Scalable Computation of Balancing Transformations for Nonlinear Control Systems with Polynomial Nonlinearities

Kernkonzepte

This paper presents a scalable method for computing balancing transformations to enable model reduction of nonlinear control systems with polynomial nonlinearities.

Zusammenfassung

Bibliographic Information:

Corbin, N. A., Sarkar, A., Scherpen, J. M. A., & Kramer, B. (2024). Scalable computation of input-normal/output-diagonal balanced realization for control-affine polynomial systems. arXiv preprint arXiv:2410.22435.

Research Objective:

This paper addresses the challenge of efficiently computing input-normal/output-diagonal balancing transformations for nonlinear control-affine systems with polynomial nonlinearities, a crucial step in model reduction using balanced truncation.

Methodology:

The authors leverage the concept of axis singular value functions and employ a tensor-based approach based on Kronecker product algebra. They derive explicit algebraic equations for the transformation coefficients, enabling a degree-by-degree computation of the polynomial transformation.

Key Findings:

• The paper provides a detailed analysis of the transformation equations in Kronecker product form, revealing their algebraic structure and enabling a scalable implementation.
• The authors present rigorous proofs for the existence of solutions to the transformation equations and analyze the algorithmic complexity of their proposed approach.
• The proposed method is validated through numerical examples, demonstrating its scalability and efficiency compared to previous Taylor series-based approaches.

Main Conclusions:

The paper concludes that the proposed tensor-based method offers a scalable and computationally efficient way to compute balancing transformations for nonlinear systems with polynomial nonlinearities, paving the way for practical application of nonlinear balanced truncation in model reduction of complex systems.

Significance:

This research significantly advances the field of nonlinear model reduction by providing a scalable and practical method for computing balancing transformations, a key bottleneck in applying balanced truncation to large-scale nonlinear systems.

Limitations and Future Research:

The paper focuses on control-affine systems with polynomial nonlinearities. Future research could explore extending the approach to more general nonlinear systems. Additionally, the authors plan to address the challenge of efficiently forming and simulating reduced-order models in future work.

Statistiken

Zitate

Wichtige Erkenntnisse aus

Scalable computation of input-normal/output-diagonal balanced realization for control-affine polynomial systems

by Nicholas A. ... um arxiv.org 10-31-2024

https://arxiv.org/pdf/2410.22435.pdf

Tiefere Fragen

How might this method be adapted for systems with non-polynomial nonlinearities?

Adapting this method for systems with non-polynomial nonlinearities presents a significant challenge. The current approach heavily relies on the analyticity of the system dynamics and energy functions, allowing for their representation as Taylor series. This polynomial structure is fundamental to the derivation of the input-normal/output-diagonal transformation equations. Here are a few potential avenues for adaptation:

Polynomial Approximation: One approach is to approximate the non-polynomial nonlinearities using polynomials. Techniques like Taylor series expansion around an operating point, Chebyshev polynomial approximation, or piecewise polynomial fitting could be employed. The accuracy of the resulting reduced-order model would depend on the approximation quality and the region of interest in the state space.

Kernel Methods: Kernel methods, widely used in machine learning, offer a way to implicitly map the non-polynomial system into a higher-dimensional space where it might exhibit polynomial-like behavior. By choosing an appropriate kernel function, one could potentially apply a modified version of the proposed method in the transformed space.

Nonlinear System Identification: Instead of directly transforming the original system, one could attempt to identify a polynomial system that approximates the input-output behavior of the non-polynomial system. Techniques like NARMAX (Nonlinear Autoregressive Moving Average with eXogenous inputs) modeling could be used for this purpose. The identified polynomial system could then be balanced using the proposed method.

Extension of Balancing Theory: A more fundamental approach would involve extending the nonlinear balancing theory itself to handle non-polynomial systems directly. This would likely require developing new mathematical tools and concepts beyond the current framework based on polynomial representations and the axis singular value functions.

Each of these adaptations has its own set of challenges and limitations. The choice of the most suitable approach would depend on the specific non-polynomial nonlinearities involved, the desired accuracy of the reduced-order model, and the available computational resources.

