Control theory : multivariable and nonlinear methods

By T. Glad, Lennart Ljung. 2000

394 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/edf431d46b5f71210cf8de1b00d8a88388aeda69

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Citations in the corpus

Design of sensor networks for instantaneous inversion of modally reduced order models in structural dynamics (2015, unknown): “A proof can be found in [24].”
MIMO interaction measure and controller structure selection (2004, methodology): “This framework is similar to that proposed elsewhere (e.g. in Glad and Ljung 2000), and avoids the ambiguity of models described in physical or engineering units.”
Spatiotemporal Learning via Infinite-Dimensional Bayesian Filtering and Smoothing: A Look at Gaussian Process Regression Through Kalman Filtering (2013, methodology): “4) Use the methods from control theory [21] to convert the transfer function model into an equivalent state space model.”
Generalized KYP lemma: unified frequency domain inequalities with design applications (2005, background): “Sensitivity-shaping: This is a typical control design with specifications on the closed-loop transfer functions [26], [27].”
An algebraic framework for linear identification (2003, methodology): “Simulation results The heat exchanger data provided in [14] was used for simulation purposes a = 0.”
A hybrid genetic algorithm and bacterial foraging approach for global optimization (2007, methodology): “The proposed algorithm is validated using four test functions and for PID controller tuning [18,29,17] in Section 3.”
Speed control of electrical drives using classical control methods (2011, background): “This is an exact linearization of the system [39].”
Kalman filtering and smoothing solutions to temporal Gaussian process regression models (2010, unknown): “…of f on input t∗ is also Gaussian p(f(t∗)|y, θ) = N(µGP(t∗), σ2GP(t∗)), with mean and variance µGP(t∗) = k∗,f (K + σ 2 noiseI) −1y σ2GP(t∗) = k(t∗, t∗) − k∗,f (K + σ2noiseI)−1kT∗,f , where the element i of row vector k∗,f is the prior covariance between f(t∗) and f(ti) as [k∗,f ]i = k(t∗, ti, θ).”