Shengguo Cui

Design and implementation of Manned Submersible semi physical simulation system

In order to improve the safety factors of MSV (Manned Submersible Vehicle) and shorten the pilot training cycle, the real hardware and software system combine with the modelling and simulation calculation of its sensors, equipment, actuators submersible ontology and motion status in deep sea environment, realizing the semi physical simulation system. First of all, the system model is built and hardware structure is introduced; then the model calculation software structure is studied, mainly focusing on the motion data simulation; finally open-loop motion data, close-loop motion data, all virtual devices and their network data transmission delay time are estimated. The conclusions that the MSVs semi physical simulation system network performance is better than Jiaolongs and the motion data is favourable are got, realizing the simulation of sensors, equipment, actuators and three-dimensional motion data. This lays foundation for improvement of MSVs safety factors and contraction of pilot training period.

Motion control of thruster-driven underwater vehicle based on Model Predictive Control

Underwater vehicles have been developed over the last six decades for potential uses in scientific, commercial, environmental, and military purposes, and always utilized to perform difficult tasks in cluttered environments such as deep-sea mining, underwater sampling and oceanic investigations. Since underwater vehicles have nonlinear and highly coupled dynamics, motion control can be difficult when completing complex tasks. This paper describes the implementation of a model predictive controller novel in a class of thruster-driven underwater vehicle. In this paper, a constrained discrete-time model predictive control method is employed for the motion control of thruster-driven underwater vehicles. A nonlinear model in six degree of freedom is established based on the dynamics of human occupied vehicle Jiao-long, and converted into a state space model after practical simplification. To ensure the performance of model predictive controller, a full feedback state observer is established to observe the state. In addition, Hildreths quadratic programming algorithm is incorporated for solving the optimal control sequence, which greatly reduced the computation of the model predictive control algorithm. In order to show the effectiveness of the motion control method, simulations based on dynamics of human occupied vehicle Jiao-long are conducted. Performance on depth control, heading control and hovering control are evaluated. All the results demonstrate the effectiveness of the method.

Research on error correction methods for the integrated navigation system of deep-sea human occupied vehicles

Because electromagnetic wave attenuates fast in water, some land navigation equipment cannot be used underwater, so Long Base Line, Doppler Velocity Log and Motion sensor become the main mean of navigation and positioning for NOV. This paper puts forward the corresponding errors compensation methods for the instruments and system that mainly used underwater in order to improve the navigation and positioning accuracy. In this paper, UKF is selected as filtering algorithm to fuse the data after compensation. At last, the numerical experiments showed that the propose approach here is applicable and effective.

Iterated square root unscented Kalman filter and its application in deep sea vehicle navigation

It is of vital importance to develop a high accuracy and fast convergence algorithm in deep sea navigation system, since location is essential for the sake of scientific survey and safety in very hazard environment. Unscented Kalman Filter (UKF) is the type of filter, which is designed in order to overrun this problem. However, in case of state estimation of the Human Occupied Vehicle (HOV) via the sensor data obtained from a Long Baseline (LBL) acoustic positioning system, a Doppler Velocity Log (DVL), a depth sensor and a motion sensor, where the nonlinearity degree of dynamic model is high and the operating environment is complex, UKF may give inaccurate results. In this study an iterated square root Unscented Kalman Filter (ISRUKF) is presented. An iterated measurement update procedure is included to increase the approximation accuracy of nonlinear state estimates, and a square root version of UKF is conducive to guarantee numerical stability of the algorithm. Compared with the UKF and square root Unscented Kalman Filter (SRUKF), used in deep-sea vehicle navigation system, the ISRUKF algorithm has potential advantages in convergence speed and location accuracy. Extensive experiment researches have been conducted by using the data obtained from previous sea trial to demonstrate its superiority.

Adaptive square-root CKF with application to DR/LBL integrated heading estimation for HOV

Dead Reckoning (DR) and Long Base Line (LBL) are a modern method in navigation of Human Occupied Vehicles (HOV). However, the accuracy of DR system would degrade sharply, and due to the obvious error drifts of each unit involved in DR. LBL has the disadvantage of low update frequency. To improve the heading estimation of DR/LBL, this paper proposes an innovative method which could adjust state error variance matrix Q in real time dynamically. Square-root Cubature Kalman filter (SR-CKF) is used to simulate the convergence of the dynamic model of DR. And, Sage-Husa maximum a posterior (MAP) is employed in filtering progress. The simulation results of the adaptive SR-CKF and CKF are compared, which show that the method proposed in this paper can obtain a fairly accurate heading estimation.