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The on-line computational burdens are reduced by pre-computing off-line the sequences of state feedback gains corresponding to the sequences of nested ellipsoids.
Most of the computational burdens are moved off-line by computing a sequence of state feedback control laws corresponding to a sequence of polyhedral invariant sets.
Complete synthesis of these models is still premature and computational burdens are enormous.
However, this approach has some serious disadvantages, including bad reliability and survivability, as well as heavy communication and computational burdens.
The proposed model is a nested optimization and a decomposition technique is then employed to relieve computational burdens.
This grouping technique considerably reduces the computational burdens: for N=8, we need to test 315 representative combinations instead of N!=40,320.
Similar(27)
Computing this factor z t results in heavy computational burden for normalization.
Moreover, it increases computational burden.
The computational burden is thus significantly lower.
This might unnecessarily increase computational burden.
However, it suffers from computational burden.
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