RESEARCH PROPOSAL
Research on an Edge Intelligence-Based Flight Control System for
Unmanned Aerial Vehicles Using Multi-Sensor Fusion and Intelligent
Fault Diagnosis
I. Research Background and Significance
Unmanned Aerial Vehicles (UAVs) now show up everywhere. People use them for crop
monitoring, package delivery, traffic patrol, and search-and-rescue operations. As these
vehicles take on more tasks, their flight control systems face tougher conditions and
higher safety standards. The flight controller acts as the vehicle's brain. Its ability to
sense accurately, decide quickly, and tolerate failures determines whether a mission
succeeds or ends in a crash. But today's flight control systems struggle with three
specific problems. First, no single sensor works well in all conditions. GPS needs clear
sky views. Cameras fail in darkness or glare. IMUs accumulate errors the longer they
run [1], [2], [3]. Build a control system around any one of these, and it will break down
when conditions turn against it. Second, fault diagnosis lacks data. We want to train
models that can spot failures, but we do not have enough real fault examples to learn
from [4], [5]. And even the examples we do have do not represent all flight regimes. A
UAV in a hover behaves differently from one in a sharp turn. Models trained on one set
of maneuvers often fail on another. Third, onboard computers are weak. They have
limited processing power and tight energy budgets. The cloud could do the heavy lifting,
but network delays make cloud inference too slow for real-time control [6], [7], [8].
Three technologies promise to help. Multi-sensor fusion addresses the first problem. It
combines data from IMU, GPS, cameras, and LiDAR. The combined picture is richer
and more reliable than any single sensor's output. This gives the controller a better
estimate of where the vehicle is and what it is doing. Intelligent fault diagnosis tackles
the second problem. It scans flight data for unusual patterns. Deep learning and transfer
learning help it recognize fault signatures from large datasets. The aim is to catch
problems before they escalate. Edge intelligence deals with the third problem. It shifts
AI processing from the cloud to the onboard computer. This removes the round-trip
delay and keeps the vehicle responsive, even when the wireless link is unreliable. Each
of these technologies offers real value. But here is the catch: researchers usually study
them in isolation. Fusion papers focus on perception and perception alone. Diagnosis
papers care about finding faults, but they seldom ask what the fusion module could
contribute. Edge computing papers worry about offloading and resource management,
not about closing the loop between perception, diagnosis, and control. We see this
fragmentation as the real gap. No one has put these three pieces together in a coherent
way. That is exactly where we want to step in. So our plan is simple. We will build a
flight control system that connects all three threads. Fusion will feed diagnosis.
Diagnosis will inform control. Control will complete the loop. The full chain runs from
"perceive and fuse" to "detect and diagnose" to "react and recover." If we get this right,
the benefits are clear: greater autonomy, stronger reliability, and safer flights when
things go wrong in complex environments.
II. Literature Review
2.1 Application of Multi-Sensor Fusion Technology in UAV Flight Control
Multi-sensor data fusion has become a foundational piece in autonomous UAV research.
If you look at the literature, you will notice that work in this area falls into two broad
groups. One group prefers classical filtering methods. The Extended Kalman Filter and
its derivatives are still widely used. These filters compute weighted combinations of
sensor readings by looking at state residuals. Youn et al., for example, combined an
adaptive weighted EKF with a tightly integrated LiDAR-IMU system [9]. Their setup
gave reliable localization and stable flight control even when GPS dropped out. The
other group goes in a different direction—they use deep learning. Over the last few
years, we have seen more and more end-to-end fusion models built on neural networks.
Clark et al. proposed VINet, which reframes fusion as a sequence learning problem [10].
Their network uses convolutional layers to extract visual features and LSTMs to
process IMU time-series data. The result is a close integration of visual and inertial
cues. More recently, Song et al. introduced MD-SAC, an end-to-end controller that
marries multimodal fusion with the Soft Actor-Critic algorithm [11]. Their system takes
perceptual inputs and maps them straight to control outputs. Early results suggest it
generalizes reasonably well across different scenarios. Still, all these frameworks share
a blind spot. They do not monitor the health of individual sensors. When a sensor
degrades or fails under extreme conditions, the network keeps ingesting its faulty
readings. This poisons the fused result. In flight control, that kind of contamination is
not just a performance issue—it is a safety hazard.