Could the non-uniqueness of the transformation coefficients be exploited to optimize for specific properties of the reduced-order model?

The non-uniqueness of the transformation coefficients in nonlinear balancing offers an intriguing opportunity for optimization. While any solution to the underdetermined system of equations yields an input-normal/output-diagonal realization, different solutions might lead to reduced-order models with varying properties. This opens up the possibility of tailoring the transformation to enhance specific characteristics of the ROM.
Here are some potential optimization objectives:

Improved Local Accuracy: The transformation could be optimized to minimize the local error between the full-order model and the ROM in a specific region of the state space. This could be particularly useful for applications where accuracy around an operating point is crucial.

Preservation of Specific Dynamics: If certain dynamic features of the original system are of particular interest, the transformation could be optimized to ensure their preservation in the ROM. This might involve minimizing the error in specific frequency bands or preserving the stability margins associated with those dynamics.

Enhanced Robustness: The non-uniqueness could be exploited to improve the robustness of the ROM to uncertainties in the system parameters or external disturbances. This might involve maximizing the stability margin or minimizing the sensitivity of the ROM's output to parameter variations.

Sparsity or Structure Preservation: In some cases, the original system might exhibit sparsity patterns or structural properties that are desirable to maintain in the ROM. The transformation could be optimized to encourage sparsity in the ROM's state-space matrices or to preserve specific interconnection structures.
Implementing such optimization would require formulating appropriate cost functions that capture the desired ROM properties and incorporating them into the solution process for the transformation coefficients. This could involve using constrained optimization techniques or exploring the solution space of the underdetermined system to identify solutions that minimize the chosen cost functions.

How does the concept of balancing in control systems relate to dimensionality reduction techniques used in other fields, such as machine learning?

The concept of balancing in control systems shares intriguing connections with dimensionality reduction techniques used in machine learning, particularly in the context of finding low-dimensional representations that capture essential information.
Shared Goals:

Dimensionality Reduction: Both balancing and dimensionality reduction techniques aim to reduce the complexity of a system while preserving its essential characteristics. In control, this translates to finding a lower-order model that accurately captures the input-output behavior of the original system. In machine learning, it often involves finding a lower-dimensional representation of data that retains the most relevant information for a given task.

Information Preservation: Balancing seeks to preserve controllability and observability properties, which reflect the system's ability to be influenced by inputs and produce informative outputs. Similarly, dimensionality reduction techniques in machine learning aim to preserve the most informative features or patterns in the data, often measured by variance, covariance, or other metrics relevant to the task at hand.
Connections and Differences:

System Dynamics vs. Data Distributions: A key difference lies in the focus. Balancing operates on the system dynamics, aiming to find a low-dimensional representation that captures the essential input-output relationships governed by differential or difference equations. In contrast, dimensionality reduction in machine learning typically operates on data distributions, seeking to find low-dimensional embeddings that capture the most salient features or patterns in the data.

Model-Based vs. Data-Driven: Balancing is a model-based approach, requiring knowledge of the system's governing equations. In contrast, many dimensionality reduction techniques in machine learning are data-driven, relying on observed data to learn low-dimensional representations.
Analogous Concepts:

Principal Component Analysis (PCA): PCA, a widely used dimensionality reduction technique, finds the directions of maximum variance in the data. This has parallels with balancing, which seeks to identify states that are both easily controllable and observable, contributing significantly to the system's input-output energy transfer.

Autoencoders: Autoencoders, a type of neural network, learn compressed representations of data by encoding the input into a lower-dimensional space and then decoding it back to the original space. This process of encoding and decoding has conceptual similarities to the transformation and inverse transformation used in balancing to find a low-dimensional representation of the system dynamics.
In essence, while the specific techniques and mathematical frameworks differ, balancing in control systems and dimensionality reduction in machine learning share the fundamental goal of finding low-dimensional representations that capture the most relevant information for a given task, whether it's controlling a dynamical system or extracting meaningful patterns from data.