登录查看更多内容

Last updated on 2024年7月24日

How do you optimize PID parameters using data-driven or adaptive methods?

由人工智能和领英社区提供技术支持

PID controllers are widely used in control systems design to regulate the output of a process based on a desired setpoint and feedback. However, finding the optimal values for the proportional, integral, and derivative gains (Kp, Ki, and Kd) can be challenging, especially when the process dynamics are uncertain, nonlinear, or time-varying. In this article, you will learn how to use data-driven or adaptive methods to tune your PID parameters for stability and performance.

此文章中的业界达人

由社区从 4 条内容中精选。了解更多

Francisco V.

Aerospace Systems | Control systems design, data analytics, advanced technologies
Rafael Nammur

Industrial Cybersecurity | Industrial Control Systems | Honeywell
John S.

Engenheiro de Automa??o| Especialista em Sistemas de controle.

1 Data-driven methods

Data-driven methods use input-output data from the process to estimate the PID parameters without requiring a mathematical model. One of the most popular data-driven methods is the Ziegler-Nichols method, which involves applying a step input to the process and observing the response. Based on the critical gain and critical period of the response, you can calculate the PID parameters using empirical formulas. However, this method may not work well for processes with complex dynamics, noise, or disturbances.

添加您的观点

2 Adaptive methods

Adaptive methods use online feedback and optimization algorithms to adjust the PID parameters in real time according to the changing conditions of the process. One of the most common adaptive methods is the model reference adaptive control (MRAC), which compares the output of the process with the output of a reference model and updates the PID parameters to minimize the error. Another adaptive method is the self-tuning regulator (STR), which uses recursive identification techniques to estimate the parameters of a process model and then designs the PID controller based on the model.

添加您的观点

John S.

Engenheiro de Automa??o| Especialista em Sistemas de controle.
举报内容
Another way to make a PID adaptive is to use fuzzy logic for parameter determination. It is best applied in nonlinear systems but has high adaptability within its operating range.

已翻译

赞

3 Advantages and disadvantages

Both data-driven and adaptive methods have advantages and disadvantages depending on the application and the available resources. Data-driven methods are simple and fast to implement, but they may not capture the full dynamics of the process and may require offline experiments that disrupt the normal operation. Adaptive methods are more robust and flexible, but they may require more computational power and may suffer from stability or convergence issues.

添加您的观点

Francisco V.

Aerospace Systems | Control systems design, data analytics, advanced technologies
举报内容
An advantage of adaptive methods is performance. Adaptive PIDs can guarantee the same performance in variant systems (i.e. different operating conditions, degradation, uncertainty, etc). However, everything comes at a cost and that cost is robustness. The convergence of the parameters could yield problematic conditions in terms of robustness, so the adaptability of the controllers must be bounded and broadly tested. These types of controllers are more suited to process in which a control failure do not yield a safety concern.

已翻译

赞

4 How to choose a method

When deciding between a data-driven or adaptive method, it’s important to consider the type and complexity of the process, the quality and quantity of data, the performance criteria and constraints, and the computational and hardware limitations. Generally, data-driven methods should be used if there is a linear, stable, and well-defined process with sufficient and reliable data. If there is a nonlinear, uncertain, or time-varying process with limited or noisy data, then adaptive methods should be used for a robust and flexible tuning solution.

添加您的观点

5 How to implement a method

To implement a data-driven or adaptive method, you need to begin by defining the input-output variables and sampling rate of the process. Afterwards, you should select a suitable method based on your criteria and constraints. Then, you can apply the method to the process and collect data to calculate or update the PID parameters. Finally, you should test and validate the PID controller performance and stability using metrics such as rise time, overshoot, settling time, steady-state error, and bandwidth.

添加您的观点

6 How to improve a method

To improve a data-driven or adaptive method, you can preprocess the data to remove outliers, noise, or bias. You should also use multiple data sets or experiments to verify the consistency and accuracy of the PID parameters. Additionally, hybrid methods that combine data-driven and adaptive features, such as iterative feedback tuning (IFT) or extremum seeking control (ESC), can be used. Advanced optimization algorithms or techniques, such as gradient descent, genetic algorithms, or fuzzy logic, can be utilized to fine-tune the PID parameters. Lastly, adaptive tuning rules or filters can be applied to prevent excessive or abrupt changes in the PID parameters.

添加您的观点

John S.

Engenheiro de Automa??o| Especialista em Sistemas de controle.
举报内容
In the improvement of adaptive controller performance, a strong emerging trend is the use of bio-inspired algorithms for determining optimal initial values. This approach has great potential, especially in controls such as MRAC, where the initial parameters influence the convergence time of the gains.

已翻译

赞

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Rafael Nammur

Industrial Cybersecurity | Industrial Control Systems | Honeywell
举报内容
Most PID control optimization techniques require some level of disturbance to the process by injecting a step change in the set point, which is prohibitive in a production environment. Alternatively, the online transfer-function calculation and response simulation, combined with a loop performance assessment technique is much easier to validate and implement than other techniques that require a process disturbance.

已翻译

赞

Control Systems Design

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

How do you optimize PID parameters using data-driven or adaptive methods?

1

2

3

4

5

6

7

1 Data-driven methods

2 Adaptive methods

3 Advantages and disadvantages

4 How to choose a method

5 How to implement a method

6 How to improve a method

7 Here’s what else to consider

Control Systems Design

给文章评分

感谢您的反馈

更多Control Systems Design相关文章

更多相关阅读内容