The demand of modern technology is highly required by humans. The Linear motor, one of the most significant inventions, has been playing a vital role in driving component. The Structure of the gantry is the main design and the requirement of high bandwidth and rigidity. Twin-linear motors coupled and paralleled with machining beam are to realize one degree-movement. To prevent the marching beam from deformation, the synchronized motion control becomes an important technology for this machine. This thesis solves the problem of the mechanism coupling by using of the synchronized master command approach which integrates the decouple control and internal model control and taking the mechanism beam as an uncertainty. Both system uncertainties and unknown disturbances occurring in actual implementation need to be carefully considered. And the synchronized motion control of the two linear servo systems with mechanism will be investigated. Better synchronization performance for two motors can therefore be anticipated.

As the automotive industry constantly makes technological progress, higher demands are placed on safety, environmentally friendly and durability. Modern vehicles are headed towards increasingly complex system, in terms of both hardware and software making it important to detect faults in any of the components. Monitoring the engine’s health has traditionally been done using expert knowledge and model-based techniques, where derived models of the system’s nominal state are used to detect any deviations. However, due to increased complexity of the system this approach faces limitations regarding time and knowledge to describe the engine’s states. An alternative approach is therefore data-driven methods which instead are based on historical data measured from different operating points that are used to draw conclusion about engine’s present state. In this thesis a proposed diagnostic framework is presented, consisting of a systematically approach for fault classification of known and unknown faults along with a fault size estimation. The basis for this lies in using principal component analysis to find the fault vector for each fault class and decouple one fault at the time, thus creating different subspaces. Importantly, this work investigates the efficiency of taking multiple classifiers into account in the decision making from a performance perspective. Aggregating multiple classifiers is done solving a quadratic optimization problem. To evaluate the performance, a comparison with a random forest classifier has been made. Evaluation with challenging test data show promising results where the algorithm relates well to the performance of random forest classifier.