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相关概念视频

Linear time-invariant Systems01:23

Linear time-invariant Systems

238
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
238
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

105
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...
105
Open and closed-loop control systems01:17

Open and closed-loop control systems

700
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...
700
State Space Representation01:27

State Space Representation

190
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
190
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

87
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...
87
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

76
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,...
76

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相关实验视频

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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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模型预测控制与变化自动编码器的信号时间逻辑规范.

Eunji Im1, Minji Choi1, Kyunghoon Cho1

  • 1Department of Information and Telecommunication Engineering, Incheon National University, Incheon 22012, Republic of Korea.

Sensors (Basel, Switzerland)
|July 27, 2024
PubMed
概括

本研究介绍了一种基于学习的模型预测控制 (MPC) 方法,用于动态系统. 它使控制器能够优先考虑规则,在不能满足所有规则的情况下模仿人类专家.

科学领域:

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

背景情况:

  • 动态系统经常面临复杂的控制挑战,有多个潜在的冲突规则.
  • 当前的控制策略在无法同时满足时,难以优先考虑规则.
  • 人类专家擅长管理这些基于规则的困境,为自动化提供宝贵的见解.

研究的目的:

  • 为具有差异约束的动态系统开发一种新的控制战略合成方法.
  • 应对不是所有指定的规则可以同时满足任务完成的场景.
  • 为了使控制器能够在规则优先级和管理中模拟人类专家行为.

主要方法:

  • 提出了一种基于学习的模型预测控制 (MPC) 方法,将传统控制与机器学习相结合.
  • 规则通过信号时间逻辑 (STL) 公式正式表示.
  • 一个条件变量自编码器 (CVAE) 从专家的演示中学习一个稳定性边际,以量化规则的满意度.

主要成果:

  • 学习的稳定性边际指导MPC过程,促进规则优先级和排除.
  • 该方法成功地产生了在复杂场景中模仿人类专家的控制行为.
  • 在模拟轨道驾驶环境中有效地管理基于规则的困境.
关键词:
基于深度学习的控制合成.正式的方法 正式的方法基于规则的路径规划规划

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结论:

  • 提出的基于学习的MPC方法为在有冲突规则的系统中进行控制策略合成提供了强大的解决方案.
  • 模拟人类专家决策提高了自主系统的适应性和有效性.
  • 这种方法为开发更智能,更具上下文意识的控制系统提供了框架.