دانلود رایگان مقاله لاتین کنترل حرکت طولی هواپیما از سایت الزویر
عنوان فارسی مقاله:
کنترل حرکت طولی هواپیما بر اساس مدل فازی تاکاگی سوگنو
عنوان انگلیسی مقاله:
Aircraft longitudinal motion control based on Takagi–Sugeno fuzzy model
سال انتشار : 2016
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مقدمه انگلیسی مقاله:
1. Introduction
In the last few years advanced control techniques capable to deal with the nonlinear systems have gained growing popularity within the aerospace community. One of the most popular method is dynamic inversion [1] which is equivalent to exact feedback linearization where a nonlinear state feedback cancels out the nonlinearity of the controlled system [2]. The main difficulty arising with the use of dynamic inversion is its big sensitivity to model inaccuracies such as parametric uncertainty or unmodeled dynamics. To overcome this difficulty different approaches are used. Under some assumptions on the dynamics robust design methods may be applied. In [3] a nonlinear H∞ robust control is utilized, whereas in [4] stochastic robust nonlinear control approach is presented. Unfortunately, both solutions have significant drawbacks. The former leads to solving of a complicated Hamilton–Jacobi partial differential inequality, the latter guarantees control performance only with some level of confidence. The easiest and most common way how to compensate the inaccuracies arising during dynamic inversion consists in using a PI controller. Such a configuration was tested on F/A-18 airplane testbed [5]. Another option is to use adaptive dynamics inversion. Such a technique is exploited either as Model Reference Adaptive Control (MRAC) scheme [6–8] where the goal is to minimize the error between the actual output and the output of the suitably chosen reference model or via Lyapunov-based design that is used to guarantee asymptotic output tracking [9]. The common disadvantage of those methods is that the bounds of nonlinear terms need to be known apriori. In [10] dynamics inversion is supported by a PID controller and Mamdani type fuzzy logic controller to control aircraft landing. A combination of backstepping control approach with dynamics inversion was introduced in [11]. In [12] dynamics inversion supported by PID controller is used to develop different nonlinear antiwindup schemes. Among the on artificial intelligence based control methods arti- ficialneuralnetworks (ANN) play the dominant role.In[13]they are used to approximate dynamics inversion whereas in [14,15] they are adapting to the errors caused by exact and linearized dynamics inversion, respectively. Besides, ANN generate the adaptation law in direct adaptive control systems [16], both identify the controlled plant and produce the controller outputin an indirect adaptive control [17,18] and aid the conventional controller for compensating the nonlinearity in Feedback Error learning Neural Control (FENC) scheme [19]. Surprisingly, only few attempts have been made to employ fuzzy logic. Moreover, most of them adopt heuristic approach to generate the rules that cannot guarantee the fundamental requirements such as stability or control performance. In [20] the rules for prescribed aircraft trajectory following are designed. Sequential adaptive fuzzy inference system that adds or removes the rules according to input data is used to support the conventional controller for an aircraft automatic landing under the failures of the stuck control surfaces in [21]. In [22] adaptive fuzzy controller for suppressing wing-rockmotion wasdeveloped. Fuzzy systemfor the longitudinal control of an F-14 airplane and DeHavilland Beaverwas designed in [23,24], respectively. Fuzzy logic based PID controllers have been applied for a control of an automatic landing [25] or a pitch control [26,27]. Neuro-fuzzy controllers have been designed especially for automatic landing control systems [28–30]. The only exception where a systematic approach of creating a nonlinear fuzzy model of the aircraft motion and fuzzy controller design guaranteeing stability of the closed-loop system have been used is the paper [31]. The presented approach is based on using Takagi–Sugeno (T–S) fuzzy model and Parallel Distributed Compensation (PDC) controller.
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کلمات کلیدی:
2-DOF PI(D) Takagi-Sugeno and sliding mode controllers for BLDC ... ieeexplore.ieee.org/document/6397258/ by AI Stînean - 2012 - Cited by 3 - Related articles This paper gives a new 2-DOF PI(D) controller structure based on Takagi-Sugeno fuzzy ... Published in: Power Electronics and Motion Control Conference ... [PDF]PSO based modeling of Takagi-Sugeno fuzzy motion controller for ... https://fedcsis.org/proceedings/2010/pliks/186.pdf by M Gupta - 2010 - Cited by 6 - Related articles PSO based modeling of Takagi-Sugeno fuzzy motion controller for dynamic object tracking with mobile platform. Meenakshi Gupta, Laxmidhar Behera, and K. S. ... Vehicle Yaw Motion Control Using Takagi-Sugeno Modeling and ... proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleID=1618181 Vehicle Yaw Motion Control Using Takagi-Sugeno Modeling and Quadratic ... An integrated steering and differential braking controller based on invariant sets, ... Adaptive model reference control based on Takagi-Sugeno fuzzy ... link.springer.com/article/10.1007/BF02996099 by J Lee - 2004 - Cited by 3 - Related articles Jan 3, 2004 - Abstract. The control scheme using fuzzy modeling and Parallel Distributed Compensation (PDC) concept is proposed to provide asymptotic ... Fuzzy variable structure control based on a Takagi-Sugeno model for ... journals.sagepub.com/doi/abs/10.1243/09596518JSCE702 by XZ Zhang - 2009 - Cited by 8 - Related articles A fuzzy variable structure control (FVSC) scheme based on a Takagi-Sugeno ... B. Y. 'Automatic disturbances rejection controller for precise motion control of ... Takagi-Sugeno Fuzzy Model-Based Flight Control and Failure ... https://arc.aiaa.org/doi/abs/10.2514/1.52509 by EJ Butler - 2011 - Cited by 10 - Related articles (2016) Aircraft longitudinal motion control based on Takagi–Sugeno fuzzy model. Applied Soft Computing 49, 269-278. Online publication date: 1-Dec-2016.
