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相关概念视频

Controller Configurations01:22

Controller Configurations

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Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
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Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
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Errors in Global Positioning System01:26

Errors in Global Positioning System

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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

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Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
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Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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改进了自动驾驶汽车的观察者设计方法,使用基于错误的超局部模型.

Daniel Fenyes1, Tamas Hegedus2, Balazs Nemeth2,3

  • 1HUN-REN Institute for Computer Science and Control (SZTAKI), Kende u. 13-17, 1111, Budapest, Hungary. daniel.fenyes@sztaki.hu.

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概括

这项研究引入了自动驾驶汽车的新观察者设计,将线性参数变化 (LPV) 方法与基于错误的超局部模型相结合,以提高车辆动态的估计精度.

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科学领域:

  • 控制系统工程 控制系统工程
  • 机器人技术 机器人技术 机器人技术
  • 汽车工程 汽车工程

背景情况:

  • 自动驾驶汽车系统需要准确的状态估计,以确保安全高效的运行.
  • 传统的观察者设计经常与车辆动态固有的模型不确定性和非线性作斗争.
  • 现有的方法可能无法充分解决自动驾驶汽车遇到的复杂现实条件.

研究的目的:

  • 开发一种用于自动驾驶汽车状态估计的新型观察者设计方法.
  • 通过整合基于错误的超局部模型来提高线性参数变化 (LPV) 观察者的性能.
  • 为了有效地管理车辆模型中的非模拟动态和非线性.

主要方法:

  • 将线性参数变化 (LPV) 框架与基于错误的超局部模型相结合.
  • 基于错误的超局部模型被用来弥补不确定性和非线性.
  • 实现自动驾驶汽车侧向速度的具体估计.

主要成果:

  • 由于新设计,LPV观测器的性能得到了显著改善.
  • 通过模拟验证观察算法的效率和操作能力.
  • 使用ZalaZone试验场的现实世界测试测量来证明有效性.

结论:

  • 拟议的观察员设计方法有效地提高了对自动驾驶汽车的状态估计.
  • 集成LPV和基于错误的超局部模型为处理模型不确定性提供了强大的解决方案.
  • 该方法显示了实际可用性,通过模拟和真实世界数据得到证实.