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相关概念视频

PD Controller: Design01:26

PD Controller: Design

272
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
272
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

96
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
96
Feedback control systems01:26

Feedback control systems

332
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
332
Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

365
Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
365
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

136
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...
136
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

122
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
122

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相关实验视频

Updated: Jul 15, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
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关于深度强化学习控制算法的研究,用于考虑不确定的时间延迟的活性悬浮.

Yang Wang1, Cheng Wang2, Shijie Zhao2

  • 1School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100811, China.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一个深度强化学习控制算法,用于管理主动悬挂系统的不确定延迟. 这种新的方法显著提高了驾驶舒适度,减少了振动,超过了被动系统的性能.

关键词:
活动悬浮剂活性悬浮剂深度强化学习的学习.悬浮控制器的悬浮控制器是什么不确定的时间延迟.

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科学领域:

  • 汽车工程 汽车工程
  • 控制系统 控制系统
  • 人工智能的人工智能

背景情况:

  • 活动悬挂系统的执行器延迟严重影响控制的有效性和稳定性.
  • 现有的控制算法经常与不确定的或可变的系统延迟作斗争.
  • 确保稳定的控制性能需要解决这些固有的延迟.

研究的目的:

  • 开发一种新的主动悬挂控制算法,能够处理不确定的执行器延迟.
  • 为了提高主动悬挂系统的稳定性和控制性能.
  • 在各种延迟条件下评估算法的有效性.

主要方法:

  • 利用了一个双延迟的深度决定性政策梯度 (TD3) 算法,专门为延迟系统量身定制.
  • 开发了深度强化学习 (DRL) 方法来优化控制政策.
  • 在三个条件下模拟性能:确定性,半常规性和不确定的延迟.

主要成果:

  • 拟议的DRL算法在所有测试的延迟条件中都表现出卓越的控制性能.
  • 与被动悬挂相比,在车身垂直加速优化方面取得了超过30%的改进.
  • 有效地减轻了低频体内振动,并提高了30%以上的驾驶舒适度.

结论:

  • 基于DRL的新型控制算法有效地解决了主动悬挂系统中不确定的执行器延迟问题.
  • 这种方法在驾驶舒适度和振动减少方面提供了显著的改进.
  • 该算法显示出在汽车主动悬挂中实际应用的巨大潜力.