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Control and network performance rely on the design of system architecture, control algorithm and scheduling of network information. In this paper, a two-layer networked learning control system (NLCS) architecture is introduced, achieving better control performance, better interference rejection and increasing the adaptability to varying environment. We establish a multi-objective optimization (MOO)...
This paper presents a novel iterative learning control method based on the research of grey theory. The grey predictor is applied to extract key information and reduce the randomness of the measured non-stationary time series signals from sensors, and send the prediction information to the iterative learning controller. This design can not only reduce the trajectory tracking error of reference input...
The precision of gyro is one of the most important points to decide precision of inertial navigation system and it is mostly effected by the gyro random drift. A kind of BP adaptive fuzzy method using minimum standard deviation estimator to identify gyro random drift is proposed in the paper, and the mathematical model is established. It makes the best of language information and data information,...
This paper presents a kind of multiple model adaptive controller based on fuzzy supervisor for the control system of robots with uncertain parameters. The controller uses the structure of model reference control system and adopts a fuzzy supervisor who controls the switching of respective control models instead of tuning dynamically the parameters of controller to tune control input. So a lot of calculations...
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