28
2 Robust Filtering for Multi-model Jumping System
the estimated states can track the real states smoothly. Moreover, it can be seen that
the designed robust filter guarantees a prescribed H ∞ norm of the operator from the
unknown disturbance to the output error with an attenuation ˜
γ = 5.4281.
2.4 Conclusions
For multi-model jumping system with time-delays and uncertainties, we designed
the robust H ∞ filter and unbiased H ∞ filter. Based on Lyapunov Krasovskii functional approach and LMIs techniques, sufficient conditions on the existence of
robust/unbiased H ∞ filter are presented and proved. Two illustrative examples are
given to demonstrate the effectiveness of the designed approaches.
2 Robust Filtering for Multi-model Jumping System
the estimated states can track the real states smoothly. Moreover, it can be seen that
the designed robust filter guarantees a prescribed H ∞ norm of the operator from the
unknown disturbance to the output error with an attenuation ˜
γ = 5.4281.
2.4 Conclusions
For multi-model jumping system with time-delays and uncertainties, we designed
the robust H ∞ filter and unbiased H ∞ filter. Based on Lyapunov Krasovskii functional approach and LMIs techniques, sufficient conditions on the existence of
robust/unbiased H ∞ filter are presented and proved. Two illustrative examples are
given to demonstrate the effectiveness of the designed approaches.