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Summary This paper deals with the problem of stability and stabilization for a non‐Newton mechanical system, and the system is described by the so‐called pattern class variable rather than a state or output variable. At the beginning of this paper, the method of pattern‐moving–based dynamics description is introduced, and it describes the dynamic properties of a concerned production process at a larger granularity. Then, a pattern‐moving–based nonlinear state space model is put forward. Moreover, a feature of output‐class partition is defined and extracted, and two constraint conditions are given for the system based on the feature. Through dealing with difference of a Lyapunov function in two cases, new methods of stability and stabilization are proposed by using the LMI and S‐procedure approaches. They guarantee the stability and robust stabilization of the system and present the relationship between the feature of output‐class partition and system stability. A sintering process and numerical examples are used to demonstrate the effectiveness and practicability of the proposed method.

A fully CMOS integrated low TX noise UHF RFID digital transmitter with tunable directional coupler is proposed. The transmitter decreases the AM noise density to 2144.6 dBc/Hz at 10 kHz offset by eliminating noisy conventional analog components and optimizing LO chain. The tunable transformer-based directional coupler which is feasible for CMOS integration exhibits high isolation performance of 255 dB over the entire band. The transmitter with quadrature DPA structure supports DSB/SSB/PR-ASK modulation, and with the directional coupler, it achieves 22.8 dBm peak power with drain efficiency of 37.8%.

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A new type of modeling method is put forward based on pattern recognition (PR) technology for some industrial production processes. The proposed method is a pure data‐driven modeling method since the model is independent of the controlled plant, and it is based on the measured input and output (I/O) data of the controlled plant in a closed loop. Different from the traditional modeling method, the system dynamics is described by I/O classes, which are obtained from raw I/O data through partitioning of the data space respectively and I/O orders of the model resort to the conditional entropy. The covering algorithm based pattern classification (PC) is used to establish the mapping between input and output of the proposed model in metric spaces. The experimental results illustrate the feasibility of the modeling method.