Fuzzy flight control system for helicopter intelligence in hover

Lower, Michał; Szlachetko, Boguslaw; Król, Dariusz

doi:10.1109/isda.2005.48

Cited by 15 publications

(10 citation statements)

References 3 publications

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“…Data collected need to be normalized according to the element attribution before feeding to IHDR algorithm. This preprocess is shown in equation (2).…”

Section: Mapping Base With Ihdrmentioning

confidence: 99%

“…This is usually called model-base method. Traditional methods, such as feedback linearization approach [1], fuzzy control technology [2], neural network control technology [3], mostly rely on the precision of the model and the veracity of identification. Unfortunately, the feedback linearization controller is quite sensitive to disturbance and noise as well as modeling uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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Hovering control of UAV based on autonomous mapping approach

Yang

Zhao

Han

2010

2010 IEEE International Conference on Robotics and Biomimetics

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The autonomous learning capability of UAV (unmanned aerial vehicle) has gained more and more attentions of researchers. As far as helicopter steering is concerned, instead of calculating dynamics of UAV in a relative short interval, pilots just take their experience and consciousness instantaneously, known as similar status has similar action, to keep the helicopter hovering. The experience and the consciousness of pilots store the mapping from the similar environment to the corresponding similar decisions. Motivated by the process, we establish the UAV mapping base from environment to decision with IHDR (Incremental Hierarchical Discriminant Regression) algorithm. UAV learns to build the mapping relation offline and online alternatively. The retrieval process from the mapping base has higher efficiency for its less mathematics complexity. Simulation results show that the approach we proposed in this paper has better performance than traditional neural network.

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“…Data collected need to be normalized according to the element attribution before feeding to IHDR algorithm. This preprocess is shown in equation (2).…”

Section: Mapping Base With Ihdrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hovering control of UAV based on autonomous mapping approach

Yang

Zhao

Han

2010

2010 IEEE International Conference on Robotics and Biomimetics

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“…Fuzzy Logic Controllers (FLCs) are ideal for these goals: they are certainly easier to design, requiring limited insight into helicopter mathematics, while allowing for relatively cheap hardware for a physical implementation. In fact such techniques have already been tested, as highlighted in [8], [9] and [10]. This project builds upon such systems to develop a controller which is easy to design without having to go into complex state-space representation.…”

Section: Introductionmentioning

confidence: 99%

Autonomous flight control for an RC helicopter

Camilleri

Scerri

Zammit

2012

2012 16th IEEE Mediterranean Electrotechnical Conference

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This paper presents an autopilot system for a Radio Controlled (RC) helicopter, in a simulation environment. The controller is designed using Fuzzy Logic in order to achieve simplicity and cost effectiveness. The system controls the helicopter from takeoff to landing, allowing the craft to reach any position and hover stably with a defined yaw attitude at the request of the user. The Fuzzy Inference Systems (FIS) in use were able to allow for these operations with more than satisfactory results, even in simulated winds.

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“…Although model or real helicopters usually are of 2 to 6 degrees of freedom, the researches are usually concerned with constrained situations to reduce the variables and degrees of freedom and let complicated theories be tested [3,4,5,6,7], yet complicated MIMO models of helicopter have also been controlled by fuzzy and ANN controllers [4,7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Hybrid intelligent control of a lab model helicopter pitch dynamics

Mohammadzaheri

Chen

2007

2007 International Conference on Intelligent and Advanced Systems

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In this paper, the pitch angle control for a lab model helicopter is discussed. This problem has some specific features. As a main unusual feature, it is observed that the steady state control command is completely dependent on the setpoint, so error-based controUers do not suit this problem. Moreover, the system is of a highly oscillating dynamic. In order to solve this control problem, two controUers are designed, an artificial neural network, whose input is the setpoint, is used to provide steady state control command, and a fuzzy inference system ,whose input is error, is used to provide transient control command. The total control command is the sum of two aforementioned control commands. The system's robustness is also checked against any probable disturbance and parameter change. It is shown that the system is likely to be completely robust against any possible disturbance or parameter change in lab environment. In order make the system robust against a special kind of parameter change which is impossible to happen in the lab, a setpoint modification algorithm is offered to vanish the steady state error and provide a rather short settling time.

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Fuzzy flight control system for helicopter intelligence in hover

Cited by 15 publications

References 3 publications

Hovering control of UAV based on autonomous mapping approach

Hovering control of UAV based on autonomous mapping approach

Autonomous flight control for an RC helicopter

Hybrid intelligent control of a lab model helicopter pitch dynamics

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