Implementation of a super twisting controller for distributed formation flight of multi-agent systems based on consensus algorithms

Rojo-Rodriguez, Erik G.; Ollervides, E. J.; Rodríguez, Jonathan; Espinoza, Eduardo S.; Zambrano‐Robledo, Patricia del Carmen; García, Octavio

doi:10.1109/icuas.2017.7991490

Cited by 11 publications

(10 citation statements)

References 23 publications

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“…Another approach to address is the use of higher-order sliding mode control, see [76] for details. To the best of our knowledge, higher-order sliding mode control has not been used in the context of dynamic average consensus, although there exists results for other networked agreement problems [77].…”

Section: Signals With Bounded Derivatives (Continuous Time)mentioning

confidence: 99%

Tutorial on Dynamic Average Consensus: The Problem, Its Applications, and the Algorithms

et al. 2019

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Technological advances in ad-hoc networking and the availability of low-cost reliable computing, data storage and sensing devices have made possible scenarios where the coordination of many subsystems extends the range of human capabilities. Smart grid operations, smart transportation, smart healthcare and sensing networks for environmental monitoring and exploration in hazardous situations are just a few examples of such network operations. In these applications, the ability of a network system to, in a decentralized fashion, fuse information, compute common estimates of unknown quantities, and agree on a common view of the world is critical. These problems can be formulated as agreement problems on linear combinations of dynamically changing reference signals or local parameters. This dynamic agreement problem corresponds to dynamic average consensus, which is the problem of interest of this article. The dynamic average consensus problem is for a group of agents to cooperate in order to track the average of locally available time-varying reference signals, where each agent is only capable of local computations and communicating with local neighbors, see Figure 1. Figure 1: A group of communication agents, each endowed with a time-varying reference signal. 1 arXiv:1803.04628v2 [cs.SY] 24 Nov 2018 Centralized solutions have drawbacksThe difficulty in the dynamic average consensus problem is that the information is distributed across the network. A straightforward solution, termed centralized, to the dynamic average consensus problem appears to be to gather all of the information in a single place, do the computation (in other words, calculate the average), and then send the solution back through the network to each agent. Although simple, the centralized approach has numerous drawbacks: (1) the algorithm is not robust to failures of the centralized agent (if the centralized agent fails, then the entire computation fails), (2) the method is not scalable since the amount of communication and memory required on each agent scales with the size of the network, (3) each agent must have a unique identifier (so that the centralized agent counts each value only once), (4) the calculated average is delayed by an amount which grows with the size of the network, and (5) the reference signals from each agent are exposed over the entire network which is unacceptable in applications involving sensitive data. The centralized solution is fragile due to existence of a single failure point in the network. This can be overcome by having every agent act as the centralized agent. In this approach, referred to as flooding, agents transmit the values of the reference signals across the entire network until each agent knows each reference signal. This may be summarized as "first do all communications, then do all computations". While flooding fixes the issue of robustness to agent failures, it is still subject to many of the drawbacks of the centralized solution. Also, although this approach works reasonably well for small size networks,...

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Section: Signals With Bounded Derivatives (Continuous Time)mentioning

confidence: 99%

Tutorial on Dynamic Average Consensus: The Problem, Its Applications, and the Algorithms

et al. 2019

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“…The control inputs u p i and u a i are second order sliding mode controllers in order to obtain a guidance and navigation for the i aerial vehicle of the MAS as a networked control system. 19,21,29 Remark 1: As the rotation of the four propellers on the quadrotor is balanced, gyroscopic moment d g gyro i will essentially be zero. The only case in which gyroscopic moments will not be zero is if there is a significant difference in the RPM of the four motors and in the presence of a strong sideways cross-wind.…”

Section: Guidance Navigation and Control For The I Aerial Vehiclementioning

confidence: 99%

“…Then, a sliding mode control (SMC) is used for each quadrotor UAV considering the complete mathematical model, for more details see literature. 19,21 In this sense, the trajectory tracking block is directly connected to the Sectorial Consensus Manager block which runs the consensus algorithm of the N quadrotor UAVs, see Figure 3. Thus, the sectorial consensus manager operates as a distributed protocol that is used to perform formation flight for the MASs or multiple quadrotor UAVs in which each agent or vehicle can take distributed decisions on the local information resulting in a collective motion of all the agents in the group.…”

