MATLAB/Simulink implementation of an Integrated Flight Control Computer (IFCC) featuring flight control laws, sensor fusion, Extended Kalman Filter (EKF) based state estimation, flight mode management, fault detection, redundancy management, actuator control, and real-time cockpit monitoring.
This project presents a modular Integrated Flight Control Computer (IFCC) developed in MATLAB/Simulink to demonstrate the architecture and functionality of modern digital aircraft flight-control systems.
The IFCC integrates aircraft sensor processing, state estimation, flight control laws, flight mode management, actuator command generation, fault detection, and redundancy management into a unified real-time simulation environment.
The project is designed to demonstrate embedded aerospace control concepts used in fly-by-wire flight-control systems, while maintaining a modular architecture suitable for further development, validation, and hardware implementation.
- Integrated Flight Control Computer architecture
- Extended Kalman Filter (EKF) based state estimation
- IMU, GNSS, and Pitot sensor fusion
- Aircraft state monitoring
- Flight control law implementation
- Flight Mode Manager
- Manual and automatic flight-control modes
- Actuator command generation
- Fault Detection and Isolation (FDI)
- Sensor health monitoring
- Redundancy management
- Sensor fault injection
- Real-time cockpit monitoring
- Flight data logging
- Aircraft visualization
- RMSE-based performance evaluation
The IFCC is organized as a modular flight-control architecture consisting of aircraft dynamics, sensor processing, state estimation, flight-control logic, fault management, actuator control, and cockpit monitoring.
The architecture separates sensing, estimation, decision-making, control, and monitoring functions to provide a structured representation of a modern aerospace embedded control system.
Integrated-Flight-Control-Computer-Simulation
│
├── Simulink_Model/
│ IFCC_Simulation.slx
│
├── MATLAB/
│ run_simulation.m
│ calculate_rmse.m
│ plot_results.m
│
├── Images/
│ Architecture.png
│ CockpitDashboard.png
│ EKF.png
│ FaultDetection.png
│ FlightModes.png
│ FlightControl.png
│
├── Results/
│ NormalFlight.png
│ FaultInjection.png
│ RMSE.png
│
├── Documentation/
│ Project_Report.pdf
│
├── README.md
├── LICENSE
└── .gitignore
Provides the simulated aircraft dynamics and generates the reference flight states used for closed-loop flight-control evaluation.
The aircraft model provides parameters such as:
- Pitch
- Roll
- Yaw
- Altitude
- Airspeed
- Angular rates
- Aircraft position
Simulates aircraft sensor measurements with configurable noise, bias, and faults.
The system includes:
- IMU
- GNSS
- Pitot Tube
The sensor subsystem generates realistic measurement variations that are processed by the IFCC before being used by the flight-control system.
An Extended Kalman Filter (EKF) estimates the aircraft state from noisy sensor measurements.
The EKF combines information from multiple sensors to provide a more reliable estimate of the aircraft's:
- Attitude
- Position
- Altitude
- Airspeed
- Angular states
The estimated states are subsequently used by the flight-control algorithms.
The central IFCC subsystem processes estimated aircraft states and generates appropriate control commands.
The IFCC performs:
- Sensor data processing
- State estimation
- Flight-mode selection
- Flight-control computation
- Fault monitoring
- Sensor redundancy management
- Actuator command generation
This provides a unified representation of an aircraft flight-control computer.
The flight-control subsystem generates control commands based on aircraft state estimates and selected flight modes.
The controller supports closed-loop control of aircraft states such as:
- Pitch
- Roll
- Yaw
- Altitude
- Airspeed
The architecture can be extended with advanced control strategies such as LQR, MPC, or gain-scheduled control.
The Flight Mode Manager determines the active aircraft control mode.
Supported modes include:
- Manual Flight
- Pitch Hold
- Altitude Hold
The active mode determines the corresponding flight-control logic and control objectives.
The fault-management subsystem continuously monitors sensor measurements and system status.
It detects abnormal sensor behavior using threshold-based monitoring and consistency checks.
