Mono- and Multivariable Control and Estimation: Linear, by Eric Ostertag

By Eric Ostertag

This booklet offers many of the layout tools of a state-feedback keep watch over legislations and of an observer. The thought of structures are of continuous-time and of discrete-time nature, monovariable or multivariable, the final ones being of major attention. 3 diverse ways are defined: • Linear layout equipment, with an emphasis on decoupling thoughts, and a basic formulation for multivariable controller or observer layout; • Quadratic optimization equipment: Linear Quadratic keep an eye on (LQC), optimum Kalman filtering, Linear Quadratic Gaussian (LQG) regulate; • Linear matrix inequalities (LMIs) to resolve linear and quadratic difficulties. The duality among keep an eye on and commentary is taken to virtue and prolonged as much as the mathematical area. quite a few routines, all given with their certain ideas, in most cases got with MATLAB, strengthen and exemplify the sensible orientation of this booklet. The courses, created through the writer for his or her fixing, can be found on the web websites of Springer and of MathWorks for downloading. This booklet is concentrated at scholars of Engineering faculties or Universities, on the Master’s point, at engineers aspiring to layout and enforce cutting edge regulate tools, and at researchers.

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Extra info for Mono- and Multivariable Control and Estimation: Linear, Quadratic and LMI Methods

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54), consists in verifying whether the plant possesses exactly n  E zeros. For a discussion about internal stability, see Sect. 15. 6 Complete Modal Control The method described in Sect. 4 allows shifting only a number p of eigenvalues equal to that of the plant inputs, very often lower than the plant order n in the case of MIMO systems. 6 Complete Modal Control 35 complete modal control, also parametric controller design. Unlike the simple modal control, the modal quantities of the closed-loop system, not of the plant, will be used here.

N . 67) represents a system of n linear equations in ( n p ) unknowns. It is thus possible to choose arbitrarily the value of p of them. The resulting system of equations has then one unique solution for the n other unknowns. The arbitrary choice of p unknowns can be spread indifferently over the components of v Li and those of pi . , the choice of making the j-th component of v Li vanish will cause the mode eMLi t to be absent from the j-th component of x . A real example developed in the exercises of this chapter will illustrate this approach.

It is enough then to place closed-loop eigenvalues in Step 1 only on “stable” plant zeros, and to join the eigenvalues which have become free this way with the other ones, which are used in Step 2 of the approach. g. that such left-aside eigenvalues have been assigned to the j-th output. 42). This approach is called partial input-output decoupling. Conclusion: a system can thus be put in a totally decoupled shape, provided it has neither any zero with positive real part (or exterior to the unit circle, for discrete systems), nor any zero which would be common to all the elements of a same row of the plant transfer matrix G OL ( s )  C( sI  A )1 B , this last situation being rarely encountered in practice (see Sect.

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