Real time fuzzy controller for quadrotor stability control

Bhatkhande, Pranav; Havens, Timothy C.

doi:10.1109/fuzz-ieee.2014.6891787

Cited by 21 publications

(11 citation statements)

References 9 publications

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“…The results showed that fuzzy controller is easily capable of controlling the Quadrotor, with the advantage that it was self-tuned as opposed to the PD controller. Besides, fuzzy outperformed PD in certain conditions [75]. Finally, in 2014, a Hybrid method of backstepping and fuzzy adaptive PID is proposed by Qingji et al [76].…”

Section: Inputmentioning

confidence: 99%

Literature Review for Digital Implementations of Fuzzy Logic Type-1 and Type-2

Ponce-Cruz

Molina

MacCleery

2015

Fuzzy Logic Type 1 and Type 2 Based on LabVIEW™ FPGA

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Some works are described below where optimization Type-1 and Type-2 FLS have had relative success according to different areas, illustrating the advantages of using methods to automate process with fuzzy controllers.The work of Sudha and Vijai Santhi [1], deals with the load frequency control (LFC) problem, which has been a major subject in electrical power system such as design and operation. In practice, LFC systems use simple proportional integral (PI) controllers. However, it shows that fuzzy logic controller is adaptive in nature and is applied successfully for power system stabilization control. Authors propose a type-2 fuzzy approach for load frequency control of two-area interconnected reheat thermal power system with the consideration of Generation Rate Constraint (GRC). The performance of the type-2 controller is compared with conventional controller and Type-1 fuzzy controller with regard to Generation Rate Constraint (GRC). The system parametric uncertainties are verified by changing parameters by 40% simultaneously from their typical values.The authors Jammeth and Fleury in their work [2] use an interval Type-2 FLC that gets better video quality compared with traditional controllers. They also use Type-1 FLC to show the response in different network scenarios, such like Internet cross-traffic, video streams occupy on an Internet protocol (IP) network. Type-2 FLC is designed for Internet conditions so they can react successfully to the network conditions of an IP-network. Type-2 FLC resulted in an order of magnitude performance improvement in comparison with Type-1 FLC.The work of Lee and Lin [3], proposes a new control scheme using type-2 fuzzy neural network (type-2 FNN) and adaptive filter for controlling nonlinear uncertain systems. This type-2 FNN model combines the advantages of type-2 fuzzy logic systems and neural networks. The type-2 FNN system has the ability of universal approximation, which identifies nonlinear dynamic systems. The control scheme consists of a PD-type adaptive FNN controller and a pre-filter. The adaptive filter is

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Section: Inputmentioning

confidence: 99%

Literature Review for Digital Implementations of Fuzzy Logic Type-1 and Type-2

Ponce-Cruz

Molina

MacCleery

2015

Fuzzy Logic Type 1 and Type 2 Based on LabVIEW™ FPGA

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“…Thus, the dependency on the GCS-UAV communication performance can be reduced. The artificial intelligence algorithms can deal with the basic flight control issues [88], [89], path planning problems [90], [91] of the UAVs themselves, as well as some mission-related problems, such as the machine vision [92], pattern recognition [93] especially when executing reconnaissance and tracking missions. What the artificial intelligence algorithms bring to the UAVs is the ability to learn and utilize the experiences and knowledge, which makes the UAVs think and behave like a human.…”

Section: (C) Intelligent Algorithmsmentioning

confidence: 99%

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Wang

Zhao

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

186

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Unmanned aerial vehicle (UAV) networks are playing an important role in various areas due to their agility and versatility, which have attracted significant attention from both the academia and industry in recent years. As an integration of the embedded systems with communication devices, computation capabilities and control modules, the UAV network could build a closed loop from data perceiving, information exchanging, decision making to the final execution, which tightly integrates the cyber processes into the physical devices. Therefore, the UAV network could be considered as a cyber physical system (CPS). Revealing the coupling effects among the three interacted components in this CPS system, i.e., communication, computation and control, is envisioned as the key to properly utilize all the available resources and hence improve the performance of the UAV networks. In this paper, we present a comprehensive survey on the UAV networks from a CPS perspective. Firstly, we respectively research the basics and advances with respect to the three CPS components in the UAV networks. Then we look inside to investigate how these components contribute to the system performance by classifying the UAV networks into three hierarchies, i.e., the cell level, the system level, and the system of system level. Further, the coupling effects among these CPS components are explicitly illustrated, which could be enlightening to deal with the challenges in each individual aspect. New research directions and open issues are discussed at the end of this survey. With this intensive literature review, we try to provide a novel insight into the state-of-the-art in the UAV networks.

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“…These rules do not provide robustness and also don't resemble the human-like reasoning for multi-inputs. A number of quadrotor control studies have used this control approach [113][114][115][116][117][118] and superior results have been reported.…”

Section: Fuzzy Logic Based Intelligent Controlmentioning

confidence: 99%

Nonlinear and Adaptive Intelligent Control Techniques for Quadrotor UAV – A Survey

Mo¹,

Farid²

2018

Asian Journal of Control

171

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Parametric uncertainties and coupled nonlinear dynamics are inherent in quadrotor configuration and infer adaptive nonlinear approaches to be used for flight control system. Numerous adaptive nonlinear and intelligent control techniques, which have been reported in the literature for designing quadrotor flight controller by various researchers, are investigated in this paper. As a priori, each conventional nonlinear control technique is discussed broadly and then its adaptive/observer based augmentation is conferred along with all possible variants. Among conventional nonlinear control approaches, feedback linearization, backstepping, sliding mode, and model predictive control, are studied. Intelligent control approaches incorporating fuzzy logic and neural networks are also discussed. In addition to adaption based parametric uncertainty handling, various other aspects of each control technique regarding stability, disturbance rejection, response time, asymptotic, exponential and finite time convergence etc., are discussed in sufficient depth. The contribution of this paper is the provision of detailed and in depth discussion on quadrotor nonlinear control approaches to the flight control designers.

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Real time fuzzy controller for quadrotor stability control

Cited by 21 publications

References 9 publications

Literature Review for Digital Implementations of Fuzzy Logic Type-1 and Type-2

Literature Review for Digital Implementations of Fuzzy Logic Type-1 and Type-2

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Nonlinear and Adaptive Intelligent Control Techniques for Quadrotor UAV – A Survey

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