Zheping Yan

Path Following Control of an AUV under the Current Using the SVR-ADRC

A novel active disturbance rejection control (ADRC) controller is proposed based on support vector regression (SVR). The SVR-ADRC is designed to force an underactuated autonomous underwater vehicle (AUV) to follow a path in the horizontal plane with the ocean current disturbance. It is derived using SVR algorithm to adjust the coefficients of the nonlinear state error feedback (ELSEF) part in ADRC to deal with nonlinear variations at different operating points. The trend of change about ELSEF coefficients in the simulation proves that the designed SVR algorithm maintains the characteristics of astringency and stability. Furthermore, the path following errors under current in simulation has proved the high accuracy, strong robustness, and stability of the proposed SVR-ADRC. The contributions of the proposed controller are to improve the characteristics of ADRC considering the changing parameters in operating environment which make the controller more adaptive for the situation.

An Adaptive UKF Based SLAM Method for Unmanned Underwater Vehicle

This work proposes an improved unscented Kalman filter (UKF)-based simultaneous localization and mapping (SLAM) algorithm based on an adaptive unscented Kalman filter (AUKF) with a noise statistic estimator. The algorithm solves the issue that conventional UKF-SLAM algorithms have declining accuracy, with divergence occurring when the prior noise statistic is unknown and time-varying. The new SLAM algorithm performs an online estimation of the statistical parameters of unknown system noise by introducing a modified Sage-Husa noise statistic estimator. The algorithm also judges whether the filter is divergent and restrains potential filtering divergence using a covariance matching method. This approach reduces state estimation error, effectively improving navigation accuracy of the SLAM system. A line feature extraction is implemented through a Hough transform based on the ranging sonar model. Test results based on unmanned underwater vehicle (UUV) sea trial data indicate that the proposed AUKF-SLAM algorithm is valid and feasible and provides better accuracy than the standard UKF-SLAM system.

Simulation model and Fault Tree Analysis for AUV

In this paper, the traditional method of fault tree analysis is briefly introduced. AUV, whose fault tree was built, did not work normally as top event. Using Monte-Carlo random sampling method, we establish the simulation model of the AUV, set up the Fault Tree Analysis simulation MATLAB program and engage in its fault tree quantitative analysis. The life distribution function of the AUV system is obtained through the digital simulation of the FT and the other elements attained are the basic unit importance and the mode importance of the basic components and are working out the Mean Time Between Failure. The specific process of the fault trees digital simulation is researched. And in this process, we can see the contribution of each part to the reliability of the whole system, which is a great help to study the reliability of the AUV system.

A novel two-subpopulation particle swarm optimization

The performance of the particle swarm is mainly influenced by individual particles experience and group experience in the period of evolution for particle swarm optimization. To make full use of the two factors and effectively improve the particle swarm optimization performance, Introduced a novel Two-subpopulation Particle Swarm Optimization, The proportion of individual experience and group experiences is different in each subpopulation swarm. If the proportion of individual experience greater than the group experience, the particle swarm search space abroad, whereas, the proportion of group experience greater than individual experience, the particle swarm search the local area fully. The proposed Two-subpopulation particle swarm optimization combines both advantages, make the search more fully and not easily into the local minimum points. Finally simulations were carried out and the results showed that the proposed Two-subpopulation particle swarm optimization, obviously better than the basic particle swarm algorithm in search precision and stability.

The application of self-tuning fuzzy PID control method to recovering AUV

The dynamic positioning to line strategy and self-tuning fuzzy PID method are used to research on the control of recovering Autonomous Underwater Vehicle (AUV) by underwater ocean workstation in this paper. According to the AUV special shape and motion characters, the mathematic model of AUV is established. According to the high precision of position requirement, the two-point (the bow and the stern) dynamic positioning method (dynamic positioning to line method) is proposed, and self-tuning fuzzy PID theory is used to design the AUV four degrees of freedom controllers. The simulation results demonstrated that self-tuning fuzzy PID controller is more suitable than PID controller to solve the problems.

