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A New Algorithm of Robust H_∞ Filter with Constraint of Variance

Zhi Guo

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Abstract

H_∞ filtering problem under the constraint of variance was discussed for a class of linear stochastic uncertain system.A new algorithm of robust filter design was proposed which avoids strict constraint in constructing diagonal matrix to meet upper bound of variance.The robust filter with both constraints of variance and H_∞ was given based on linear matrix inequality(LMI).Comparing with other existing filtering algorithms,less filtering gain and less filtering variance were achieved under the allowed free parameter condition.And the numeric example proves the validity of this method.

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H_∞ filtering problem under the constraint of variance was discussed for a class of linear stochastic uncertain system.A new algorithm of robust filter design was proposed which avoids strict constraint in constructing diagonal matrix to meet upper bound of variance.The robust filter with both constraints of variance and H_∞ was given based on linear matrix inequality(LMI).Comparing with other existing filtering algorithms,less filtering gain and less filtering variance were achieved under the allowed free parameter condition.And the numeric example proves the validity of this method.

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Available abstract

H_∞ filtering problem under the constraint of variance was discussed for a class of linear stochastic uncertain system.A new algorithm of robust filter design was proposed which avoids strict constraint in constructing diagonal matrix to meet upper bound of variance.The robust filter with both constraints of variance and H_∞ was given based on linear matrix inequality(LMI).Comparing with other existing filtering algorithms,less filtering gain and less filtering variance were achieved under the allowed free parameter condition.And the numeric example proves the validity of this method.

Key concepts: Constraint (computer-aided design), Variance (accounting), Mathematics, Filter (signal processing), Diagonal, Mathematical optimization, Algorithm, Upper and lower bounds

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