Adaptive LOS Path Following for a Podded Propulsion Unmanned Surface Vehicle with Uncertainty of Model and Actuator Saturation
Abstract
:1. Introduction
- (1)
- Based on force analysis and MMG (Ship Manoeuvring Mathematical Model Group)separation modeling theory, the podded propulsion USV is proven to be an underactuated system.
- (2)
- An improved LOS algorithm is employed as a navigation strategy for USV, which means that it not only ensures the expected compensation effect, but also avoids the use of expensive sensor equipment.
- (3)
- A novel neural shunting model is adopted to deal with the “explosion of complexity”, which can reduce the computational complexity of the control system.
- (4)
- Model uncertainties and time-varying external disturbances are estimated by the neural network minimum parameter learning method. Compared with RBF and BP, the neural network minimum parameter learning method has a smaller amount of computation.
- (5)
- The auxiliary dynamic system is introduced to prevent the input saturation problem, which is closer to practical engineering.
2. Modeling of USV
2.1. Kinematics Equation
2.2. Kinetic Equation
3. LOS Guidance Algorithms
3.1. Problem Formulation
3.2. Adaptive Compensation of the Sideslip Angle
3.3. Time-Varying Lookahead Distance
- (1)
- and are normalized to ; the data domain of ℑ is .
- (2)
- is equally divided into NB, NS, Z, PS and PB; is equally divided into NB, NS, Z, PS and PB; ℑ is equally divided into VS, S, M, B and VB.
- (3)
- Zadeh and max-min are used for fuzzy reasoning. Meanwhile, the centroid area center of gravity method is used for defuzzification.
4. Control System Design
4.1. Preliminary Knowledge
4.1.1. Neural Network Minimum Parameter Learning Method
4.1.2. Neural Shunting Model
4.1.3. Input Saturation
4.2. Yaw Rate Controller
4.3. Surge Speed Controller
5. Stability Analysis
5.1. Stability of the Controller
5.2. Stability of the Closed-Loop System
6. Numerical Simulations
6.1. Straight-Line Path Following
6.2. Curve Path Following
6.3. Control Parameter Setting Strategy
- (1)
- For the navigation system, larger values of gain and adaptive gain mean that the sideslip angle can converge at a faster rate. Meanwhile, and can also affect the convergence rate. A fast convergence rate means that the sideslip angle can be compensated better, but it certainly adds to the possibility of oscillation or divergence in the navigation system.
- (2)
- Larger gains and do not affect the amplitude of generated control signals in (41) and (47). They are adjusted to get the desired following performance without consideration of the actuator saturation. Nevertheless, larger and may cause unnecessary chattering of the control signals.
- (3)
- Large values of adaptive gains and in (44) and (50) increase the learning speed of the neural network minimum parameter learning method. This means that USV can obtain more accurate following performance. However, too fast a learning speed will affect the stability of the control system. and are used to optimize (44) and (50), respectively.
- (4)
- , A, B and D can affect the response speed of the control system. Faster system response enables USV to reach the reference path in a shorter time, but it also raises the possibility of system instability.
- (5)
- , , and are employed to adjust the auxiliary dynamic system. Only a suitable set of parameters can be used to achieve the desired results.
7. Conclusions
Supplementary Files
Supplementary File 1Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
USV | unmanned surface vehicle |
DOF | degree of freedom |
LOS | line of sight |
UUB | uniformly ultimately bounded |
UGES | uniformly globally exponentially stable |
UGAS | uniformly globally asymptotically stable |
ALOS | adaptive line of sight |
ILOS | integral line of sight |
PLOS | predictor-based line of sight |
DSC | dynamic surface control |
MPC | model predictive control |
RBF | radial basis function |
BP | back propagation |
PP | podded propulsion |
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Mu, D.; Wang, G.; Fan, Y.; Sun, X.; Qiu, B. Adaptive LOS Path Following for a Podded Propulsion Unmanned Surface Vehicle with Uncertainty of Model and Actuator Saturation. Appl. Sci. 2017, 7, 1232. https://doi.org/10.3390/app7121232
Mu D, Wang G, Fan Y, Sun X, Qiu B. Adaptive LOS Path Following for a Podded Propulsion Unmanned Surface Vehicle with Uncertainty of Model and Actuator Saturation. Applied Sciences. 2017; 7(12):1232. https://doi.org/10.3390/app7121232
Chicago/Turabian StyleMu, Dongdong, Guofeng Wang, Yunsheng Fan, Xiaojie Sun, and Bingbing Qiu. 2017. "Adaptive LOS Path Following for a Podded Propulsion Unmanned Surface Vehicle with Uncertainty of Model and Actuator Saturation" Applied Sciences 7, no. 12: 1232. https://doi.org/10.3390/app7121232
APA StyleMu, D., Wang, G., Fan, Y., Sun, X., & Qiu, B. (2017). Adaptive LOS Path Following for a Podded Propulsion Unmanned Surface Vehicle with Uncertainty of Model and Actuator Saturation. Applied Sciences, 7(12), 1232. https://doi.org/10.3390/app7121232