By Graham C. Goodwin PhD, José A. De Doná PhD, María M. Seron PhD (auth.)

ISBN-10: 184628063X

ISBN-13: 9781846280634

ISBN-10: 1852335483

ISBN-13: 9781852335489

This publication offers a complete remedy of the rules underlying optimum limited regulate and estimation. The contents development from optimisation concept, fastened horizon discrete optimum regulate, receding horizon implementations and balance stipulations, particular ideas and numerical algorithms, relocating horizon estimation, and connections among restricted estimation and keep an eye on. a number of case reviews and additional advancements illustrate and extend the center principles.

Specific issues lined include:

• an outline of optimisation theory.

• hyperlinks to optimum keep an eye on idea, together with the discrete minimal principle.

• Linear and nonlinear receding horizon limited keep an eye on together with stability.

• restricted keep watch over strategies having a finite parameterisation for particular periods of problems.

• Numerical tactics for fixing restricted optimisation problems.

• Output suggestions optimum limited control.

• restricted kingdom estimation.

• Duality among restricted estimation and control.

• purposes to finite alphabet keep an eye on and estimation difficulties, cross-directional regulate, rudder-roll stabilisation of ships, and keep an eye on over conversation networks.

The ebook supplies a self-contained remedy of the topic assuming that the reader has simple history in structures thought, together with linear regulate, balance and kingdom area tools. it really is appropriate to be used in senior point classes and as fabric for reference and self-study. A significant other web site is consistently up to date through the authors.

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Extra resources for Constrained Control and Estimation: An Optimisation Approach

Example text

Hence, x ¯ is the unique global minimum, and this completes the proof. 44 2. 14) using the cone of feasible directions defined below. 14) to be a convex program. As a consequence of this generality, only necessary conditions for optimality will be derived. 5, we will impose suitable convexity conditions to the problem in order to obtain sufficiency conditions for optimality. 2 (Cones of Feasible Directions and of Improving Directions) Let S be a nonempty set in Rn and let x¯ ∈ cl S. The cone of feasible directions of S at x ¯, denoted by D, is given by D = {d : d = 0, and x¯ + λd ∈ S for all λ ∈ (0, δ) for some δ > 0}.

We observe that strictly quasiconvex functions are not necessarily quasiconvex. However, if f is lower semicontinuous 2 , then it can be shown that strict quasiconvexity implies quasiconvexity. We will next introduce another type of function that generalises the concept of a convex function, called a pseudoconvex function. Pseudoconvex functions share the property of convex functions that, if ∇f (¯ x) = 0 at some point x¯, then x ¯ is a global minimum of f . 8 (Pseudoconvex Function) Let S be a nonempty open set in Rn , and let f : S → R be differentiable on S.

Then, there exists a unique point x 28 2. 2. Examples of cones. minimum distance from y. Furthermore, x ¯ is the minimising point, or closest point to y, if and only if (y − x ¯)t (x − x ¯) ≤ 0 for all x ∈ S. Proof. We first establish the existence of a closest point. Since S = ∅, there exists a point x ˆ ∈ S, and we can confine our attention to the set S¯ = S ∩ {x : y−x ≤ y−x ˆ } in seeking the closest point. In other words, the closest ¯ point problem inf{ y − x : x ∈ S} is equivalent to inf{ y − x : x ∈ S}.

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Constrained Control and Estimation: An Optimisation Approach by Graham C. Goodwin PhD, José A. De Doná PhD, María M. Seron PhD (auth.)

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