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By Gang Tao

Perceiving a necessity for a scientific and unified realizing of adaptive keep watch over concept, electric engineer Tao provides and analyzes universal layout methods with the purpose of masking the basics and state-of-the-art of the sector. Chapters hide platforms idea, adaptive parameter estimation, adaptive country suggestions regulate, continuous-time version reference adaptive regulate, discrete-time version reference adaptive regulate, oblique adaptive keep an eye on, multivariable adaptive keep an eye on, and adaptive keep an eye on of structures with nonlinearities.

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7 Local control configuration for the reactor–separator process Fig. 8 A networked control configuration for the reactor–separator process. In this configuration, a networked control system is designed to replace the three local control loops in the local control configuration tration measurements of each component in the three vessels. These measurements are subject to sampling delays and network transmission data package dropouts and they may not be available at every sampling time. To use the additional information, we may design an NCS to replace the three local control loops.

To develop distributed predictive control methods for large-scale nonlinear process networks taking into account asynchronous measurements and time-varying delays as well as different sampling rates of measurements. 4. To illustrate the applications of the developed networked and distributed predictive control methods to nonlinear process networks and wind–solar energy generation systems. The book is organized as follows. In Chap. 2, we first review some basic results on Lyapunov-based control, model predictive control and Lyapunov-based model predictive control (LMPC) of nonlinear systems and then present two Lyapunovbased model predictive control designs for systems subject to data losses and timevarying measurement delays.

31 is satisfied, then V˙ (x(t)) ˆ ≤ −εs /Δ. Integrating this bound on t ∈ [tk , tk+1 ) we obtain that the inequality of Eq. 33 holds. Using Eq. 33 recursively, it is proved that, if x(t0 ) ∈ Ωρ /Ωρs , the state converges to Ωρs in a finite number of sampling times without leaving the stability region. Once the state converges to Ωρs ⊆ Ωρmin , it remains inside Ωρmin for all times. This statement holds because of the definition of ρmin in Eq. 32. 1 ensures that if the nominal system under the control u = h(x) implemented in a sample-and-hold fashion with state feedback every sampling time 24 2 Lyapunov-Based Model Predictive Control starts in the region Ωρ , then it is ultimately bounded in Ωρmin .

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