Download Receding Horizon Control: Model Predictive Control for State by Wook Hyun Kwon, Soo Hee Han PDF

By Wook Hyun Kwon, Soo Hee Han

Easy-to-follow studying constitution makes absorption of complex fabric as pain-free as attainable Introduces whole theories for balance and value monotonicity for limited and non-linear structures in addition to for linear structures In co-ordination with MATLAB® documents to be had from springeronline.com, routines and examples supply the scholar extra perform within the predictive keep an eye on and filtering options offered

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Additional resources for Receding Horizon Control: Model Predictive Control for State Models (Advanced Textbooks in Control and Signal Processing)

Sample text

The system noise wi ∈ p and the measurement noise vi ∈ q are zero-mean white Gaussian and mutually uncorrelated. The covariances of wi and vi are denoted by Qw and Rv respectively, which are assumed to be positive definite matrices. We assume that these noises are uncorrelated with the initial state xi0 . In practice, the state may not be available, so it should be estimated from measured outputs and known inputs. Thus, a state estimator, called a filter, is needed. This filter can be used for an output feedback control.

E. 150) look like an LQ solution. 147). 151) It is noted that Mi,if is obtained from Ki,if of the LQ control by replacing BR−1 B T by Π. 156) with Here, Qf must be nonsingular. 157) which is required for the existence of the saddle-point. 136) may not be satisfied. That is why the terminal equality constraint for case of the RH H∞ control does not make sense. 150), we now turn to the infinite horizon H∞ control, which is summarized in the following theorem. 5. Suppose that (A, B) is stabilizable and (A, Q 2 ) is observable.

13) is not necessary, which is replaced with xif = xrif . 24) i=i0 where xi ∈ n is the state, ui ∈ m is the input and wi ∈ l is the disturbance. e. ui ∈ U and wi ∈ W. Here, the fixed terminal state is not dealt with because the minimax problem in this case does not make sense. The minimax criterion we are dealing with is related to a difference game. We want to minimize the performance criterion, while disturbances try to maximize one. 25) We may think that u∗ is the best control, while w ∗ is the worst disturbance.

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