一种基于自适应神经网络的新型时间延迟估计控制,用于受干扰和未知动态影响的非线性系统
Hoai Vu Anh Truong1, Manh Hung Nguyen2, Duc Thien Tran3
1Department of Mechanical Engineering, Pohang University of Science and Technology, Gyeongbuk 37673, South Korea.
ISA transactions
|August 5, 2023
概括
本研究为非线性系统引入了一种自适应的后退无模型控制 (BSMFC). 它提高了跟踪性能,尽管未知的动态和干扰使用神经网络和过.
科学领域:
- 控制系统工程 控制系统工程
- 非线性动力学是一种非线性动力学.
- 人工智能的人工智能
背景情况:
- 高阶非线性系统 (HNS) 经常面临未知的动态和外部干扰的挑战,阻碍精确的跟踪性能.
- 传统的基于模型的控制方法需要准确的系统模型,这些模型通常是不可用的或很难获得复杂的系统.
- 现有的无模型控制策略可能会与高阶非线性系统的复杂性和稳定性作斗争.
研究的目的:
- 为一般高阶非线性系统开发一个基于后退的自适应式无模型控制 (BSMFC) 策略.
- 在存在干扰和非结构化的不确定性时,提高HNS的跟踪性能.
- 为了解决无模型控制技术中固有的设计复杂性和近似错误.
主要方法:
- 拟议的BSMFC集成后退控制 (BSC) 与基于辐射基函数神经网络 (RBFNN) 的时间延迟估计 (TDE) 来处理未知的系统动态.
- 使用命令过 (CF) 方法来减轻通常与BSC设计相关的复杂性爆炸.
- 新的控制规律是为了最大限度地减少近似误差而制定的,以确保控制精度的提高.
主要成果:
- 适应性BSMFC方法论证明了对具有未知的动态和干扰的HNS的有效控制.
- 整合RBFNN-TDE和CF技术成功克服了传统无模型方法的局限性.
- 模拟结果通过利亚普诺夫定理证实了闭环系统的稳定性,并突出了拟议方法的优越性.
结论:
- 开发的自适应BSMFC为控制没有明确的系统模型的高阶非线性系统提供了强大的和有效的解决方案.
- 先进的控制技术的结合显著提高了系统跟踪性能和稳定性.
- 这种方法为复杂的动态环境中的无模型控制应用提供了一个有希望的方向.
相关概念视频
01:21Linear Approximation in Time Domain
101
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,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
101
01:07Time-Domain Interpretation of PD Control
141
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...
Consider the example of control of motor torque. Initially, a positive...
141
01:26Feedback control systems
344
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...
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...
344
01:26Linear Approximation in Frequency Domain
110
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....
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....
110
01:18Second Order systems II
130
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
130
01:23Linear time-invariant Systems
289
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...
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...
289