Supported fault scenarios include:
- IMU failure
- GNSS failure
- Pitot failure
- Sensor measurement deviation
- Sensor signal loss
The Redundancy Manager maintains system operation during sensor failures.
When a faulty sensor is detected:
- The sensor health status is updated.
- The faulty measurement is isolated.
- Backup or alternate measurements are selected.
- Valid measurements continue to be supplied to the estimation and control system.
This demonstrates a simplified form of fault-tolerant aerospace control architecture.
The actuator-control subsystem converts flight-control commands into simulated actuator inputs.
The actuator interface represents control surfaces such as:
- Elevator
- Aileron
- Rudder
The subsystem can be extended to include actuator saturation, rate limits, failures, and dynamic actuator models.
The cockpit dashboard provides real-time visualization of the aircraft and IFCC status.
Displayed parameters include:
- Pitch
- Roll
- Yaw
- Altitude
- Airspeed
- Flight Mode
- Sensor Health
- Fault Status
- Warning Status
- Control Commands
The simulation includes controlled fault-injection scenarios to evaluate the robustness of the IFCC.
- IMU Failure
- GNSS Failure
- Pitot Failure
- Sensor measurement corruption
- Sensor signal loss
The fault-management architecture detects the abnormal measurement and activates the corresponding redundancy-management response.
This enables evaluation of the system under both nominal and degraded operating conditions.
The IFCC simulation is evaluated using Root Mean Square Error (RMSE) between reference aircraft states and estimated states.
| State | RMSE |
|---|---|
| Pitch | 0.00599 |
| Roll | 0.00626 |
| Yaw | 0.00480 |
| Altitude | 0.01427 |
| Airspeed | 0.01833 |
The low estimation error demonstrates effective sensor fusion and EKF-based state estimation within the simulated flight-control architecture.
Aircraft Dynamics
│
▼
Sensor Simulation
│
├── IMU
├── GNSS
└── Pitot
│
▼
Sensor Validation
│
▼
Fault Detection
│
▼
Redundancy Management
│
▼
EKF State Estimation
│
▼
Flight Mode Manager
│
▼
Flight Control Laws
│
▼
Actuator Commands
│
▼
Aircraft Plant
│
└────────────── Feedback ──────────────┘
The simulation provides quantitative performance analysis using:
- State-estimation RMSE
- Reference vs. estimated states
- Sensor fault response
- Fault detection response
- Flight-mode transitions
- Control-command behavior
- System response during degraded sensor operation
Results can be generated using the MATLAB analysis scripts included in the project.
- MATLAB
- Simulink
- Control Systems
- Extended Kalman Filter (EKF)
- Sensor Fusion
- Flight Dynamics
- Fault Detection and Isolation
- Redundancy Management
- Aerospace Embedded Systems
- Digital Flight Control
- Closed-Loop Control
Open the project directory in MATLAB.
Open:
Simulink_Model/IFCC_Simulation.slx
Execute:
run_simulationExecute:
calculate_rmseExecute:
plot_resultsThe generated results can be stored in the Results/ directory.
This project demonstrates practical understanding of:
- Aerospace embedded systems
- Fly-by-wire control architecture
- Real-time control systems
- Sensor fusion
- State estimation
- Kalman filtering
- Fault-tolerant systems
- Redundancy management
- Flight-control algorithms
- Closed-loop feedback systems
- Simulation-based verification
- System-level validation
- Full nonlinear 6-DOF aircraft dynamics
- Triple-redundant flight-control architecture
- Multi-sensor voting and monitoring
- Advanced sensor fault isolation
- Adaptive Kalman filtering
- LQR flight-control laws
- Model Predictive Control (MPC)
- Gain-scheduled flight-control laws
- Actuator failure simulation
- Hardware-in-the-Loop (HIL) testing
- Automatic code generation
- Embedded C implementation
- AUTOSAR-compatible software architecture
- Real-time target deployment
- DO-178C-oriented verification workflow
HARISH RAJI GOVINDARASSOU & DHANUSRI VEERAPPAN
Master's Students – Embedded Systems
Electronics and Communication Engineering
This project is released under the MIT License.