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Published in Philip A. Laplante, Comprehensive Dictionary of Electrical Engineering, 2018
state space conditional codec an approach where the number of codes is much less than with conditional coding. The previous N - 1 pixels are used to determine the state s j . Then the jth variable word-length is used to code the value. state space model a set of differential and algebraic equations defining the dynamic behavior of systems. Its generic form for linear continuoustime systems is given by d x(t) = Ax(t) + Bu(t) dt y(t) = C x(t) + Du(t) where u(t) is the system input signal, x(t) is its internal or state space variable, and y(t) is its output. Matrices A, B, C, D of real constants define the model. The internal variable is often a vector of internal variables, while in the general multivariable case all the input and output signals are also vectors of signals. Although not identically equivalent, the state space model can be related to the transfer function (or transfer function matrix)
Feedback Control of Fluidised Bed Drying
Published in Alex Martynenko, Andreas Bück, Intelligent Control in Drying, 2018
Andreas Bück, Robert Dürr, Nicole Vorhauer
Using this approach, the controller transfer function matrix C is diagonal, that is control error channel one only influences manipulated variable one and control error channel two influences only manipulated variable two. In the current case with two manipulated variables and two controlled variables, two possible pairings of inputs to outputs exists. A thorough analysis of suitable pairings can be performed using relative gain analysis (RGA), as presented in many textbooks on MIMO feedback control (Skogestad and Postlethwaite 2005). For illustration, the pairings inlet mass flow rate and solid moisture content and inlet gas temperature and solid temperature are chosen.
Vibration Control
Published in Haym Benaroya, Mark Nagurka, Seon Han, Mechanical Vibration, 2017
Haym Benaroya, Mark Nagurka, Seon Han
It is possible to determine the transfer function matrix from the state-space representation, as we did previously. (The procedure from Section 10.7.1 applies for MIMO systems.) Taking the Laplace transform of both sides of Equation 10.37 and assuming zero initial conditions gives
Robustness evaluation and robust design for proportional-integral-plus control
Published in International Journal of Control, 2019
Emma D. Wilson, Quentin Clairon, Robin Henderson, C. James Taylor
For controlling real systems, closed-loop robustness is a key concern. Robust control design aims to ensure satisfactory performance in the presence of uncertainty, e.g. parametric and non-parametric modelling errors, external disturbances and sensor noise. The control problem, as originally formulated by Zames (1981), is concerned with minimising appropriately defined norms (Green & Limebeer, 2012; Zhou & Doyle, 1998) to maximise the robustness properties of the controller. A key research question in this regard is how to design a controller which minimises the norm of a pre-designated closed-loop transfer function matrix (Francis, 1987). Considerable research effort has been made over many years towards answering this question, e.g. Kwakernaak (1993), Zhou et al. (1996), Zhou and Doyle (1998) and Green and Limebeer (2012).