Consider the continuous-time linear system with the disturbance input w(t) and the measured output y(t) where...
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Consider the continuous-time linear system with the disturbance input w(t) and the measured output y(t) where the transfer function T(s) from w to y is s(s + 1) (s+2)(s + 3)(s? + 6s + 10) T(s) = Also, we assume that there is a measurement noise input v(t). (a) Obtain a state-space model for this system. (b) Is this system stable? (c) Assume that w(t) and i(t) are white-noise processes. Take some sensible impulsive correlation matrices Q and R and generate a Kalman filter for estimating the states of the system. (d) Generate an H filter to estimate the states of the system. (e) Simulate the plant and the filters from (c) and (d) for various types of w(t) and v(t), e.g. white noises, sinusoidal inputs, some other deterministic functions. Compare the performance of the filters. Consider the continuous-time linear system with the disturbance input w(t) and the measured output y(t) where the transfer function T(s) from w to y is s(s + 1) (s+2)(s + 3)(s? + 6s + 10) T(s) = Also, we assume that there is a measurement noise input v(t). (a) Obtain a state-space model for this system. (b) Is this system stable? (c) Assume that w(t) and i(t) are white-noise processes. Take some sensible impulsive correlation matrices Q and R and generate a Kalman filter for estimating the states of the system. (d) Generate an H filter to estimate the states of the system. (e) Simulate the plant and the filters from (c) and (d) for various types of w(t) and v(t), e.g. white noises, sinusoidal inputs, some other deterministic functions. Compare the performance of the filters.
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