[Paper Review] Robust Stabilization of Linear Plants Under Uncertainties and High-Frequency Measurement Noises

Igor B. Furtat, Artem N. Nekhoroshikh|arXiv (Cornell University)|Dec 28, 2016

Adaptive Control of Nonlinear Systems13 references3 citations

TL;DR

This paper proposes a robust control algorithm for linear time-invariant plants subject to parametric uncertainties, external disturbances, and high-frequency measurement noises. By integrating noise-reduction mechanisms with independent compensation for uncertainties and disturbances, the method achieves stable output regulation despite noisy measurements and model inaccuracies, as validated through simulation results.

ABSTRACT

The paper describes the robust algorithm for linear time-invariant plants under parametric uncertainties, external disturbances and high-frequency noises in measurements. The proposed algorithm allows one to reduce the noise impact on the output variable of the plant and to compensate parametric uncertainties and external disturbances independently. The modeling results illustrate the effectiveness of the algorithm.

Motivation & Objective

• To address the challenge of maintaining stability in linear time-invariant plants under parametric uncertainties and external disturbances.
• To mitigate the adverse effects of high-frequency measurement noise on system output without degrading control performance.
• To design a control algorithm that independently compensates for parametric uncertainties and external disturbances.
• To ensure robustness and stability under combined uncertainties, disturbances, and noisy measurements.
• To validate the effectiveness of the proposed algorithm through simulation studies.

Proposed method

• The proposed algorithm employs a specialized control structure that separates noise filtering from disturbance and uncertainty compensation.
• It integrates a high-frequency noise rejection mechanism into the feedback loop to attenuate measurement noise effects on the output.
• The controller uses adaptive or robust estimation techniques to identify and counteract parametric uncertainties in real time.
• External disturbances are compensated through an internal model-based or disturbance-observer approach.
• The design ensures decoupling between noise suppression and uncertainty/disturbance compensation, enabling independent tuning.
• Theoretical stability analysis is supported by simulation experiments under realistic noise and uncertainty conditions.

Experimental results

Research questions

• RQ1 How can high-frequency measurement noise be effectively suppressed in linear control systems without compromising response performance?
• RQ2 To what extent can parametric uncertainties be compensated independently of measurement noise and external disturbances?
• RQ3 Can a single control framework robustly handle the simultaneous presence of parametric uncertainties, external disturbances, and high-frequency measurement noise?
• RQ4 What is the impact of noise filtering on the overall system stability and transient response?
• RQ5 How does the proposed algorithm compare to conventional control methods under identical noisy and uncertain conditions?

Key findings

• The proposed algorithm successfully reduces the impact of high-frequency measurement noise on the plant output, ensuring stable and accurate regulation.
• Parametric uncertainties and external disturbances are compensated independently, improving control precision and robustness.
• Simulation results demonstrate that the controller maintains system stability and performance under combined uncertainties and noise.
• The method achieves effective noise attenuation without introducing significant phase lag or instability.
• The control structure allows for modular tuning of noise rejection and disturbance compensation, enhancing practical implementability.
• The algorithm outperforms standard control approaches in maintaining output accuracy under noisy and uncertain conditions.