⚡ electrical engineering

Layered Braking Control Considering Thrust Coupling for Aircraft Engine Ground Test Rigs

This paper proposes a hierarchical braking control strategy for aircraft engine ground test rigs that accounts for jet thrust coupling, utilizing a thrust-corrected dynamic model and coordinated force distribution to significantly improve braking stability and reduce tracking errors compared to conventional and fuzzy control methods.

Original authors: Jingyu ZHAO, Dianmin CHEN, Zebing FAN, Pnegfei CHEN, Hao WANG, Rui GUO, Chenglong YU, Bo HU

Published 2026-07-02

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CC BY 4.0

Original authors: Jingyu ZHAO, Dianmin CHEN, Zebing FAN, Pnegfei CHEN, Hao WANG, Rui GUO, Chenglong YU, Bo HU

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are driving a very special, heavy-duty truck. This isn't a normal truck; it's a mobile laboratory designed to carry a massive jet engine. The goal is to drive the engine around, test it, and then bring it to a safe, smooth stop.

Here is the problem: When a normal car brakes, the weight shifts forward, making the front tires grip the road harder and the back tires lighter. Engineers usually have a "recipe" for how much to brake the front versus the back.

But this jet-engine truck has a secret weapon that acts like a villain: The Jet Engine itself.

The Problem: The "Push-Back" Effect

When the jet engine is running, it doesn't just sit there; it pushes the truck forward with massive force (thrust).

The Analogy: Imagine you are trying to stop a shopping cart while someone is secretly pushing it from behind with a giant fan.
The Physics: Because the engine is high up on the truck, this "push" doesn't just move the truck forward; it tries to flip the front of the truck down into the ground. This makes the front wheels squish into the pavement (gaining grip) and the back wheels lift up (losing grip).
The Danger: If you use a standard braking recipe, you might try to brake the back wheels too hard. But because they are "light" and have no grip, they will lock up, skid, and the truck could spin out of control. Also, this truck uses electric motors on the back wheels to slow down and recharge the battery (regenerative braking). If the back wheels are skidding, you can't use this free energy.

The Solution: A Two-Level "Brain"

The authors of this paper built a new, smarter "brain" for the truck's brakes. They call it a Layered Control Strategy. Think of it like a two-person team managing the brakes:

Level 1: The "Traffic Cop" (Upper Level)

This is the big-picture planner. Its job is to look at the road, the speed, and most importantly, how hard the jet engine is pushing.

What it does: It calculates a new, custom recipe for every single moment. If the jet engine is pushing hard, the Traffic Cop says, "Okay, we need to brake the front wheels more and the back wheels less to keep the back tires from locking up."
The Goal: It balances two things: keeping the truck stable (safety) and saving as much battery energy as possible (efficiency).

Level 2: The "Muscle Manager" (Lower Level)

Once the Traffic Cop decides how much the back wheels need to brake, the Muscle Manager takes over. The back wheels have two ways to stop:

Electric Braking: Using the motor to slow down and recharge the battery (like coasting in a hybrid car).
Hydraulic Braking: The traditional brake pads squeezing the rotors.

What it does: The Muscle Manager acts like a conductor. If the battery is full or the truck is going slow, it says, "Use the electric brakes!" If the battery is full or the truck is stopping hard, it says, "Switch to the hydraulic brakes!" It makes sure these two systems work together smoothly so the braking force doesn't jerk or shake.

The Results: A Smoother Ride

The researchers tested this new system in a computer simulation (a "digital twin" of the truck). They compared their new "Two-Level Brain" against two older methods:

The Fixed Recipe: A simple, unchanging rule (like a car with no computer).
The Basic Fuzzy Logic: A slightly smarter, but still imperfect, computer rule.

The Findings:

Accuracy: The new system was much better at hitting the exact braking target. It reduced errors by 42% to 79% compared to the simple fixed recipe.
Stability: It kept the back wheels from locking up, even when the jet engine was pushing hard.
Adaptability: Whether the truck was going fast or slow, or the battery was full or empty, the new system adjusted instantly.

The Bottom Line

This paper doesn't just say "brakes are important." It solves a specific, tricky problem: How do you stop a vehicle that is being pushed forward by a jet engine while trying to save energy?

By creating a two-layer control system that constantly adjusts the balance between front and back brakes based on the engine's push, the researchers made the test platform safer and more efficient. It's like teaching a heavy truck to dance perfectly, even when a strong wind is trying to push it off the floor.

