<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Safety-critical and mission-critical systems are often sensitive to functional degradation at the system or component level. Such degradation dynamics are often dependent on system usage (or control input), and may lead to significant losses and a potential system failure. Therefore, it becomes imperative to develop control designs that are able to ensure system stability and performance whilst mitigating the effects of incipient degradation by modulating the control input appropriately. In this context, this paper proposes a novel approach based on an optimal control theory framework wherein the degradation state of the system is considered in the augmented system model and estimated using sensor measurements. Further, it is incorporated within the optimal control paradigm leading to a control law that results in deceleration of the degradation rate at the cost of system performance whilst ensuring system stability. To that end, the speed of degradation and the state of the system in discrete time are considered to develop a linear quadratic tracker (LQT) and regulator (LQR) over a finite horizon in a mathematically rigorous manner. Simulation studies are performed to assess the proposed approach.</p>
</abstract>

Safety-critical and mission-critical systems are often sensitive to functional degradation at the system or component level. Such degradation dynamics are often dependent on system usage (or control input), and may lead to significant losses and a potential system failure. Therefore, it becomes imperative to develop control designs that are able to ensure system stability and performance whilst mitigating the effects of incipient degradation by modulating the control input appropriately. In this context, this paper proposes a novel approach based on an optimal control theory framework wherein the degradation state of the system is considered in the augmented system model and estimated using sensor measurements. Further, it is incorporated within the optimal control paradigm leading to a control law that results in deceleration of the degradation rate at the cost of system performance whilst ensuring system stability. To that end, the speed of degradation and the state of the system in discrete time are considered to develop a linear quadratic tracker (LQT) and regulator (LQR) over a finite horizon in a mathematically rigorous manner. Simulation studies are performed to assess the proposed approach.

Degradation Tolerant Optimal Control Design for Stochastic Linear Systems

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Degradation Tolerant Optimal Control Design for Stochastic Linear Systems"}

Safety-critical and mission-critical systems are often sensitive to functional degradation at the system or component level. Such degradation dynamics are...

Degradation Tolerant Optimal Control Design for Stochastic Linear Systems

Online veröffentlicht: 26. März 2024

Seitenbereich: 5 - 14

Eingereicht: 30. Jan. 2023

Akzeptiert: 24. Sept. 2023

DOI: https://doi.org/10.61822/amcs-2024-0001

Schlüsselwörter
linear quadratic Gaussian control, optimal control, Kalman filter, stochastic linear system, degradation

© 2024 Soha Kanso et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Degradation Tolerant Optimal Control Design for Stochastic Linear Systems

Online veröffentlicht: 26. März 2024

Seitenbereich: 5 - 14

Eingereicht: 30. Jan. 2023

Akzeptiert: 24. Sept. 2023

DOI: https://doi.org/10.61822/amcs-2024-0001

Schlüsselwörterlinear quadratic Gaussian control, optimal control, Kalman filter, stochastic linear system, degradation

© 2024 Soha Kanso et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
linear quadratic Gaussian control, optimal control, Kalman filter, stochastic linear system, degradation