数据驱动的代学习模型预测控制与自我修改的先前知识
IEEE transactions on neural networks and learning systems
|September 17, 2024
概括
这项研究引入了一种新的代学习模型预测控制 (ILMPC) 方法. 它通过使用自我修改方案和深度神经网络来提高控制性能,提高了适应不同生产需求的适应性.
科学领域:
- 控制工程 控制工程 控制工程
- 人工智能的人工智能
- 制造系统制造系统的制造
背景情况:
- 代学习模型预测控制 (ILMPC) 对于批量制造是有效的,但在不同的操作条件下扎.
- 现有的ILMPC方法需要相同的试验,在生产要求发生变化时限制其使用.
研究的目的:
- 开发一种可适应的ILMPC策略,能够处理不合规的先前信息和不同的试验条件.
- 增强ILMPC在数字化批量制造过程中的灵活性.
主要方法:
- 在ILMPC中嵌入了数据驱动的自我修改方案,以调整历史数据以适应当前的试验条件.
- 适应性深度神经网络 (DNN) 模仿控制动作并生成用于代学习的参考信号.
- 管控框架用于二维优化,以确保时间域有限的稳定性,减轻DNN近似误差.
主要成果:
- 拟议的ILMPC方法证明了对运行参考和持续时间的重大变化具有卓越的适应性.
- 理论验证证实了开发的ILMPC系统的代域边界融合.
- 对非线性注塑成型工艺的模拟显示了更好的跟踪性能和干扰排斥.
结论:
- 新的ILMPC方法有效地解决了动态制造环境中传统方法的局限性.
- 集成DNN和自我修改方案增强了ILMPC的稳定性和灵活性.
- 该方法确保了时间域稳定性和代域融合,以实现可靠的控制.
相关概念视频
PD Controller: Design
199
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,...
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
199
Time-Domain Interpretation of PD Control
85
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...
85
Control Systems
1.1K
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...
At the heart...
1.1K
PID Controller
110
Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...
110
Feedback control systems
295
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...
295
PI Controller: Design
222
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...
222


