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相关概念视频

Feedback control systems01:26

Feedback control systems

657
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...
657
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

314
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,...
314
Control Systems01:10

Control Systems

1.8K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
1.8K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

329
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
329
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

345
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...
345
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.5K
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
1.5K

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相关实验视频

Updated: Jan 7, 2026

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
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WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

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对连续时间非线性系统的数据驱动反向最佳控制.

Hamed Jabbari Asl1, Anh Vu Le2, Eiji Uchibe3

  • 1Department of Electrical Engineering, Faculty of Science and New Technology, Urmia University of Technology, Urmia, Iran.

ISA transactions
|December 25, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种用于反强化学习的新算法,用于估计复杂系统中的成本函数. 它为自主系统和机器人技术提供了一种计算效率高,无模型的方法.

关键词:
数据驱动的解决方案反向最优控制的反向最优控制反向增强学习是一种反向增强学习.没有模特的自由模式.非线性系统是非线性系统.

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科学领域:

  • 控制理论 控制理论
  • 机器学习 机器学习
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 估计成本函数对于理解和复制自主系统中的专家行为至关重要.
  • 现有的反强化学习方法通常需要系统模型或具有高的计算成本.

研究的目的:

  • 引入一种新的无模型和部分无模型算法,用于反向最佳控制.
  • 用专家轨迹估计连续时间非线性确定性系统的成本函数.

主要方法:

  • 该算法使用专家的输入状态轨迹.
  • 它单独使用控制政策信息和Hamilton-Jacobi-Bellman方程进行参数估计.
  • 在无模型版本的初始化中,需要一个单一的前向最佳控制问题解决方案.

主要成果:

  • 拟议的算法有效地估计了连续时间非线性确定性系统的成本函数参数.
  • 与现有方法相比,无模型方法减少了计算复杂性.
  • 部分无模型版本为已知输入动态的系统提供了进一步的效率.

结论:

  • 开发的算法为反强化学习提供了一个广泛适用的和计算效率高的解决方案.
  • 它的有效性通过模拟得到验证,表明在自主系统和机器人技术中对现实世界的应用有潜力.