Neural Network Adaptive Attitude Control of Full-States Quad Tiltrotor UAV
Abstract
1. Introduction
- (1)
- Aiming at the problem of control redundancy of quad tiltrotor UAVs, the manipulation strategy under different flight modes is constructed, and the weight matrix is introduced to integrate the helicopter manipulation strategy with the aircraft manipulation strategy, and the control allocation under the transition mode is constructed to solve the problem of control redundancy during the flight of quad tiltrotor UAV.
- (2)
- The RBF neural network is applied to the identification process of the controlled system, and the Jacobi information of the controlled system is obtained. Combined with the parameter update rules, the parameters of the ADRC control module are dynamically updated, which enhances the robustness and anti-disturbance ability of the controller.
- (3)
- The attitude control system of the quad tiltrotor UAV is constructed by a combined ADRC-RBF controller. Under the condition of external disturbance, the attitude control system can stabilize the aircraft, and the anti-disturbance performance can be significantly improved. At the same time, it can realize the smooth transition and flight of the attitude in the full-flight mode.
2. Quad Tiltrotor UAV Modeling
2.1. Flight Dynamics Model
2.2. Manipulation Strategy and Control Allocation
3. ADRC-RBF Adaptive Controller
3.1. Structure of the ADRC Controller
3.1.1. Tracking Differentiator (TD)
3.1.2. Extended State Observer (ESO)
3.1.3. Nonlinear State Error Feedback (NLSEF)
3.2. RBF Neural Network
3.3. ADRC Parameter Update
- (a)
- Initialize the ADRC neural network parameters and RBF neural network parameters, set the simulation duration , start the system operation, and then proceed to step b.
- (b)
- At time , acquire the controlled system’s control input variable , state output variable , and RBF neural network identification error . Then, the RBF neural network carries out forward propagation to compute the neural network output variable and the controlled system’s Jacobian information , then proceeds to step c.
- (c)
- Determine whether the system operation time satisfies the condition . If the condition is satisfied, proceed to step d; otherwise execute step g.
- (d)
- The RBF neural network executes back propagation to compute increments for the updatable parameters, updates parameters , , and in the neural network based on Equation (19), then proceeds to step e.
- (e)
- Obtain the output response error of the controlled system, and determine whether the system’s output state error satisfies the condition . If the condition is satisfied, proceed to step b for the next iteration; if not satisfied, proceed to step f.
- (f)
- The output response error of the controlled system, the Jacobian information from the RBF neural network output, and the function of ESO and NLSEF. The ADRC controller parameters are then updated according to Equation (23), after which the process proceeds to step b for the next iteration cycle.
- (g)
- The simulation execution is terminated.
3.4. ADRC-RBF Attitude Control System for Quad Tiltrotor UAV
4. Simulation Results
4.1. Helicopter Mode
4.2. Airplane Mode
4.3. Full-States Mode
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
ADRC | Active Disturbance Rejection Control |
RBF | Radial Basis Function |
ESO | Extended State Observer |
NLSEF | Nonlinear State Error Feedback |
IAE | Integral Of Absolute Error |
VTOL | Take-Off and Landing |
UAV | Unmanned Aerial Vehicle |
PD | Proportional-Derivative |
PSO | Particle Swarm Optimization |
PID | Proportional-Integral-Derivative |
QRAV | Quadrotor Air Vehicles |
LQR | Linear Quadratic Regulator |
INDI | Incremental Nonlinear Dynamic Inversion |
MPC | Model Predictive Control |
NNC | Neural Network-Based Control |
ANN | Adaptive Neural Network |
RNN | Recurrent Neural Network |
TD | Tracking Differentiator |
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Parameter | Unit | Value |
---|---|---|
Quality | kg | |
Rotor speed | ||
Numbers of blades | ||
Length of front wing | ||
Length of rear wing | ||
Parameter | (°) | Value | Parameter | (°) | Value |
---|---|---|---|---|---|
Parameter | ADRC/ADRC-RBF (No Disturbance) | ADRC/ADRC-RBF (Disturbance) |
---|---|---|
Parameter | ADRC/ADRC-RBF (No Disturbance) | ADRC/ADRC-RBF (Disturbance) |
---|---|---|
Parameter | ADRC/ADRC-RBF (No Disturbance) | ADRC/ADRC-RBF (Disturbance) |
---|---|---|
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He, J.; Ren, B.; Xu, Y.; Zhao, Q.; Du, S.; Wang, B. Neural Network Adaptive Attitude Control of Full-States Quad Tiltrotor UAV. Aerospace 2025, 12, 684. https://doi.org/10.3390/aerospace12080684
He J, Ren B, Xu Y, Zhao Q, Du S, Wang B. Neural Network Adaptive Attitude Control of Full-States Quad Tiltrotor UAV. Aerospace. 2025; 12(8):684. https://doi.org/10.3390/aerospace12080684
Chicago/Turabian StyleHe, Jiong, Binwu Ren, Yousong Xu, Qijun Zhao, Siliang Du, and Bo Wang. 2025. "Neural Network Adaptive Attitude Control of Full-States Quad Tiltrotor UAV" Aerospace 12, no. 8: 684. https://doi.org/10.3390/aerospace12080684
APA StyleHe, J., Ren, B., Xu, Y., Zhao, Q., Du, S., & Wang, B. (2025). Neural Network Adaptive Attitude Control of Full-States Quad Tiltrotor UAV. Aerospace, 12(8), 684. https://doi.org/10.3390/aerospace12080684