Koopman Operator Theory for Hybrid Dynamical Systems with Globally Asymptotically Stable Periodic Orbits

Kernekoncepter

This paper develops a Koopman operator theory for hybrid dynamical systems with globally asymptotically stable periodic orbits (hybrid limit-cycling systems), enabling global analysis of these systems using tools from linear systems theory.

Resumé

Bibliographic Information

Katayama, N., & Susuki, Y. (2024). Koopman Operators for Global Analysis of Hybrid Limit-Cycling Systems: Construction and Spectral Properties [Preprint]. arXiv:2411.04052.

Research Objective:

This paper aims to develop a Koopman operator theory for hybrid dynamical systems, specifically focusing on those exhibiting globally asymptotically stable periodic orbits, termed "hybrid limit-cycling systems." The objective is to leverage the well-established theory of Koopman operators for smooth dynamical systems to analyze the global behavior of these hybrid systems.

Methodology:

The authors utilize the concept of a "hybrifold," a smooth manifold constructed by gluing together manifolds representing the individual modes of the hybrid system. This allows them to extend the smooth Koopman operator theory to the hybrid setting. They rigorously define an observable space that preserves the smooth structure of the hybrifold and demonstrate the existence and uniqueness of Koopman eigenfunctions within this space.

Key Findings:

The paper establishes the existence and uniqueness of Koopman eigenfunctions for hybrid limit-cycling systems under specific conditions (r-nonresonant and spectral spread conditions). These eigenfunctions are shown to capture the global geometric properties of the hybrid system, similar to their counterparts in smooth dynamical systems. Furthermore, the existence of these eigenfunctions implies the existence of linear embeddings for hybrid limit-cycling systems, enabling their analysis using linear systems tools.

Main Conclusions:

The research provides a theoretical foundation for applying Koopman operator methods to analyze hybrid limit-cycling systems. The established framework allows for a global, linear perspective on these systems, potentially facilitating the development of novel analysis and control techniques.

Significance:

This work significantly contributes to the field of hybrid dynamical systems by extending the powerful Koopman operator framework to a broader class of systems. This has implications for various applications, including robotics, power systems, and biological systems, where hybrid limit-cycle behavior is prevalent.

Limitations and Future Research:

The current work focuses specifically on hybrid systems with globally asymptotically stable periodic orbits. Future research could explore extending this framework to hybrid systems with other types of ω-limit sets, such as multiple limit cycles or chaotic attractors. Additionally, investigating the practical implications of this theory for control design and data-driven analysis of hybrid systems presents a promising avenue for future work.

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Koopman Operators for Global Analysis of Hybrid Limit-Cycling Systems: Construction and Spectral Properties

by Natsuki Kata... kl. arxiv.org 11-07-2024

https://arxiv.org/pdf/2411.04052.pdf

Dybere Forespørgsler

How can this Koopman operator framework be extended to analyze hybrid systems with more complex ω-limit sets, such as those exhibiting quasi-periodic behavior or chaotic dynamics?

Extending the Koopman operator framework to hybrid systems with more complex ω-limit sets, like those exhibiting quasi-periodic behavior or chaotic dynamics, presents significant challenges but also exciting opportunities. Here's a breakdown of potential approaches and considerations:
1. Quasi-Periodic Behavior:

Challenge: Quasi-periodicity involves multiple, incommensurate frequencies on an invariant torus. The current framework relies on the existence of a single, well-defined period associated with the limit cycle.
Potential Approaches:

Generalized Isochrons: Explore the concept of generalized isochrons on the torus, capturing the phase dynamics associated with each fundamental frequency.
Multi-Frequency Analysis: Develop spectral analysis techniques that can identify and separate the contributions of multiple frequencies in the Koopman operator spectrum.
Toroidal Coordinates: Consider using toroidal coordinates to parameterize the state space, potentially simplifying the representation of quasi-periodic dynamics.
2. Chaotic Dynamics:

Challenge: Chaotic systems exhibit sensitive dependence on initial conditions and often possess a complex, fractal structure in their ω-limit sets (e.g., strange attractors).
Potential Approaches:

Invariant Measures and Ergodicity: Shift focus from individual trajectories to the evolution of probability densities on the attractor. Investigate the Koopman operator's action on these densities and explore concepts like ergodicity and mixing.
Symbolic Dynamics: Represent the chaotic dynamics using symbolic sequences, potentially allowing for a finite-dimensional approximation of the Koopman operator.
Transfer Operator Approach: Consider using the closely related transfer operator, which acts on probability densities and can provide insights into the long-term statistical behavior of chaotic systems.
General Considerations:

Observable Space: The choice of observable space becomes even more crucial for complex ω-limit sets. Carefully designed observables that capture the relevant geometric and dynamical features of the system will be essential.
Spectral Properties: The Koopman operator spectrum for quasi-periodic or chaotic systems will be more intricate. Advanced spectral analysis techniques may be needed to extract meaningful information.
Numerical Challenges: Computing Koopman operator approximations for high-dimensional systems with complex dynamics is computationally demanding. Efficient numerical methods will be key to practical applications.

