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This paper considers the design of fuzzy error governors for multivariable systems subject to input saturation.
Improvements are made to an existing fuzzy error governor, which expands the class of systems to which the technique can be applied.
The goal of this paper is to handle the large variation issues in fuzzy data by constructing a variable spread multivariate adaptive regression splines (MARS) fuzzy regression model with crisp parameters estimation and fuzzy error terms.
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Based on the redistribution of fuzzy errors from the Markov transition technique, the fuzzified form of the forecast error ( mu (e_{n + 1},m) ) is then obtained as mu (e_{n + 1},m) = Pmu (e_{k},m) (9) 2.
Probabilistic record linkage or other "fuzzy", error-tolerant matching techniques would be used for this matching or "lookup" operation, possibly assisted by human intervention where required.
An adaptive robust structure is used to cope with fuzzy approximation error and external disturbances.
The aim of this study is to design and analyze an H∞ filter that can stabilize the T-S fuzzy filtering error dynamics.
According to the analysis of the typical time-delay issue induced by communication network and the parallel distributed compensation (PDC) technique, a fuzzy filtering error system is established.
Also, to cope with fuzzy approximation error and external disturbances an adaptive discontinuous structure is used to make the controller more robust, as long as due to adaptive mechanism attenuates chattering effectively.
Attention is focused on designing the robust L1 filter that guarantees the fuzzy filtering error system to be asymptotically stable and to have a prescribed L1 noise attenuation level γ with respect to all persistent and amplitude-bounded disturbances.
Moreover, in the proposed controller an auxiliary control signal is introduced to attenuate the effect of fuzzy approximation error and to mitigate the effect of external disturbance on the tracking performance.
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