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Networked control systems (NCSs) is drawn a lot of attention recently. A new method is proposed in this paper, which involves the predictive control algorithm. A queuing strategy is presented for coping with the networked delay, so that NCSs can be stabilized and achieve quite a good dynamic performance for both linear and nonlinear plants. The advantages of this method are (1) no need to redesign the existed predictive controllers; (2) no requirement of clock synchronization; (3) only slight influence of bad network condition such as package loss. Simulations and experiments are designed to show these advantages.

In this paper, a new method is proposed for transparent fuzzy modeling, which involves the minimum cluster volume (MCV) clustering algorithm. This method enables us to obtain membership functions with less overlap and larger core regions, then both ordinary least square and total least square methods are employed for less bias estimation of the consequent parameters. The characteristic of MCV applied to fuzzy modeling is analyzed from the perspectives of fuzzy modeling, such as the robustness of MCV, the cost function which contributes to simplify the rule base, etc. The modeling result gives easier understanding about the system than other clustering methods, such as available information for linear or nonlinear properties of the system, more accurate local model of the system and linguistic understanding of the system.