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This paper proposes a baseband signal simulation system in complex electromagnetic environment. The proposed system includes the upper computer software and FPGA processor. The former sets characteristic parameters of a radar, and computes control parameters for FPGA processor. FPGA processor real-time generates pulse description word (PDW) of the radar and digital signals of video pulse, and converts digital signals to the pulses. When multiple radars' pulses appear simultaneously, FPGA processor can overlap selected pulses with set parameters, or output a radar according to the priority. Thus this method makes full use of the real-time computing power of FPGA, and meanwhile simulates multiple radars. Compared with the traditional simulation system which pre-calculates, stores and replays electromagnetic environment, the proposed system reduces the complexity and cost, and significantly improves efficiency.

Taking the control aim into the system state equations, a nonlinear affine model of Boost converter working on current conduction mode (CCM) is set up. It is testified that the model is satisfied the controllability condition and the involutivity condition, and the nonlinear system of the CCM Boost converter is said to be input-state linearizable. Further, the nonlinear output function and the state feedback law are derived. The selecting of feedback coefficient is analyzed in details based on conventional classical control theory. The proposed control method do not need voltage PI controller, and is simple compared with other nonlinear control method used in DC-DC converter. Numerical simulation results are also reported, which allows a comparison between the exact linearization approach and other nonlinear control techniques, e.g. passivity-based control, showing its effectiveness.

A new fuzzy system architecture named Adaptive Inverse Fuzzy Control (AFIC) is prompted. Adaptive inverse control, which is introduced by Bernard Widrow and Eugene Walach, is an efficient control theory for dynamic systems. It is also can be used to fuzzy system design. This paper proposes fuzzy system architecture based on the basic idea of AFIC and discusses some basic problems, including a new description of fuzzy model, acquiring of fuzzy model, and controller design method via adaptive inverse control theory. To show the correctness and the efficiency of the new fuzzy system, a real dynamic system is constructed based on the prompted method and the numeric simulation is presented.