Fuzzy Controllers

Albertos, P.

doi:10.1016/b978-0-08-042016-5.50024-3

Cited by 5 publications

(6 citation statements)

References 3 publications

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“…We can compute the control to either, counteract the disturbances keeping the output constant, or optimize the controlled variables. Similar methodologies have been applied successfully in other technical fields (Sugeno, 1985;Albertos, 1992). In the following sections, its suitability for application in social processes is explored.…”

Section: If Fact1 and Fact2 Then Fact3mentioning

confidence: 97%

Fuzzy Logic Modeling of Social Behavior

Barberá

Albertos

1994

Cybernetics and Systems

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Section: If Fact1 and Fact2 Then Fact3mentioning

confidence: 97%

Fuzzy Logic Modeling of Social Behavior

Barberá

Albertos

1994

Cybernetics and Systems

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“…40 Literature shows extensive applications of FL such as train control, vacuum cleaners, embedded control, and mobile robot navigation. [41][42][43][44] Particularly, this technique is especially relevant for the CT and Human Drone Interaction (HDI) tasks considered in this paper since it allows incorporation of human knowledge in controller design via linguistic variables and rules. 41,43,45 Humans carry out CT very effectively irrespective of muscular strength, shape of object, and number of humans, and without much direct communication.…”

Section: Fuzzy Logic Force-torque Feedback Controllermentioning

confidence: 99%

“…[41][42][43][44] Particularly, this technique is especially relevant for the CT and Human Drone Interaction (HDI) tasks considered in this paper since it allows incorporation of human knowledge in controller design via linguistic variables and rules. 41,43,45 Humans carry out CT very effectively irrespective of muscular strength, shape of object, and number of humans, and without much direct communication. In case of application of HDI, it is desirable for the follower to have humanlike characteristics since it needs to coordinate with a human operator.…”

Section: Fuzzy Logic Force-torque Feedback Controllermentioning

confidence: 99%

“…30 Moreover, FL is inherently more robust to sensor noise and other disturbances arising from inexact system knowledge and environmental parameters. 42,46 As a result, inexpensive light uncertain noisy force-torque sensors can be utilized 43 even for control of complicated tasks such as CT.…”

Section: Fuzzy Logic Force-torque Feedback Controllermentioning

confidence: 99%

“…45 Some researchers have addressed stability issues by using simulations and experiments. 42 Another disadvantage of using FL is that tuning these controllers is time consuming and strenuous 43 due to existence of many tunable parameters. There are numerous works in literature that have used optimization methods such as Genetic Algorithm, Simulated Annealing, and neuro-fuzzy schemes for tuning FL parameters.…”

Section: Introductionmentioning

confidence: 99%

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Force-torque (F-T) based multi-drone cooperative transport using fuzzy logic and low-cost and imprecise F-T sensor

Barawkar

Kumar

2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper presents a novel approach to perform the task of cooperative transportation (CT) by using multiple quadcopter drones. A leader-follower approach is utilized. Considering that the leader drone can be controlled using different means, such as Proportional, Integral, and Derivative (PID) control or remote control by an operator, this paper focuses on designing the control scheme for the follower drone. This paper specifically considers outdoor application of such systems that requires usage of Global Positioning System (GPS) to receive the positional information of the drones. However, GPS has inherent errors of order of magnitude that can destabilize the system. In order to address this major limitation, a Force-Torque Feedback Controller (FT-FC) is proposed to control the follower drone. The FT-FC provides control based on the interaction forces and torques acting at the point of contact between the follower drone and the payload. Using such passive control schemes, drones are thus not required to communicate with each other. Fuzzy Logic (FL) is used to implement the FT-FC. Fuzzy Logic provides effective force-torque coordination between drones, emulates human behavior during CT, and allows use of noisy inexpensive force-torque sensors. This paper presents results from numerical simulations showing the effectiveness of the proposed controller for way-point navigation and complex trajectory tracking. Additionally, the effectiveness of the fuzzy-based FT-FC in terms of handling disturbances is also demonstrated. The proposed FL FT-FC is also experimentally validated for two applications viz. three-dimensional physical Human Drone Interaction (HDI) and CT.

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