Frequency Domain Auto-tuning of Structured LPV Controllers for High-Precision Motion Control

Core Concepts

The author introduces a novel frequency domain auto-tuning technique for structured LPV MIMO controllers, leveraging frequency domain data. The methodology enables modular structured controller synthesis.

Abstract

Motion systems in industrial processes demand high precision and throughput, challenging traditional control methods. The paper proposes a structured feedback control auto-tuning approach using FRF estimates and LPV framework. It addresses challenges in motion control design by automating controller design with local stability guarantees. Key innovations include norm-based magnitude optimization, automated stability check, and modular parameterization.
Modern motion systems face position-dependent effects requiring adaptation of RB decoupling techniques. Automation of motion control design using FRF data is explored to overcome limitations in accommodating MIMO dynamics. The paper presents a novel frequency domain-based auto-tuning approach for LPV MIMO systems, bypassing the need for complex parametric identification while ensuring local stability and performance guarantees.
The study introduces a modular LPV MIMO structured feedback controller parameterization for auto-tuning, dividing the controller into low-frequency LTI and high-frequency LPV components. Stability analysis is enhanced through an extended factorized Nyquist criterion for full block MIMO controllers based on lFRFs. Performance shaping is achieved through norm-based optimization of weighted closed-loop dynamics.
Experimental validation on a moving-magnet planar actuator prototype demonstrates the effectiveness of the proposed approach compared to robust controllers. The LPV controller achieves significant performance improvement in position tracking error during lithographic scanning motions.

Stats

"The worst-case error is reduced from 11.55 × 10−6 rad to 6.78 × 10−6 radians."
"LPV controller outperforms the robust controller with a relative improvement of 43.10%."

Quotes

"The proposed approach automates the controller design while providing local stability and performance guarantees."
"LPV controller achieves a relative performance improvement of 43.10% compared to the synthesized robust controller."

Key Insights Distilled From

Frequency Domain Auto-tuning of Structured LPV Controllers for High-Precision Motion Control

by Yori... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.05878.pdf

Deeper Inquiries

How can this structured feedback control approach be applied to other industrial processes beyond motion systems

The structured feedback control approach outlined in the context can be extended to various industrial processes beyond motion systems. By leveraging frequency response function (FRF) estimates and the linear-parameter-varying (LPV) control framework, this methodology can be adapted for applications such as robotics, aerospace systems, automotive control, and even process industries like chemical or pharmaceutical manufacturing. The modular nature of the controller design allows for flexibility in addressing diverse system dynamics and requirements. For instance, in robotics, where precision and adaptability are crucial, this approach can enhance trajectory tracking and disturbance rejection capabilities. In aerospace systems, it could improve flight stability and maneuverability by tailoring controllers to different operating conditions. Similarly, in automotive control applications like autonomous driving or advanced driver-assistance systems (ADAS), this method could optimize vehicle performance under varying road conditions.

What are potential drawbacks or limitations of relying solely on frequency domain data for auto-tuning controllers

While relying solely on frequency domain data for auto-tuning controllers offers several advantages such as simplifying controller design complexity and providing local stability guarantees without extensive parametric identification efforts; there are potential drawbacks and limitations to consider:

Limited Information: Frequency domain data may not capture all system behaviors comprehensively. It might overlook nonlinearities or time-varying dynamics that could impact controller performance.
Robustness Concerns: Over-reliance on frequency domain information may lead to suboptimal robustness against uncertainties or disturbances not fully captured by FRFs.
Model Mismatch: Inaccuracies in FRF estimation or discrepancies between the actual system behavior and the model derived from FRFs can result in suboptimal controller tuning.
Complex Systems: For highly complex systems with intricate interactions across multiple channels or modes, a simplified frequency-domain representation may not suffice for effective controller synthesis.

How can the concept of modularity in structured controller design be expanded to different types of control systems

The concept of modularity in structured controller design can be expanded to different types of control systems by customizing the interconnection structure based on specific system requirements:

Hierarchical Modularity: Implement hierarchical levels of modularity where each level addresses different aspects of the control problem - from low-level sensor integration to high-level decision-making modules.
Functional Modularity: Divide controllers into functional blocks based on their roles such as setpoint tracking, disturbance rejection, safety monitoring; allowing easy integration or replacement of individual components.
Adaptive Modularity: Incorporate adaptive elements within modular structures to enable dynamic reconfiguration based on changing operating conditions or performance objectives.
4Interdisciplinary Modularity: Extend modularity concepts across interdisciplinary domains like mechatronics where mechanical components interact closely with electronic controls; ensuring seamless integration while maintaining distinct functionalities within each discipline's realm.

By expanding these principles of modularity creatively across diverse control system architectures ranging from simple SISO setups to complex interconnected MIMO configurations will enhance scalability flexibility efficiency during both design implementation phases