2.2 Intelligent Fault Diagnosis for UAV Flight Control Systems
The fault diagnosis literature has moved steadily away from model-based schemes and
toward data-driven alternatives. Song et al. developed a neural adaptive controller for
quadrotors dealing with both model uncertainties and actuator faults [12]. Their
network learns to approximate the unknown dynamics online while simultaneously
estimating the fault's impact on the actuators. The controller maintained stable tracking
under fault conditions in their experiments.
Lien et al. took a somewhat different route [13]. They designed an adaptive observer
specifically for single-rotor failure scenarios. Their framework estimates the fault state
and adjusts the control law accordingly. The system kept the vehicle flying safely even
after a rotor had failed.
But data-driven methods face a recurring problem: they need data, and lots of it. Highquality fault data from real flights is hard to come by. Xiong et al. highlighted this issue
in their recent work [5]. They pointed out that UAVs operate across widely varying
flight regimes, and each regime has its own data characteristics. On top of that, labeling
fault events accurately is expensive and time-consuming. Another limitation is sensor
diversity. Most current approaches rely on only one data source—flight logs or inertial
measurements, for example. They miss the opportunity to cross-check information
across different sensor types.
One more point worth raising here is partial observability. In real operations, UAVs
frequently lose access to certain states. A visual camera may fail in low light. GPS may
drop out indoors or in urban canyons. The control system then has to make decisions
with incomplete information. Zhou et al. tackled this problem using incremental
approximate dynamic programming (iADP) [14]. heir method separates the local
dynamics from the cost-to-go approximation. This separation allows the controller to
learn online from whatever measurements are available, even when only the tracking
error is observable. They tested their approach on a spacecraft attitude control problem
with liquid slosh dynamics. The results were encouraging: the controller tracked its
reference accurately despite unknown, time-varying internal disturbances. For our work,
this offers a useful pointer on how to handle sensor failures without full state
reconstruction.
Computational constraints also matter. Flight controllers run on lightweight embedded
hardware, not server racks. Junell et al. showed that a coarse model can still be useful
in this setting [15]. They used an approximate model to compute gradient directions
and then refined the policy with just a handful of real flights—three to five trials
sufficed. Their approach combines model-based computation with real-world
validation. That combination looks promising for our edge deployment scenario, where
we cannot afford to run hundreds of trials online.
2.3 Edge Intelligence and Its Integration with UAV Flight Control
Edge computing has opened up new possibilities for UAVs. Instead of streaming data
to the cloud and waiting for a response, the vehicle can process data onboard. This cuts
latency and reduces dependence on ground infrastructure. Liu et al. demonstrated a
system architecture that runs perception, learning, and decision-making entirely on the
vehicle's onboard computer [16]. Their setup worked without any ground-station
support during flight. That level of autonomy is exactly what many UAV applications
require.
2.4 Research Gaps
If you step back and look at these three bodies of work together, a few gaps become
hard to ignore. First, sensor fusion and fault diagnosis operate in separate silos. Fusion
algorithms rarely ask whether a sensor is trustworthy before they use its data. They just
take whatever comes in and blend it. Diagnosis algorithms, on the other hand, rarely
tap into the rich feature representations that fusion modules produce. They work with
raw or low-level data instead of leveraging the fused output. These two communities
do not talk to each other much, and the field suffers for it. Second, edge intelligence
has not been fully woven into the flight control loop. Most studies treat edge computing
as an add-on—a way to make existing algorithms run faster, not as something that
changes the control architecture itself. The perception, diagnosis, and control functions
remain loosely coupled at best. They are stitched together rather than designed as an
integrated whole. Third, we lack lightweight diagnostic models that can actually run on
edge hardware. The models that perform well in the lab are often too large and too slow
for onboard deployment. Inference latency becomes a serious bottleneck, particularly
when the system needs to detect and respond to faults within milliseconds. These three
gaps point us toward the space we want to work in.
III. Research Questions and Objectives
3.1 Overall Objective
This study aims to construct an edge-intelligent flight control system for UAVs based
on multi-sensor fusion and intelligent fault diagnosis, achieving a closed-loop
architecture that integrates adaptive fusion perception of multi-source heterogeneous
sensor information, lightweight intelligent fault diagnosis for edge deployment, and
diagnosis-driven fault-tolerant flight control decision-making, thereby significantly
improving UAV autonomous flight capability and system reliability in complex
environments.
