Sun Yushan

Motion Control System Architecture of Underwater Robot

Motion control system architecture of underwater robot is presented, which includes hardware and software architecture. Considering the longitudinal velocity effects on the other dimensions of underwater robot, a nonlinear controller is presented. The stability is verified with Lyapunov function. In order to coordinate motions on different dimensions, a virtual sonar based guidance law is introduced which originates from the behavior of human beings. Finally, the reliability and feasibility of the motion control system are demonstrated by sea trial

Motion Control System of Underwater Robot without Rudder and Wing

Motion control system of underwater robot without rudder and wing is presented with hardware and software architecture. Considering coupling effects and thrust reduction of propellers, the control layer, perception layer and executive layer in underwater robot system architecture are modified. In control layer, a nonlinear controller is presented to handle coupling effects between the longitudinal dimension and other dimensions of underwater robot. The stability of the controller is verified with Lyapunov function. A virtual sonar based guidance law is proposed in perception layer to coordinate motions on different dimensions, which originates from the behavior of human beings. In executive layer a kind of thruster configuration for high speed traveling is introduced to handle thrust reduction of tunnel thrusters. Finally, the reliability and feasibility of the motion control system are demonstrated by sea trial

Design of Semi Physical Motion Simulation System of Underwater Robot

Semi physical motion simulation system of underwater robot is established by combining physical simulation of control layer with virtual simulation of sensory and executive layer. Not only the software logic but also the hardware architecture, information interface and reliability can be verified with the semi physical motion simulation system. The hardware and software architecture of the simulation system are explained in detail. The virtual simulation of hydrodynamics, motion sensors and physical simulation of control layer are described. Finally, the long distance motion simulation and obstacle avoidance simulation experiments are conducted to demonstrate the importance of the system for the success of sea experiments.

Obstacle avoidance of autonomous underwater vehicle based on improved balance of motion

An autonomous underwater vehicle (AUV) generally works in an unknown and complicated ocean environment, so it is essential for AUV to have the safe and reliable ability of autonomous obstacle avoidance. An intelligent obstacle avoidance method for AUV is introduced. The obstacle avoidance strategy based on the method can reflect the dynamic obstacle avoidance ability which combines local obstacle avoidance planning, motion control and hydrodynamic performance of underwater vehicle. The alert safety distance of AUV is computed based on underwater vehicle velocity information and the model based on balance point of motion is constructed. Considering the effects to the ability of obstacle avoidance of AUV, the velocity and energy information of AUV are introduced to obstacle avoidance strategy. Finally, the feasibility of the obstacle avoiding methods is verified by simulation test.

S plane control based on parameters optimization with simulated annealing for underwater vehicle

Nonlinearity of underwater vehicles(UVs) and complexity of working environments make the precise control of AUVs difficult for some of the intelligent operations. Based on the analysis of fuzzy control and combined with the form of PID control, S plane control is a simple and effective method, but parameters must be manually set for its controller. Improved simulated annealing algorithm is introduced to S plane control method to optimize the controllers parameters automatically. The feasibility and advantages of the algorithm has been demonstrated by simulation results.

A Method of Instruction Understanding for AUV Control Layer Based on Attention Mechanism

In order to maintain an appropriate relationship between the planning layer and the control layer of autonomous underwater vehicle(AUV),with Itti visual attention model considered as a reference,a planning instruction understanding (PIU) model based on attention mechanism is presented,and the processes of PIU and the secondary planning are established. The attention focus of planning instruction is obtained using fuzzy reasoning for analyzing the feature and significance of instruction element.Furthermore,the secondary planning is implemented adopting attention focus,which can ensure a higher priority execution of the state concerned by the planning layer.The simulation results demonstrate that the proposed PIU process can not only realize the acquisition of the instruction intention of planning layer,and the planning instruction can be executed more effectively,but also the motion control effect is heightened and the intelligence level of control layer is improved.

Fuzzy Control of Underwater Robots Based on Data Mining

Aiming at high overshoot and steady-state error in fuzzy controller of underwater robots, a new method based on data mining technique was presented. Apply Boolean association rule data mining to mine the polling list of fuzzy control from the database of manual operation record, and simulation and pool experiments were carried out on ship detection underwater robot to verify the feasibility and superiority. The results show that the controller has lower overshoot and good robustness to external disturbances, and the polling list of fuzzy control can be constructed automatically by Boolean association rule data mining, which improves the accuracy and the precision of motion control for underwater robots.