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Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) are very practical method to get high precision navigation. GPS/INS data Fusion in navigation has been a research topic for decades. Usually the update time of GPS an INS is different. The update time of GPS is 20HZ, and the time of INS is 100HZ or higher. Since GPS and INS are mainly methods in navigation system. Techniques of fusing GPS and INS data are in high demand in navigation industry. Usually the Karman filter is used to fuse the data from GPS and INS. But the Kalman filter is model-dependent and a priori the model parameters need to be known. In this work, a generic and intelligent approach of GPS and INS data fuse using fuzzy interpolation before Kalman algorithm is proposed. First a simplified interpolation model is introduced in a ordinary GPS and INS data fusion. Second a fuzzy rule-based system is established to fuzzy interpolation with objective fuzzy method.Then the parameters of objective fuzzy system are identified, the best n-rule is determinate and the optimal rule base system is found. Finally this approach is applied on an example for a car GPS and INS data fusion, and the results are calculated with computer system to demonstrate the advantages of this approach.

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Determinationof optimal machining parameters -spindle rate, feed rate, and depth of cut -has been a research topic for decades. Since the parameters of CNC machining significantly influence part machining time, part surface quality, and tool life, techniques of determining optimal machining parameters are in high demand in manufacturing industry. Usually the depth of cut and spindle rate are determined according to machinist manuals before machining; and the feed rate is determined subjectively either by CNC machine operators or programmers. As a result, the feed rate is not optimal in terms of the machining condition at every cutter location, and it is fixed at a conservative value causing longer machining time or shorter tool life. In this work, a generic and intelligent approach of feed rate determination for CNC profile milling is proposed. First a simplified cutting force model is introduced and an example database of machining parameters is presented. Second a fuzzy rule-based system is established to predict the cutting force based on the radial and axial depths of cut, and the assumed feed rate. Then identify the geometric features of the part, calculate the engagement angles of the geometric features, and find optimal feed rates for them. Finally apply this approach on an example part for profile milling, and the results are simulated with CATIA CAD/CAM system to demonstrate the advantages of this approach.

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More and more hybrid electric vehicles are driven since they offer such advantages as energy savings and better active safety performance. Hybrid vehicles have two or more power driving systems and frequently switch working condition, so controlling stability is very important. In this work, a two-stage Kalman algorithm method is used to fuse data in hybrid vehicle stability testing. First, the RT3102 navigation system and Dewetron system are introduced. Second, a modeling of data fusion is proposed based on the Kalman filter. Then, this modeling is simulated and tested on a sample vehicle, using Carsim and Simulink software to test the results. The results showed the merits of this modeling.

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