Section: Fuzzy Logic Formation Controllermentioning

confidence: 99%

“…A simulation result was shown to illustrate the effectiveness of the obtained theoretical approach. In Rojo-Rodriguez et al 19 two schemes for formation flight were proposed based on a PID and a second order sliding mode. In this paper, the two algorithms were studied as a comparison, and mainly these algorithms control the orientation and position dynamics of each agent computing its own algorithm based on local information and information from its neighbors in the MASs.…”

Section: Introductionmentioning

confidence: 99%

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A sectorial fuzzy consensus algorithm for the formation flight of multiple quadrotor unmanned aerial vehicles

Ollervides-Vazquez

Rojo-Rodriguez

Garcia‐Salazar

et al. 2020

International Journal of Micro Air Vehicles

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This paper presents an algorithm based on fuzzy theory for the formation flight of the multi-quadrotors. For this purpose, the mathematical model of N-quadrotor unmanned aerial vehicles is presented using the Newton-Euler formulation. The strategy of the formation flight is based on a structure composed by a sectorial fuzzy controller and the linear systems whose state variables are the position and velocity of the ith quadrotor. The stability analysis is described as a generalized form for N-quadrotor unmanned aerial vehicles and it is based on the Lyapunov theory. This analysis demonstrates that the closed-loop system is globally asymptotically stable so that the quadrotors unmanned aerial vehicles reach the consensus. Numerical simulation demonstrates the robustness of the proposed scheme for the formation flight even in the presence of disturbances. Finally, experimental results show the feasibility of the proposed algorithm for the formation flight of multiple unmanned aerial vehicles.

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“…These algorithms do not follow a holistic approach to addressing security beyond interaction. Examples of these consensus algorithms are firefly [25], distributed containment control [26], forced bipartite [27], distributed flocking [28], constraint and projected [29], constant step-size gradient descent [30], proportional integral derivative and super twisting [31], uncertain linear [32], rate [33], and the ones based on swarm intelligence [34] among others. Each device senses things differently, for instance, a sensor might check for motion, some to measure temperature, while another humidity and they can exist in the same Wireless Sensor Network.…”

Section: Planning and Consensus Within Disparate Iot Elementsmentioning

confidence: 99%

Securing Self-organizing IoT Ecosystem: A Distributed Ledger Technology Approach

Joseph

Raffety

Morrow

et al. 2019

2019 IEEE 5th World Forum on Internet of Things (WF-IoT)

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The proliferation of the Internet of Things has seen it adopted to practically all aspects of life. There has been an increase in demand for more IoT devices which are manufactured by several companies. This has however left need to address vulnerabilities within and threats to these devices. In many cases, these vulnerabilities arise from manufacturer focus on functionality rather than security. Secure by design IoT devices are rare in the market today. Efforts to address this are being made by the IoT research community, however, more effort is required. Deficiencies of current efforts include accountability of devices and privacy of data generated across the IoT landscape. The aim of this Ph.D. research is to improve the security, privacy, veracity, and trust. The approach developed in this study will be based on non-repudiation of actions among self-organized IoT devices in an IoT Ecosystem by leveraging Distributed Ledger Technology (DLT). A proposed system architecture which relies on the Distributed Ledger Technology and its related features will enable services to be applied to the IoT landscape to achieve aspects of end to end IoT security. The initial progress to date is presented within this manuscript.

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Implementation of a super twisting controller for distributed formation flight of multi-agent systems based on consensus algorithms

Cited by 11 publications

References 23 publications

Tutorial on Dynamic Average Consensus: The Problem, Its Applications, and the Algorithms

Tutorial on Dynamic Average Consensus: The Problem, Its Applications, and the Algorithms

A sectorial fuzzy consensus algorithm for the formation flight of multiple quadrotor unmanned aerial vehicles

Securing Self-organizing IoT Ecosystem: A Distributed Ledger Technology Approach

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