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PSO tuned FLC for full autopilot control of quadrotor to tackle wind disturbance using bond graph approach

机译:PSO调整FLC用于使用键参数方法进行四轮车的全自动驾驶仪控制,以解决风扰动

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The ability of Bond Graph (BG) in modeling multi-domain structures results in a more precise and expansive interface. Hence, this paper develops the model of a quadrotor using BG approach. Then, the paper introduces and optimizes a Fuzzy Logic Controller (FLC) with the aim of making intelligent decisions close to human decisions. Additionally, a Particle Swarm Optimization (PSO) algorithm is utilized to have minimum 4 rules for FLC, which leads the controller to be quick. It is because a fast FLC is necessary in the next part to convert the controller to a full one (considering all main states). Furthermore, two Proportional-Integral-Derivative (PID) controllers have helped the FLC to bring all the main states of quadrotor under control. In addition, the set points for the angles, which cause higher nonlinearity behavior, could now be set close to pi/2. Furthermore, multi-domain model of the system has helped in modeling of constant and random variable wind disturbances for which a logical solution is suggested. (c) 2018 Elsevier B.V. All rights reserved.
机译:粘合图(BG)在建模多域结构中的能力导致更精确和膨胀的界面。因此,本文使用BG方法开发了四元电机的模型。然后,本文介绍并优化了模糊逻辑控制器(FLC),其目的是使智能决策接近人为决策。另外,利用粒子群优化(PSO)算法至少有FLC的4个规则,这导致控制器快速。这是因为在下一部分中需要快速FLC,以将控制器转换为完整的(考虑所有主要状态)。此外,两种比例积分衍生物(PID)控制器有助于FLC在控制下带来所有正动力器的所有主要状态。另外,现在可以将导致较高的非线性行为的角度的设定点设置接近PI / 2。此外,系统的多域模型已经有助于建模恒定和随机变量风扰动,其中建议逻辑解决方案。 (c)2018 Elsevier B.v.保留所有权利。

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