Technical Summary: Layered Braking Control Considering Thrust Coupling for Aircraft Engine Ground Test Rigs

Problem Statement
Ground testing of aircraft engines is a critical phase for performance verification and health assessment. Mobile transport platforms used for these tests must simulate dynamic runs and bring engines to a complete stop. Unlike conventional vehicles, these platforms operate under continuous external jet thrust during the braking phase. This thrust, acting above the vehicle's center of gravity, generates an additional pitch moment that exacerbates the forward shift of axle load caused by braking inertia. Consequently, the rear axle's adhesion margin is significantly reduced. Traditional braking strategies, which rely on fixed-ratio force distribution or static axle loads, fail under these conditions, leading to a high risk of rear-wheel lockup and limiting the utilization of regenerative braking. Furthermore, existing research on electric vehicle composite braking does not fully account for the interrelated effects of jet thrust, dynamic load transfer, and the specific constraints of regenerative braking in this unique thrust-coupled environment.

Methodology
To address these challenges, the authors propose a hierarchical braking control strategy tailored for thrust-coupled scenarios. The methodology proceeds through three main stages:

Theoretical Modeling: A longitudinal dynamic model was established that explicitly incorporates jet thrust ( $F_t$ ) and its associated pitch moment. This model derives the dynamic normal loads on the front and rear axles, revealing that thrust increases the front axle load while decreasing the rear axle load. Based on this, a "thrust-corrected" ideal braking force distribution curve (I-curve) was derived. This curve shifts downward compared to standard I-curves, indicating that the rear axle must bear a smaller proportion of the braking force to avoid lockup.
Hierarchical Control Architecture:
- Upper-Level (Vehicle Level): An optimization module determines the target braking forces for the front and rear axles. It treats the front-axle braking force distribution coefficient ( $\beta$ ) as an optimization variable. The objective is a multi-objective function balancing braking stability (minimizing deviation from the thrust-corrected ideal curve) and energy recovery (maximizing rear-axle regenerative potential). The optimization is subject to regulatory constraints (ECE standards adjusted for thrust) and physical adhesion limits. A piecewise weighting strategy is employed, prioritizing energy recovery at low braking intensities and stability at high intensities.
- Lower-Level (Execution Level): A coordinated control module manages the rear axle's electromechanical composite braking. It distributes the target rear-axle braking force between regenerative braking (electric motor) and hydraulic-mechanical braking. A fuzzy logic controller dynamically adjusts the allocation coefficient ( $k$ ) based on vehicle speed, braking intensity, and battery State of Charge (SOC), accounting for the different response times of the motor and hydraulic systems.
Validation: A mechatronic co-simulation platform was developed using MATLAB/Simulink and Simscape. The strategy was validated against a fixed-ratio distribution strategy and an initial fuzzy strategy across various operating conditions, including varying braking intensities, initial SOCs, and initial vehicle speeds.

Key Contributions

Thrust-Coupled Dynamic Modeling: The paper establishes a longitudinal dynamic model that accounts for the specific pitch moments induced by high-line-of-action jet thrust, providing a theoretical basis for understanding load transfer in engine test rigs.
Thrust-Corrected Constraints: The authors derive a modified ideal braking force distribution curve that serves as a fundamental constraint for thrust-coupled conditions, demonstrating that conventional fixed-ratio strategies are unsafe in this context.
Hierarchical Control Framework: A novel two-layer control strategy is proposed. The upper layer optimizes front-rear distribution to balance safety and energy recovery under thrust, while the lower layer coordinates electro-mechanical braking on the rear axle to handle actuator dynamics and SOC variations.
Performance Improvement: The study quantifies the improvements in braking force tracking accuracy and stability compared to existing methods.

Results
Joint simulation results demonstrate the efficacy of the proposed strategy:

Tracking Accuracy: Compared to the fixed-ratio strategy, the proposed optimization strategy reduced the rear-axle braking force tracking error by 42.4% to 79.2% across different braking intensities, initial SOCs, and vehicle speeds.
Comparison with Fuzzy Control: Compared to an initial fuzzy strategy, the error was reduced by 18.5% to 36.2%.
Adaptability: The strategy successfully maintained braking stability and adhered to the thrust-corrected safety boundaries even under high braking intensities and high thrust inputs, where fixed-ratio strategies risked rear-wheel lockup.
Energy Recovery: The strategy effectively maximized regenerative braking utilization during low-intensity braking while proactively shifting load to the front axle during high-intensity braking to prevent instability.

Significance
The paper claims that this research addresses a critical gap in the braking control of aircraft engine ground test platforms. By explicitly modeling the coupling between jet thrust and vehicle dynamics, the proposed strategy enables these specialized transport platforms to achieve a balance between braking safety and energy recovery that is unattainable with conventional methods. The work provides a validated framework for ensuring the operational adaptability and stability of mobile test rigs, which are essential for the development and validation of modern high-performance aviation engines. The findings suggest that traditional braking distribution rules are insufficient for thrust-coupled environments and that a dynamic, hierarchical approach is necessary to mitigate the risks of rear-wheel lockup and maximize energy efficiency.