Fault-Tolerant Active Disturbance Rejection Control of Plant Protection of Unmanned Aerial Vehicles Based on a Spatio-Temporal RBF Neural Network
Abstract
:1. Introduction
- (1)
- An active FTC scheme that can simultaneously adapt to the UAV control system with actuator faults and model uncertainty is proposed. The FTC scheme is resistant to wind disturbance and capable of FTC under time-varying faults. Compared with the ESO in earlier studies [30], our control scheme further optimizes the parameter design. Instead of setting parameters as in conventional methods, there is only a need for the adaptive adjustment of the uncertain terms and disturbance parameters. This leads to the improvement in the system’s robustness.
- (2)
- We propose an RBF neural network for predicting the time series of aircraft state parameters, which can rapidly estimate the fault data and uncertain parameter values. Fault data were used for training the weight parameters of the neural network. The trained model was then employed to estimate the testing data of larger fault values. Through iterative training, the estimate accuracy of the neural network for fault data was improved, thereby improving the fault-tolerant performance.
- (3)
- Rather than the model-based fault observers, the optimized RBF neural network was used in this study to estimate the parameter values in the face of changing parameters of nonlinear terms. The optimized ADRC controller was applied for the overall stability control of the system, thus achieving a rapid and accurate estimate of the actuator fault values with the proposed FTAC.
2. Mathematical Modeling of Uav
2.1. Dynamic Modeling
2.2. Problem Statement
2.2.1. Wind Disturbance Model of the Multi-Rotor UAV
2.2.2. Actuator Fault Model
3. Fault-Tolerant Aircraft Controller Design
3.1. Adrc Controller Design
3.2. Design of the Gradient Descent-Based Spatio-Temporal RBF Neural Network
3.3. Design of Fault-Tolerant Aircraft Control Law
4. Stability Verification
5. Simulation Results
- Scenario 1: Testing the flight effects of an active disturbance rejection, fault-tolerant controller based on a spatio-temporal RBF predictive neural network in the absence of external wind gust disturbances and actuator failures.
- Scenario 2: Numerical simulation of a horizontal wind gust disturbance of 3–9 m/s. The controller’s performance was analyzed on the basis of the effect of the aircraft in a sustained wind disturbance scenario. The performance of the controller was analyzed on the basis of the effects of the aircraft in a wind disturbance situation that lasts for a period of time.
- Scenario 3: In the gust-disturbed environment of Scenario 2, UAV Motors 1–4 were further injected with specified fault values via the UAV’s fault-tolerant control system, corresponding to the gain fault and deviation fault data shown in Table 1. At the same time, an additional 80 dB Gaussian white noise disturbance was added to the fault data during the experiment.
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ADRC | Active Disturbance Rejection Control |
UAV | Unmanned Aerial Vehicles |
FTAC | Fault-Tolerant Aircraft Control |
RBF | Radial Basis Function |
FTC | Fault-Tolerant Control |
DC | Direct-Current |
ESO | Extended State Observer |
SMNLSEF | Sliding Mode Nonlinear State Error Feedback |
ST-RBFNN | Spatio-Temporal Radial Basis Function Neural Network |
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Fault Generation Time (s) | Gain Fault Value | Deviation Fault Values (pwm: 0–150) |
---|---|---|
5 s | 5% | 30 |
13 s | 10% | 50 |
30 s | 15% | 60 |
40 s | 15% | 100 |
50 s | 20% | 150 |
Controler | Settling Time | Overshoot | Angle Error |
---|---|---|---|
Traditional ADRC in Scenario 1 | 2.3 s | 12.3% | 0.3° |
ST-RBFNN ADRC in Scenario 1 | 1.5 s | 0% | 0.04° |
Traditional ADRC in Scenario 2 | 3.2 s | 15.6% | 0.4° |
ST-RBFNN ADRC in Scenario 2 | 2.2 s | 5.3% | 0.12° |
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Hua, L.; Zhang, J.; Li, D.; Xi, X. Fault-Tolerant Active Disturbance Rejection Control of Plant Protection of Unmanned Aerial Vehicles Based on a Spatio-Temporal RBF Neural Network. Appl. Sci. 2021, 11, 4084. https://doi.org/10.3390/app11094084
Hua L, Zhang J, Li D, Xi X. Fault-Tolerant Active Disturbance Rejection Control of Plant Protection of Unmanned Aerial Vehicles Based on a Spatio-Temporal RBF Neural Network. Applied Sciences. 2021; 11(9):4084. https://doi.org/10.3390/app11094084
Chicago/Turabian StyleHua, Lianghao, Jianfeng Zhang, Dejie Li, and Xiaobo Xi. 2021. "Fault-Tolerant Active Disturbance Rejection Control of Plant Protection of Unmanned Aerial Vehicles Based on a Spatio-Temporal RBF Neural Network" Applied Sciences 11, no. 9: 4084. https://doi.org/10.3390/app11094084