Wu Dinghui

Model Reference Fuzzy Adaptive Control of Permanent Magnet Synchronous Motor

Permanent magnet synchronous motors (PMSM) are widely used in industrial drives due to their simple structure, high efficiency and high factor. Variable speed system of PMSM based on vector has owed an important position where has seriously requirement for celerity and precision. Firstly, the controlling characteristics and working principles of PMSM are analyzed, and the PMSM precision mathematical models are established based on the dq reference coordinates by strict analysis and calculation. The fuzzy model reference adaptive control method uses the fuzzy basis function expansion to represent the unknown parameters and change the identification problem from identifying the original unknown parameters to identifying the coefficients of fuzzy basis function expansion. The stability and convergence properties of the controller are ensured. The results of the simulation based on Matlab and dSPACE experiment show the fuzzy adaptive speed controller has high control and parameter track performance.

Fuzzy adaptive control for Wind Energy Conversion System based on model reference

Considering wind power generation system with variable-speed squirrel-cage induction generators (SGIG), a Model Reference Fuzzy Adaptive (MRFA) controller was proposed for harvesting the maximum power from wind, The designed MRFA controller was established by embedding fuzzy controller into model reference adaptive control framework, substituting complicated traditional adaptive rules with the fuzzy inverse model. The simplified control model was applied to the Wind Energy Conversion System (WECS) and the simulation was well done. The simulation results show that the proposed controller has the advantages of shortening the time for system adjustment and quickening the response speed, higher anti -interference ability and adaptability to parameter varation than conventional PID controllers.

Multi-sensor Based Lane Detection and Object Tracking Method

Considering the relation between the movement of vehicles and the lane parameter, a novel fusion method of lane detection and object tracking is proposed. Though millimeter radar can accurately provide longitude range and velocity information of vehicle ahead, it can not recognize lateral position and road state, which makes it easy to loss targets when vehicle ahead turns or changes its lane. To solve this problem, lane information achieved from image is integrated with radar-filtered information. With the selected road shape model and the intensity feature of lane image, an optimization algorithm was established to maximize likelihood function evaluating how well the image gradient data on an assumed lane marking supports a given set of template parameters. Lane curve parameter can be transformed into vehicle front wheel orientation, and finally the fusion estimation of vehicle state can be achieved by UKF (uscented Kalman filter) estimator and least-squares based data fusion algorithm. Simulation results validate the proposed method can improve vehicles pose tracking accuracy significantly.