3.2 Specific Research Contents
Research Content 1: Adaptive Multi-Sensor Fusion Perception Method for Flight Control
This research investigates time synchronization and spatial calibration techniques for
multi-source heterogeneous sensors (IMU, GPS, vision, LiDAR, etc.) and establishes
an online sensor health status assessment model. Based on this, a health-status-aware
adaptive fusion algorithm is designed—dynamically adjusting fusion weights
according to the real-time confidence levels of each sensor, achieving an intelligent
fusion strategy of "making full use of good data, using less of poor data, and discarding
faulty data." For typical scenarios such as GPS denial and vision failure, a multi-source
information seamless switching mechanism based on state residuals is studied.
Research Content 2: Lightweight Intelligent Fault Diagnosis Model and Edge Deployment
Two hard constraints shape this part of our work: limited onboard computing power
and scarce fault data. We tackle the first constraint with model compression. Pruning,
quantization, and knowledge distillation are the primary techniques we consider. These
methods allow us to shrink the model size while preserving diagnostic accuracy. We
will design a lightweight neural network architecture tailored for flight control sensors
and actuators—small enough to fit on edge hardware, yet capable of reliable fault
recognition. For the second constraint, we turn to synthetic data. Real fault logs from
UAV flights are rare and expensive to obtain. Digital twins and flight control simulators
offer a practical alternative. We can inject fault patterns into simulated flight scenarios
and generate large labeled datasets this way. These synthetic datasets will cover the
typical fault modes we expect to encounter: sensor drift, signal dropout, and actuator
stalling or stuck conditions. With a compressed model trained on synthetic data, we will
then deploy a fault diagnosis engine on edge platforms such as the Jetson series. The
engine must support real-time operation. Our target is millisecond-level latency for both
anomaly detection and fault classification
Research Content 3: Diagnosis-Driven Fault-Tolerant Flight Control Decision Mechanism
The deep coupling mechanism between fault diagnosis results and flight control
decisions is investigated. A control mode switching strategy based on diagnosis
confidence is designed—when sensor faults are detected, automatic switching to
redundant sensor channels or degraded control modes is performed; when actuator
faults are detected, fault-tolerant control compensation is activated. An end-to-end
information link of "perception fusion → fault diagnosis → fault-tolerant control" is
established, achieving autonomous recovery and safe flight of the flight control system
under fault conditions.
Research Content 4: System Integration and Experimental Validation
A hardware-in-the-loop (HIL) simulation platform and a physical validation platform
for UAV edge-intelligent flight control are constructed. The above three modules are
integrated, and system-level tests are conducted under typical fault scenarios (sensor
drift, signal loss, actuator stuck, etc.) to verify the system's real-time performance,
robustness, and reliability.
IV. Research Methodology and Technical Route
This research adopts a four-stage technical route of "simulation modeling → algorithm
development → edge deployment → physical validation":
Stage 1: Simulation Environment Construction and Data Generation.
High-fidelity UAV dynamics models and sensor models are established based on the
Gazebo/Flightmare simulation platform. Typical fault modes are injected, and multisensor fusion datasets and fault diagnosis datasets are generated.
Stage 2: Core Algorithm Development.
(1) For multi-sensor fusion, a factor graph optimization framework is adopted to fuse
multi-source heterogeneous information, combined with a confidence assessment
network for adaptive weighting; (2) for fault diagnosis, a lightweight Transformer or
CNN architecture is designed, combined with transfer learning strategies to alleviate
the data scarcity problem; (3) for fault-tolerant control, a Model Predictive Control
(MPC) framework is adopted to integrate diagnosis results.
Stage 3: Edge Deployment and Optimization.
Inference acceleration tools such as TensorRT and ONNX Runtime are utilized for
quantization and deployment optimization of the diagnostic model. A task scheduling
mechanism is designed to achieve real-time pipeline parallelism of fusion perception,
fault diagnosis, and flight control decision-making.
Stage 4: System Integration and Experimental Validation.
Secondary development is conducted based on the Pixhawk/PX4 open-source flight
controller, integrating edge computing modules. Simulation tests and actual flight tests
are carried out, and various performance metrics are systematically evaluated.
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