Research on dive plane trajectory tracking control method of AUV under current disturbance

In order to realize the dive plane trajectory tracking control of AUV under the current disturbance, state-dependent riccati equation algrithom is presented. When AUV far away from trajectory point, driving pitch angle same as line-of-sight angle, when AUV nearby the trajectory point, make the velocity vector of AUV same as the trajectory tangent vector. In order to realize the rudder saturated constraints problem, introducing the hyperbolic tangent S type function, equivalent substitution rudder Angle variables. Through the pseudo-linearization of dynamic equations, and calculation of Algebraic Riccati equation, the nonlinear feedback control law is obtained. Avoid the errors caused by the traditional processing of linearization. The method is simple in structure, convenient design. The simulation results show that the proposed controllers can effectively overcome current disturbance, realize longitudinal plane trajectory tracking control of AUV.

Nonlinear feedback control for trajectory tracking of an unmanned underwater vehicle

This paper presents a nonlinear input-state feedback linearization controller for tracking trajectory on horizontal plane with the rudder of an unmanned underwater vehicle (UUV). UUV system is strongly nonlinear, but the model is often simplified into linearization under some strict assumptions in some traditional linear control methods for the need of the control laws, and the linearization may induce large modeling errors and cause severe problems in the practical applications. In this paper, an input-state feedback linearization controller is designed to transform the nonlinear UUV model into an equivalent linear model. The trajectory tracking system is confirmed to be stable and UUV tracks trajectory approximately by pole placement through properly choosing the virtual input. Simulation results illustrate the system is stable and has robust with proposed control scheme.

Research on an improved dead reckoning for AUV navigation

It is difficult for Autonomous Underwater Vehicle (AUV) to satisfy the demand of long time navigation because of the installation angles deviation error as well as the measurement error. The data collected by GPS were filtered using Extended Kalman Filter (EKF) prior to being utilized in the paper. Besides, the installation angles deviation error of DVL was compensated. The paper does some deep research on the improved dead reckoning method, and the performance is evaluated using real data collected by an AUV. The presented experimental results verify that the improved dead reckoning method can significantly enhance the accuracy of navigation, which for the vehicle position was estimated to be less than 0.5% both in north and east.

Polar Grid Navigation Algorithm for Unmanned Underwater Vehicles

To solve the unavailability of a traditional strapdown inertial navigation system (SINS) for unmanned underwater vehicles (UUVs) in the polar region, a polar grid navigation algorithm for UUVs is proposed in this paper. Precise navigation is the basis for UUVs to complete missions. The rapid convergence of Earth meridians and the serious polar environment make it difficult to establish the true heading of the UUV at a particular instant. Traditional SINS and traditional representation of position are not suitable in the polar region. Due to the restrictions of the complex underwater conditions in the polar region, a SINS based on the grid frame with the assistance of the OCTANS and the Doppler velocity log (DVL) is chosen for a UUV navigating in the polar region. Data fusion of the integrated navigation system is realized by a modified fuzzy adaptive Kalman filter (MFAKF). By neglecting the negative terms, and using T-S fuzzy logic in the adaptive regulation of the noise covariance, the proposed filter algorithm can improve navigation accuracy. Simulation and experimental results demonstrate that the polar grid navigation algorithm can effectively navigate a UUV sailing in the polar region.

Obstacle Avoidance for Unmanned Undersea Vehicle in Unknown Unstructured Environment

To avoid obstacle in the unknown environment for unmanned undersea vehicle (UUV), an obstacle avoiding system based on improved vector field histogram (VFH) is designed. Forward looking sonar is used to detect the environment, and the divisional sonar modal is applied to deal with the measure uncertainty. To adapt to the VFH, rolling occupancy grids are used for the map building, and high accuracy details of local environment are obtained. The threshold is adaptively adjusted by the statistic of obstacles to solve the problem that VFH is sensitive to threshold. To improve the environment adaptability, the hybrid-behaviors strategy is proposed, which selects the optimal avoidance command according to the motion status and environment character. The simulation shows that UUV could avoid the obstacles fast and escape from the U shape obstacles.