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ESO-MPC and Differential Flatness-Based Multivariable Control of High-Altitude Chamber Intake Systems.

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Title: ESO-MPC and Differential Flatness-Based Multivariable Control of High-Altitude Chamber Intake Systems.
Authors: Zheng, Bochen1 (AUTHOR) 231027042@fzu.cn, Zhang, Hehong2 (AUTHOR) HZHANG030@e.ntu.edu.sg, Wang, Xin3 (AUTHOR), Zhai, Chao4 (AUTHOR), Lin, Hongyu1 (AUTHOR), Li, Xin1 (AUTHOR)
Source: Journal of Aerospace Engineering. May2026, Vol. 39 Issue 3, p1-15. 15p.
Subjects: Multivariable control systems, Nonlinear systems, Temperature control, Control theory (Engineering), Observability (Control theory), Predictive control systems, Vacuum chambers
Abstract: The intake system of high-altitude simulation chambers is characterized by strong nonlinearities, high coupling effects, and significant external disturbances, posing substantial challenges for achieving precise temperature and pressure regulation. Conventional control strategies frequently encounter challenges in effectively managing the system's complex dynamics and multivariable coupling effects. To address these limitations, this paper presents an integrated control framework combining model predictive control (MPC) and extended state observer (ESO), with the differential flatness theory for simplifying control structure. The proposed methodology first establishes the differential flatness property of the intake system, enabling its transformation into an approximately decoupled canonical form. Based on this foundation, a cascaded control architecture is developed where ESO actively compensates for residual coupling terms and unmodeled disturbances, while MPC operates on the simplified plant model to coordinate multivariable control actions. The integration of ESO-based disturbance estimation with MPC's predictive capabilities can achieve optimal balance between control precision and actuator effort minimization, ensuring high regulation accuracy while mitigating mechanical wear. Comprehensive simulation studies demonstrate that the proposed ESO-MPC scheme achieves 85% reduction in maximum temperature/pressure fluctuations. These results demonstrate an effective framework in handling strongly coupled nonlinear systems while maintaining implementation practicality, thus providing a promising solution for advanced environmental simulation applications. Practical Applications: High-altitude simulation chambers are essential ground-based facilities designed to replicate the pressure and temperature conditions experienced by aircraft engines during high-altitude flight. However, their control systems often suffer from imprecision and accelerated mechanical wear, primarily due to the complex interplay between temperature and pressure variables, nonlinear dynamics, and external disturbances. To address these challenges, this paper introduces an intelligent control strategy that integrates mathematical model simplification, real-time disturbance observation, and predictive optimization. The method begins by transforming the strongly coupled temperature–pressure system into a set of independent linear models, significantly streamlining controller design. A real-time observer is then employed to continuously monitor and offset unknown disturbances and system uncertainties, thereby strengthening control robustness. Furthermore, a predictive optimization mechanism plans actuator movements in advance while satisfying operational constraints, achieving an optimal trade-off between tracking accuracy and mechanical preservation. The proposed approach is applicable to high-altitude test stands, environmental simulation chambers, and thermal vacuum chambers. It offers a practical pathway to improve the reliability, longevity, and energy efficiency of aerospace testing systems, supporting more sustainable and stable testing operations. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:The intake system of high-altitude simulation chambers is characterized by strong nonlinearities, high coupling effects, and significant external disturbances, posing substantial challenges for achieving precise temperature and pressure regulation. Conventional control strategies frequently encounter challenges in effectively managing the system's complex dynamics and multivariable coupling effects. To address these limitations, this paper presents an integrated control framework combining model predictive control (MPC) and extended state observer (ESO), with the differential flatness theory for simplifying control structure. The proposed methodology first establishes the differential flatness property of the intake system, enabling its transformation into an approximately decoupled canonical form. Based on this foundation, a cascaded control architecture is developed where ESO actively compensates for residual coupling terms and unmodeled disturbances, while MPC operates on the simplified plant model to coordinate multivariable control actions. The integration of ESO-based disturbance estimation with MPC's predictive capabilities can achieve optimal balance between control precision and actuator effort minimization, ensuring high regulation accuracy while mitigating mechanical wear. Comprehensive simulation studies demonstrate that the proposed ESO-MPC scheme achieves 85% reduction in maximum temperature/pressure fluctuations. These results demonstrate an effective framework in handling strongly coupled nonlinear systems while maintaining implementation practicality, thus providing a promising solution for advanced environmental simulation applications. Practical Applications: High-altitude simulation chambers are essential ground-based facilities designed to replicate the pressure and temperature conditions experienced by aircraft engines during high-altitude flight. However, their control systems often suffer from imprecision and accelerated mechanical wear, primarily due to the complex interplay between temperature and pressure variables, nonlinear dynamics, and external disturbances. To address these challenges, this paper introduces an intelligent control strategy that integrates mathematical model simplification, real-time disturbance observation, and predictive optimization. The method begins by transforming the strongly coupled temperature–pressure system into a set of independent linear models, significantly streamlining controller design. A real-time observer is then employed to continuously monitor and offset unknown disturbances and system uncertainties, thereby strengthening control robustness. Furthermore, a predictive optimization mechanism plans actuator movements in advance while satisfying operational constraints, achieving an optimal trade-off between tracking accuracy and mechanical preservation. The proposed approach is applicable to high-altitude test stands, environmental simulation chambers, and thermal vacuum chambers. It offers a practical pathway to improve the reliability, longevity, and energy efficiency of aerospace testing systems, supporting more sustainable and stable testing operations. [ABSTRACT FROM AUTHOR]
ISSN:08931321
DOI:10.1061/JAEEEZ.ASENG-6702