abstract

Formation Control of Multiple Unmanned Aerial Vehicles by Event-Triggered Distributed Model Predictive Control

Cited by 50 publications

References 27 publications

UAV Swarm Intelligence: Recent Advances and Future Trends

UAV Swarm Intelligence: Recent Advances and Future Trends

Adaptive model predictive control with extended state observer for multi‐UAV formation flight

Emergency supplies transportation robot trajectory tracking control based on Koopman and improved event‐triggered model predictive control

Contact Info

Product

Resources

About