Iterative Distributed Moving Horizon Estimation for Linear Systems with Penalties on Both System Disturbances and Noise

المفاهيم الأساسية

The core message of this article is to develop an iterative partition-based distributed moving horizon estimation (DMHE) scheme for linear systems, where the local estimators penalize both system disturbances and measurement noise to provide accurate and stable state estimates.

الملخص

The article presents two DMHE designs for linear systems:

1. DMHE-1 for the unconstrained case:

• The global objective function of centralized MHE is partitioned into local objective functions for the subsystem estimators.
• The local objective functions incorporate penalties on both subsystem disturbances and measurement noise from the interacting subsystems.
• The local estimators are executed iteratively within each sampling period to converge to the centralized MHE estimates.
• Sufficient conditions are derived for the convergence of the subsystem state estimates and the stability of the estimation error dynamics.

2. DMHE-2 for the constrained case:

• Hard constraints on subsystem states and disturbances are incorporated into the local objective functions.
• The local estimators are executed iteratively to solve the constrained optimization problems.
• Stability of the entire distributed estimation scheme is proven for the constrained case.

The proposed DMHE schemes leverage the advantages of distributed architectures, such as improved fault tolerance, computational efficiency, and organizational flexibility, while providing accurate state estimates convergent to the centralized MHE. A benchmark chemical process example is used to illustrate the effectiveness of the proposed methods.

الإحصائيات

The article does not contain any explicit numerical data or metrics. It focuses on the theoretical development and analysis of the proposed distributed estimation algorithms.

اقتباسات

"The major advantage of these iterative distributed state estimation approaches lies in that they have the potential to provide estimates convergent to the centralized counterpart; this is favorable when more accurate estimates or faster convergence are needed."
"In this work, the entire system is partitioned into subsystems that interact with each other. An individual objective function incorporating penalties on both subsystem disturbances and measurement noise is formulated, and local MHE-based estimators are developed to provide estimates of the subsystem states in a collaborative manner."

الرؤى الأساسية المستخلصة من

Iterative distributed moving horizon estimation of linear systems with penalties on both system disturbances and noise

by Xiaojie Li,S... في arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06706.pdf

استفسارات أعمق

How can the proposed DMHE schemes be extended to handle nonlinear systems

To extend the proposed DMHE schemes to handle nonlinear systems, one can consider incorporating nonlinear models for the subsystems and utilizing nonlinear optimization techniques. Instead of linear state-space equations, the subsystem models can be represented by nonlinear differential equations. The objective functions for the local estimators would need to be reformulated to account for the nonlinear dynamics of the system. Techniques such as nonlinear programming and model predictive control can be employed to solve the optimization problems in the DMHE framework for nonlinear systems. Additionally, methods like unscented Kalman filters or particle filters can be used for state estimation in nonlinear systems within the DMHE framework.

What are the potential challenges and limitations of the iterative DMHE approach when applied to large-scale industrial systems with a large number of subsystems

Computational Complexity: As the number of subsystems in a large-scale industrial system increases, the computational complexity of the iterative DMHE approach also grows significantly. The iterative nature of the algorithm requires multiple iterations at each sampling instant, leading to a high computational burden.

Communication Overhead: In a distributed system with a large number of subsystems, the communication overhead between the subsystems can become a challenge. The exchange of state estimates and measurements in real-time over a communication network can introduce delays and potential data loss.

Convergence and Stability: Ensuring the convergence and stability of the estimation error dynamics for a large number of subsystems can be challenging. The interactions between multiple subsystems can lead to complex dynamics that may affect the convergence properties of the iterative DMHE approach.

Model Complexity: Large-scale industrial systems often have complex dynamics and interactions between subsystems. Modeling these dynamics accurately and incorporating them into the DMHE framework can be a challenging task, requiring detailed system identification and modeling efforts.

Can the DMHE framework be integrated with distributed control strategies to enable comprehensive distributed decision-making for complex industrial processes

The DMHE framework can be integrated with distributed control strategies to enable comprehensive distributed decision-making for complex industrial processes. By combining distributed state estimation with distributed control, the system can achieve a higher level of fault tolerance, adaptability, and performance optimization. The distributed control strategies can utilize the state estimates provided by the DMHE framework to make informed decisions and adjustments in real-time. This integration can lead to a more robust and efficient distributed decision-making system for large-scale industrial processes.