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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.
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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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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...
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基于适应性动态编程的自学滑动模式控制,用于非holonomic移动机器人的移动机器人.

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  • 1College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China.

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概括
此摘要是机器生成的。

本研究介绍了非全学移动机器人 (NMR) 的自学滑动模式控制 (SlSMC). 这种新的方法提高了轨迹跟踪性能,并确保稳定性,即使在不确定性和干扰的情况下.

关键词:
适应性动态编程 (ADP) 是一种非holonomic移动机器人 (NMRs) 是一种非holonomic移动机器人.下降的地平线下降.滑动模式控制 (SMC) 是一个轨迹跟踪控制器可以控制轨迹的跟踪.

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 控制系统工程 控制系统工程
  • 人工智能的人工智能

背景情况:

  • 非holonomic移动机器人 (NMR) 在轨迹跟踪方面面临挑战,原因是滑动,滑动和环境噪音等不确定性.
  • 传统的滑动模式控制 (SMC) 提供了稳定性,但可以进一步优化以提高性能.

研究的目的:

  • 开发一种自学滑动模式控制 (SlSMC) 策略,确保NMR轨迹跟踪的稳定性.
  • 通过优化SMC的到达法和滑动模式表面来提高控制性能.
  • 为了确保有限时间的融合和针对匹配的不确定性的稳定性.

主要方法:

  • 考虑不利因素的NMR动力学模型的重建.
  • 一个集成终端滑动模式控制器的设计.
  • 在稳定性约束下,制定优化问题,以达到规律和滑动模式的表面.
  • 实现在线连续时间退缩地平线优化机制,使用演员关键算法进行异步问题解决.

主要成果:

  • 理论和基于模拟的验证拟议的SlSMC战略的稳定性和趋同性.
  • 通过广泛的模拟,证明与最近的三种方法相比,控制性能优越.
  • 在真正的智能车辆上进行实验验证,证实该方法对NMR的实际适用性.

结论:

  • 拟议的自学SMC战略有效地增强了NMR的轨迹跟踪.
  • 收缩地平线基于学习的方法在控制性能和稳定性方面提供了显著的改进.
  • 该方法适用于现实世界的应用,满足智能汽车的实际控制需求.