Research On The Fuzzy Controller Of The Dynamic Positioning System For 1000m Mining Dredger

According to the development of world's economy, most country paid more attention to the exploitation of ocean resources. The 1000m mining dredger is mainly used to mining the Mn resources about 1000-6000m under the sea water. Because the traditional positioning method can not fit the new working condition economically and technically, so this kind of prospector must controlled and guided by a dynamic positioning system.Dynamic positioning system can make the ship use its own impetus to counteract the influence of wind current and wave which act on the ship, and the ship can keep its position and direction without any anchor system. The dynamic positioning system has some advantages like do not limited by the depth of water and very efficiency, it also can make the ship's movements become more precise.Dynamic positioning system of ship include three main parts: measuring system, control system and execution parts, and the control system is the core. In the process of ship's dynamic position, there are many random factors, and the process is nonlinear. The classic PID control is a linear method, so it can't be used in a dynamic positioning system for good result. But the fuzzy control is a kind of intelligent control, it has excellent robustness and can reflect nonlinear system easily, so fuzzy control is suitable for dynamic positioning system.This paper choose the space state model of dynamic positioning system put forward by Thor. I . Fossen. Using its nonlinear viewer and the feedback of output to design the controller, and it also avoid the linearization process of the classic ship moving math model which means the space state model is very practical.Unlike other traditional design method of ship dynamic positioning fuzzy controller. This paper use Tagaki-Sugeno fuzzy logic system to design the fuzzy controller. According to the classic Mandanni fuzzy model, T-S fuzzy model changed the membership functions of the outputs, make it in the form of linear combination. This makes the T-S model is very ideal to use the multiinput/output system controller to control the complex dynamic nonlinear system. It is also because the structure of T-S fuzzy model is very tight and its expression is very efficient, the adaptive technology can be easily applied in the T-S fuzzy controller.A T-S fuzzy model's rules also can be used to describe a static forecast gainer or a dynamic compensator. This compensate structure based on rules is another unique advantages of T-S fuzzy model when it is used to describe a system. This state compensate feedback structure is called parallel distributed compensation. The structure can analysis and design controller based on linear matrix inequality. Another reason of why this model has be chosen is it can make numerical analysis of the stability of the controller.When the fuzzy variable ,the universe of discourse, and the fuzzy rules has be chosen. The LMI toolbox has been used to solve the problem of design the fuzzy controller. This paper has develop a structure of using the viewer to feedback to the controller, and been applied in the simulate of the controller.At the end of the paper, the simulink toolbox of matlab has been used to simulate the controller. The numerical analysis of controller's stability has also been done to prove the controller is stable in the means of Lyapunov.