Nonlinear Model Predictive Control by G. De Nicolao, L. Magni, R. Scattolini (auth.), Frank

By G. De Nicolao, L. Magni, R. Scattolini (auth.), Frank Allgöwer, Alex Zheng (eds.)

During the previous decade version predictive keep an eye on (MPC), also known as receding horizon keep an eye on or relocating horizon keep an eye on, has turn into the popular keep watch over procedure for more than a few of business methods. there were many major advances during this quarter over the last years, essentially the most very important ones being its extension to nonlinear structures. This ebook offers an updated evaluation of the present cutting-edge within the new box of nonlinear version predictive regulate (NMPC). the most subject parts that seem to be of principal value for NMPC are coated, specifically receding horizon keep watch over concept, modeling for NMPC, computational points of online optimization and alertness matters. The e-book comprises chosen papers offered on the overseas Symposium on Nonlinear version Predictive regulate – evaluation and destiny instructions, which happened from June three to five, 1998, in Ascona, Switzerland.
The publication is geared in the direction of researchers and practitioners within the quarter of regulate engineering and keep an eye on conception. it's also suited to postgraduate scholars because the ebook comprises a number of assessment articles that supply an educational creation into a number of the points of nonlinear version predictive keep watch over, together with platforms idea, computations, modeling and applications.

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Feedback min-max model predictive control is far less conservative that open-loop min-max model predictive control. It appears to provide a good solution to the robustness problem, provided it is implementable. The expansion of the decision space complicates significantly the complexity of the optimal control problem. However, it is encouraging to note that a few versions of feedback model predictive control have already appeared in the literature. 5. 1. EXAMPLE 1 Our first example is the interesting proposal recently made in [8].

Also, the concentration in a simple chemically reactive system may be inferred from the reactor temperature, a more easily measured state variable. Even when the state is directly accessible from the process measurements, one still needs to address measurement noise. If the state is static, then the standard approach is to invoke a weighted averaging technique. The theory of point 46 C. V. Rao and J. B. Rawlings estimation is well developed and its application is ubiquitous in scientific inquiry.

Mayne + Vw(x) c W for all x E W. A8a: F(x) ~ f(x, hw(x)) + F(f(x, hw(x) + d) for all (x, d) E W x Vw(x). e. hw 0 maintains the state in W, despite the disturbance, if the initial state is in W. Assumption A8a ensures that F(·) is a robust Lyapunov function (that decreases, depite d, under h w (')) in W. e. X mm := {x I Umm(X) i= 0}. The proofs of the following results are similar to their earlier counterparts. 1. Suppose that Assumptions Al-A6, A 7a and ABa are satisfied and that x E X mm . 14)) is feasible for any x' E F(x, h(x)) and X mm is positively invariant for the system x(k + 1) E F(x(k), h(x(k))) (is robustly positively invariant for the system x(k + 1) = h(x(k)).

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