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相关概念视频

Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

181
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...
181
Feedback control systems01:26

Feedback control systems

429
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...
429
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

125
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,...
125
PI Controller: Design01:24

PI Controller: Design

503
Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
503
PD Controller: Design01:26

PD Controller: Design

353
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,...
353
Controller Configurations01:22

Controller Configurations

151
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...
151

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WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
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对于具有性能约束的非线性系统,通过自适应动态编程实现数据驱动的最佳跟踪控制.

Lulu Zhang1, Huaguang Zhang2, Xiaohui Yue1

  • 1College of Information Science and Engineering, Northeastern University, China.

Neural networks : the official journal of the International Neural Network Society
|July 18, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了对未知非线性系统的数据驱动控制方法,确保在约束范围内进行最佳跟踪. 该方法使用自适应动态编程和神经网络来实现精确的轨迹跟踪和最小化错误.

关键词:
适应性动态编程是适应性的.数据驱动的政策代数据驱动的政策代神经网络的神经网络的神经网络规定的性能要求 规定的性能要求

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科学领域:

  • 控制理论 控制理论
  • 非线性系统是非线性系统.
  • 人工智能的人工智能

背景情况:

  • 对于非线性系统来说,最优的跟踪至关重要,但由于未知的动态和约束因素,这具有挑战性.
  • 现有的方法往往需要系统模型知识或与同时输入和性能限制作斗争.

研究的目的:

  • 为未知非线性系统开发数据驱动的最佳跟踪控制方案.
  • 确保系统状态遵循所需的轨迹,同时尊重输入和性能约束.
  • 为了最大限度地降低控制成本,并保证追踪错误的有限时间融合.

主要方法:

  • 一个有限时间的性能函数被用于错误的融合.
  • 一个非二次性成本函数和修改的汉密尔顿-雅各比-贝尔曼方程解决了输入约束.
  • 具有演员关键神经网络 (NN) 的自适应动态编程算法学习了最佳控制策略.
  • 最小方程方法调整了NN权重,使用收集的数据.

主要成果:

  • 提出的方案有效地实现了对未知的非线性系统的最佳跟踪.
  • 在预定义区域内显示了跟踪错误的有限时间收.
  • 在整个跟踪过程中,输入约束被严格满足.
  • 在楚亚电路上的模拟验证了算法的性能.

结论:

  • 数据驱动的受约束的最佳跟踪控制方案对未知的非线性系统有效.
  • 适应动态编程和NN的集成为控制设计提供了一种强大的方法.
  • 该方法成功地平衡了跟踪性能,成本最小化和约束满足.