This paper presents the co-simulation of the self-adjusting fuzzy PI controller to control a two-axes system. Each axis was driven by a permanent magnet linear synchronous motor (PMLSM). The position and speed controller used the fuzzy PI algorithm with parameters adjusted by a radial basis function neural network (RBFNN). The vector control was applied to the decoupled effect of the PMLSM. The field programmable gate array (FPGA) was used to control both axes of the system. The very high-speed integrated circuithardware description language (VHDL) was developed in the Quartus II software environment, provided by Altera, to analyze and synthesize designs. Firstly, the mathematical model of PMLSM and fuzzy PI was introduced. Secondly, the RBFNN adjusted the knowledge base of the fuzzy PI. Thirdly, the motion trajectory was introduced for testing the control algorithm. Fourthly, the implementation of the controller based on FPGA with the FSM method and the structure of co-simulation between Matlab/Simulink and ModelSim were set up. Finally, discussion about the results proved the effectiveness of the control system, determining the exact position and trajectory of the XY axis system. This research was successful in implementing a two-motor controller within one chip.
Compressed Sensing (CS) is a new mathematical concept, which can reconstruct the original signal accurately with lower Nyquist sampling. Besides, multipath arrivals in an Ultra-wideband (UWB) channel have a long time intervals between clusters and rays where the signal takes on zero or negligible values. It is precisely this signal sparsity of the impulse response of the UWB channel that is suitable for the application of Compressed Sensing theory. However, these multipath arrivals mainly depend on the channel models that generate different sparse levels (low-sparse or high-sparse) of the UWB channels according to which, the authors have analysed and chosen the best recovery algorithms which are suitable to the sparse level for each type of channel environment. Criteria for evaluating the algorithms are based on computational complexity, ability to reduce the sampling rate and processing time. In addition, the results of this study are an open topic for further research aimed at creating a optimal algorithm specially for application of CS based UWB systems.
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