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相关概念视频

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

129
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,...
129
PD Controller: Design01:26

PD Controller: Design

358
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,...
358
Feedback control systems01:26

Feedback control systems

436
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...
436
Controller Configurations01:22

Controller Configurations

153
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...
153
PI Controller: Design01:24

PI Controller: Design

508
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...
508
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

183
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...
183

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相关实验视频

Updated: Sep 17, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
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以事件触发的基于ADP的跟踪控制器,用于具有输入和状态约束的部分未知非线性不确定系统.

Raju Dahal1, Indrani Kar1

  • 1Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India.

Neural networks : the official journal of the International Neural Network Society
|July 1, 2025
PubMed
概括

本研究介绍了一个事件触发的自适应动态编程 (ADP) 框架,用于对具有约束和不确定性的非线性系统进行强大的跟踪控制. 该方法通过使用神经网络和利亚普诺夫理论来确保系统稳定性和边界参数.

关键词:
适应式动态编程是适应式的.由事件触发的事件触发.输入约束的限制部分未知的动力学.安全关键系统安全关键系统国家制约国家制约

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科学领域:

  • 控制系统工程 控制系统工程
  • 非线性系统动态 非线性系统动态
  • 人工智能在控制中

背景情况:

  • 非线性系统经常表现出不确定性和约束,使强大的跟踪控制复杂化.
  • 现有的方法可能会与部分未知的动态和无与伦比的不确定性作斗争.
  • 事件触发控制为提高效率和资源管理提供了潜力.

研究的目的:

  • 为具有无与伦比的不确定性,未知的动态和输入/状态约束的非线性系统开发一个强大的跟踪控制策略.
  • 为此目的设计一个由事件触发的自适应动态编程 (ADP) 框架.
  • 在干扰下确保系统稳定性和参数界限性.

主要方法:

  • 使用识别器神经网络 (NN) 来估计未知的系统动态.
  • 构建一个增强系统并将不确定性分为匹配和不匹配的组件.
  • 使用控制屏障函数 (CBF) 和非二次性成本项开发一种新的事件触发安全的汉密尔顿-雅各比-贝尔曼 (HJB) 方程.
  • 采用一个NN批评器来解决安全的HJB方程和基于Lyapunov的触发规则来更新控制器.

主要成果:

  • 使用利亚普诺夫稳定性理论证明了闭环系统的稳定性.
  • 证明了标识符和关键网络参数仍然具有统一的最终界限 (UUB).
  • 通过模拟验证了拟议的事件触发ADP方法的有效性.

结论:

  • 拟议的事件触发的ADP框架有效地解决了对具有不确定性的受约束非线性系统的强有力的跟踪控制.
  • 结合CBF和ADP,确保安全限制得到满足,同时保持稳定性.
  • 该方法为各种工程应用中的复杂控制问题提供了有希望的解决方案.