By Panos J. Antsaklis
The world of clever regulate is a fusion of a few study components in engineering desktop technological know-how and arithmetic, which has developed from traditional keep watch over to reinforce the prevailing nonlinear, optimum, adaptive and stochastic regulate tools. clever regulate innovations are at present being applied for closed-loop suggestions regulate in space-based functions, production platforms, robot platforms, avionic structures, between others, to enhance method functionality, reliability and potency. total, the first aim of clever keep an eye on is to reinforce the functionality of the method to the level that it achieves a few point of independent keep an eye on. This paintings presents an advent to, and survey of, the very important and rising quarter of clever keep an eye on through major researchers within the zone. individuals to "An advent to clever and self sustaining keep an eye on" are world-wide specialists who've been invited at the energy in their learn. the basic idea, archictectures and views on clever regulate are awarded. ways to clever regulate, together with specialist regulate, making plans structures, fuzzy keep watch over, neural regulate and studying keep watch over are studied intimately. purposes are brought through robot structures, avionic platforms and failure prognosis for method operations. "An creation to clever and self sustaining regulate" is designed as a reference for execs and educational researchers and will even be used because the beginning for graduate point classes on clever and independent keep an eye on.
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Extra info for An Introduction to Intelligent and Autonomous Control
2)) is obtained at the boundary value of = m . 5). 48) is stabilizable if I − ρE2 H2 H2 E2 is singular, and I − ρE2 H2 H2T E2T ≥ 0. 5). 64) and if I − ρE2 H2 H2T E2T is singular, then Sm (P, ) = ¯T P B ¯m−s B ¯m−s + R ¯ m−s AT P A − P + Q − AT P B m−s −1 ρ ¯ T ¯m−s + N P A. 48), it follows that Sm (P, 1) ≤ Sm (P, 2) if 1 ≥ 2 > 0. 2, and is omitted. 4 presents a design of an optimal guaranteed cost control under the multiplicative gain uncertainties, and the closed-loop value of the cost function J is bounded by the minimal value of xT0 Popt x0 .
The state feedback control designs for optimal guaranteed cost control under the two classes of gain uncertainties are given in terms of solutions to algebraic Riccati equations. The designs are such that the cost of the closed-loop system is guaranteed to be within a certain bound for all admissible uncertainties. The proposed approach in this chapter also provides a basis for solving other related problems that are to be studied in the rest of the book. 1) where xk ∈ Rn is the state and uk ∈ Rm is the control input, and A and B are known constant matrices.
Non-Fragile H∞ Dynamic Output Feedback Controller Design Additive Controller Gain Uncertainty Case In this section, we consider the non-fragile H∞ controller design problem via dynamic measurement feedback with respect to additive controller gain uncertainties. 25) with T >0 R0 = R0 (σ1 ) = I − σ1 D12 H1 H1T D12 ρ 2 T T 2 R01 = R01 (σ2 , ρ2 ) = γ I − D21 L22 L22 D21 > 0 σ2 T C1 ) K = −R2−1 (B2T P + D12 T T D12 R0−1 (σ1 )C1 A¯ = A + σ1 B2 K11 K11 ρ2 −1 T T + B1 R01 (σ2 )D21 L22 L22 C2 . 29) 42 Linear Systems: Non-Fragile Control and Filtering Then, we have the following theorem for the non-fragile H∞ controller design problem with dynamic measurement feedback.
An Introduction to Intelligent and Autonomous Control by Panos J. Antsaklis