Research on Longitudinal Control Algorithm for Flying Wing UAV Based on LQR Technology

Li, Yibo; Chen, Chao; Chen, Wei

doi:10.21307/ijssis-2017-632

Cited by 17 publications

(12 citation statements)

References 16 publications

(20 reference statements)

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“…The aim of inner-loop control is minimizing the difference between the UAV state and commands. The difference is represented by the cost function J [ 39 ], which is given by

where Q , R are the weighting matrices.

is the output feedback,

, where D is the feedback gain matrix.…”

Section: Appendix A1 Inner-loop Controller Designmentioning

confidence: 99%

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Rapidly Tuning the PID Controller Based on the Regional Surrogate Model Technique in the UAV Formation

Wang

Duan

Yan

et al. 2020

Entropy

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The leader–follower structure is widely used in unmanned aerial vehicle formation. This paper adopts the proportional-integral-derivative (PID) and the linear quadratic regulator controllers to construct the leader–follower formation. Tuning the PID controllers is generally empirical; hence, various surrogate models have been introduced to identify more refined parameters with relatively lower cost. However, the construction of surrogate models faces the problem that the singular points may affect the accuracy, such that the global surrogate models may be invalid. Thus, to tune controllers quickly and accurately, the regional surrogate model technique (RSMT), based on analyzing the regional information entropy, is proposed. The proposed RSMT cooperates only with the successful samples to mitigate the effect of singular points along with a classifier screening failed samples. Implementing the RSMT with various kinds of surrogate models, this study evaluates the Pareto fronts of the original simulation model and the RSMT to compare their effectiveness. The results show that the RSMT can accurately reconstruct the simulation model. Compared with the global surrogate models, the RSMT reduces the run time of tuning PID controllers by one order of magnitude, and it improves the accuracy of surrogate models by dozens of orders of magnitude.

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“…The aim of inner-loop control is minimizing the difference between the UAV state and commands. The difference is represented by the cost function J [ 39 ], which is given by

where Q , R are the weighting matrices.

is the output feedback,

, where D is the feedback gain matrix.…”

Section: Appendix A1 Inner-loop Controller Designmentioning

confidence: 99%

“…Following [ 39 , 40 ], the inner-loop controller is designed as follows. The linearized model of UAV is

where

is the state vector and

represent forward velocity, attack angle, pitch rate, pitch angle, side-slip angle, roll rate, yaw rate, and yaw angle, respectively.…”

Section: Appendix A1 Inner-loop Controller Designmentioning

confidence: 99%

Rapidly Tuning the PID Controller Based on the Regional Surrogate Model Technique in the UAV Formation

Wang

Duan

Yan

et al. 2020

Entropy

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show abstract

“…It is particularly appealing in the mobile device context as it minimizes the usage of wireless access links. [8] So it is easier to trust the cloud provider for location privacy. Thus, a presence service can be an essential service so that any application that needs location information can talk to the presence service.…”

Section: Platform Servicesmentioning

confidence: 99%

“…Context extraction service provides data mining analysis of mobile data combined with other forms of data, such as social networking data and sensor network data, in order to extract contextual clues relevant to the user. For example, recognizing the user's activity based on mobile accelerometer and audio data is one such contextual mining service that is currently being explored [8]. The context extraction service will be a common service that relieves each context-rich application from replicating context extraction, thus saving energy and reduce computation costs of mobiles.…”

Section: Context-rich Servicesmentioning

confidence: 99%

Secure Data Storage Mechanism for Integration of Wireless Sensor Networks and Mobile Cloud

2016

International Journal on Smart Sensing and Intelligent Systems

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Abstract-Together with an explosive growth of the mobile applications and emerging of cloud computing concept, mobile cloud computing (MCC) has been introduced to be a potential technology for mobile services. Wireless Sensor Networks (WSN) is the technology that connects the virtual world and the physical world where nodes can autonomously communicate among each other and with intelligent systems. This paper describes the concept of wireless sensor networks and mobile cloud computing. Recently, much research has proposed to integrate wireless sensor networks (WSNs) with mobile cloud computing, so that powerful cloud computing can be exploited to process the sensory data accumulated by WSNs and provide these date to the mobile users on demand. The current WSN-MCC integration schemes have several drawbacks. This paper proposes a data processing framework, which aims at transmitting desired data to the mobile users in a rapid, reliable and even more secure manner. The proposed framework decreases the storage requirements for sensor nodes and networks gateway. And it minimizes the traffic overhead and bandwidth requirement for sensor networks. Additionally, the framework can predict the future trend of sensory data and provide security for this sensory data. This framework ensures the mobile users obtain their desired data faster.

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“…The conventional LQR method present in [18] has effectively solved the problems that fixed-wing UAV is disturbed by air current easily and has poor flight stability. The longitudinal control for the flying wing UAV based on LQR can make the flying wing UAV achieves satisfactory longitudinal flying qualities [19]. The authors presented a studying the control of the vertical moving for UAV under PID, LQR, fuzzy control, and self-tuning fuzzy PID.…”

Section: Introductionmentioning

confidence: 99%

UAV Control Based on Dual LQR and Fuzzy-PID Controller

Al-Isawi¹,

Attiya²,

Adoghe

2020

alkej

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This paper presents the design of a longitudinal controller for an autonomous unmanned aerial vehicle (UAV). This paper proposed the dual loop (inner-outer loop) control based on the intelligent algorithm. The inner feedback loop controller is a Linear Quadratic Regulator (LQR) to provide robust (adaptive) stability. In contrast, the outer loop controller is based on Fuzzy-PID (Proportional, Integral, and Derivative) algorithm to provide reference signal tracking. The proposed dual controller is to control the position (altitude) and velocity (airspeed) of an aircraft. An adaptive Unscented Kalman Filter (AUKF) is employed to track the reference signal and is decreased the Gaussian noise. The mathematical model of aircraft has been (Cessna 172) presented. The stability and robustness of the system have been verified in a simulation experiment.

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Research on Longitudinal Control Algorithm for Flying Wing UAV Based on LQR Technology

Abstract: Abstract-Linear Quadratic Regulator (LQR) is widely used in many practical

Cited by 17 publications

References 16 publications

Rapidly Tuning the PID Controller Based on the Regional Surrogate Model Technique in the UAV Formation

Rapidly Tuning the PID Controller Based on the Regional Surrogate Model Technique in the UAV Formation

Secure Data Storage Mechanism for Integration of Wireless Sensor Networks and Mobile Cloud

UAV Control Based on Dual LQR and Fuzzy-PID Controller

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