Could the assumption of global asymptotic stability of the periodic orbit be relaxed to accommodate systems with locally stable limit cycles or multiple limit cycles?

Relaxing the assumption of global asymptotic stability to accommodate locally stable limit cycles or multiple limit cycles is indeed possible, but it requires careful modifications to the framework:
1. Locally Stable Limit Cycles:

Challenge: The current construction relies on the global asymptotic stability to ensure that all trajectories converge to the limit cycle, simplifying the analysis.
Potential Approaches:

Domain Decomposition: Divide the state space into regions of attraction for each locally stable limit cycle. Construct separate Koopman operators and observable spaces for each region.
Basin Boundaries: Analyze the dynamics on the boundaries between basins of attraction, as these regions can exhibit complex behavior.
2. Multiple Limit Cycles:

Challenge: The presence of multiple limit cycles introduces additional complexity in the global dynamics and the Koopman operator spectrum.
Potential Approaches:

Simultaneous Embedding: Seek a single Koopman operator and observable space that can capture the dynamics of all limit cycles simultaneously. This might require a higher-dimensional embedding space.
Cycle-Specific Analysis: Analyze each limit cycle separately using the existing framework, treating them as individual systems. Then, study the interactions and transitions between these cycles.
General Considerations:

Spectral Interpretation: The Koopman operator spectrum will reflect the presence of multiple stable objects. Eigenvalues and eigenfunctions will be associated with each limit cycle and potentially with the basin boundaries.
Non-Uniqueness: The Koopman eigenfunctions may no longer be globally unique, as different eigenfunctions might correspond to the same eigenvalue but be localized around different limit cycles.
Control Implications: Control strategies will need to account for the possibility of the system converging to different limit cycles depending on the initial condition and control input.

How can the insights gained from the Koopman operator analysis of hybrid limit-cycling systems be leveraged to develop novel control strategies for these systems, particularly in applications like robotics or power electronics?

The Koopman operator analysis of hybrid limit-cycling systems offers valuable insights that can be leveraged to develop novel and effective control strategies, particularly in applications like robotics and power electronics:
1. Trajectory Optimization and Planning:

Leveraging Isochrons: Koopman eigenfunctions, particularly the isochrons, provide a natural way to represent and control the phase of a limit cycle. This is valuable for tasks like gait synchronization in legged robots or optimizing switching times in power electronics.
Predictive Control: Koopman-based models can predict the system's future evolution, enabling the design of model predictive control (MPC) strategies that optimize performance over a finite horizon while respecting the hybrid nature of the system.
2. Stabilization and Robustness:

Controller Design in Koopman Space: Design controllers directly in the Koopman-transformed space, where the dynamics are linear. This can simplify the control design process and potentially lead to globally optimal solutions.
Robustness Analysis: Koopman operator methods can be used to analyze the robustness of limit cycles to disturbances or uncertainties in the system parameters. This information can guide the design of robust controllers.
3. Gait Design and Switching Control:

Robotics: In legged robots, Koopman analysis can aid in designing energy-efficient gaits by optimizing the timing and coordination of leg movements to exploit the natural dynamics of the system.
Power Electronics: For power converters, Koopman-based methods can optimize switching sequences to minimize losses, improve efficiency, and ensure stable operation.
Specific Applications:

Legged Robot Locomotion: Design controllers that stabilize desired gaits, synchronize leg movements, and enable robust locomotion over uneven terrain.
Power Converters: Develop control strategies that regulate output voltage or current, minimize switching losses, and ensure stable operation under varying load conditions.
Biological Systems: Gain insights into the control mechanisms of rhythmic biological processes, such as heartbeat regulation or neural oscillations.
Advantages of Koopman-Based Control:

Global Perspective: Koopman operators capture the global dynamics of the system, potentially leading to more effective control strategies compared to local linearization techniques.
Linearity: The linear nature of the Koopman operator in the transformed space simplifies control design and analysis.
Data-Driven Applications: Koopman models can be learned from data, making them suitable for systems where an accurate first-principles model is difficult to obtain.