By Rudibert King
The booklet reviews at the most up-to-date theoretical and experimental advances within the box of lively circulation and combustion keep an eye on. It covers new advancements in actuator know-how and sensing, in strong and optimum open- and closed-loop regulate, in addition to in version aid for keep watch over. It collects contributions provided throughout the 3rd version of the energetic circulation and Combustion keep watch over convention, held in September 10-12, 2014 on the Technische Universität Berlin (Germany). This convention, in addition to the learn provided within the booklet, were supported by means of the collaborative examine heart SFB 1029 -Substantial potency bring up in fuel generators via direct use of coupled unsteady combustion and circulate dynamics, funded by way of the DFG (German learn Foundation).
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6. Top: First PC that describes the impact of the actuation on the cp -distribution of the suction surface of the middle blade for diﬀerent maximum actuation amplitudes umax . 85. 8 1 Fig. 7. Top: First (left) and second (right) PC describing the impact of the running disturbance generator on the cp -distribution of the suction surface of the middle blade. 85. |W (z)|, |Wp,m (z)| |Gn (z)|, |Gp,m (z)| Comparison of Iterative Learning and Repetitive Control 100 10−1 10−2 −2 10 |Gn (z)| |Gp,m (z)| 10−1 100 f [Hz] 101 102 49 100 10−1 10−2 −2 10 |W (z)| |Wp,m (z)| 10−1 100 f [Hz] 101 102 Fig.
Section 3 describes the compressor stator cascade in detail and Sec. 4 introduces the surrogate control variable and the control architecture. Experimental closed-loop results are presented in Sec. 5 and in Sec. 6 a conclusion is drawn. e. optimisation based ILC  and H∞ –optimal RC , are revisited in this section. Whereas a time-domain description will be given for ILC, frequency-domain approaches will be better suited for RC. 1 Iterative Learning Control System Description. Consider a linear, time-invariant, single-input singleoutput system in its discrete-time state space representation x(k + 1) = Ax(k) + b u(k) , x(0) = x0 (1) y(k) = cT x(k) + d(k) , (2) where u(k) ∈ IR is the input, x(k) ∈ IRnx the state, y(k) ∈ IR the output, and d(k) ∈ IR the disturbance at time step k = t/Ts ∈ IN0 .
Top: First (left) and second (right) PC describing the impact of the running disturbance generator on the cp -distribution of the suction surface of the middle blade. 85. |W (z)|, |Wp,m (z)| |Gn (z)|, |Gp,m (z)| Comparison of Iterative Learning and Repetitive Control 100 10−1 10−2 −2 10 |Gn (z)| |Gp,m (z)| 10−1 100 f [Hz] 101 102 49 100 10−1 10−2 −2 10 |W (z)| |Wp,m (z)| 10−1 100 f [Hz] 101 102 Fig. 8. Left: Gain of the frequency responses of the nominal plant model Gn (z) and the perturbed plant models Gp,m (z).