TAILIEUCHUNG - Lecture Signals, systems & inference – Lecture 6: Modal solution of undriven CT LTI state-space models

The following will be discussed in this chapter: Glucose-insulin system, UVA/Padova model (FDA approved!), linearization at an equilibrium yields an LTI model, phase plane trajectories, complex eigenvalue pairs (CT case). | Lecture Signals, systems & inference – Lecture 6: Modal solution of undriven CT LTI state-space models Modal solution of undriven CT LTI state-space models , Spring 2018 Lec 6 1 Glucose-insulin system From Messori et al., IEEE Control Systems Magazine © IEEE. All rights reserved. This content is excluded from our 2 Creative Commons license. Feb 2018 For more information, see UVA/Padova model (FDA approved!) From Messori et al., IEEE Control Systems Magazine © IEEE. All rights reserved. This content is excluded from our Creative Commons license. For more information, see 3 Feb 2018 Linearization at an equilibrium yields an LTI model e , x(t) = x¯ + x(t) ¯ + q(t) CT case: q(t) = q e , q˙ (t) = f (q(t), x(t)) # h @f i h @f i e˙ q(t) ⇡ e + q(t) e x(t) @q ¯ ,x q ¯ @x ¯ ,x q ¯ e for small perturbations q(t) e and x(t) from equilibrium 4 Phase plane trajectories State trajectories for different initial conditions 5 4 [-6, ] 3 2 1 [, ] q2(t) 0 [2, ] -1 -2 [4, ] -3 -4 [8, -4] -5 5 -10 -8 -6 -4 -2 0 2 4 6 8 10 q1(t) Complex eigenvalue pairs (CT case) If Ai is a (complex) eigenvalue with eigenvector vi , then its complex conjugate A⇤i is also an eigenvalue, with associated eigenvector vi⇤ . Write i = i + j!i , vi = ui + jvi . Then the contribution of the complex pair to the modal solution is ⇤ ↵ i vi e it + ↵i⇤ vi⇤ e it = h i it Ki e ui cos(!i t + ✓i ) wi sin(!i t + ✓i ) 6 Acoustics and Vibration Animations Have fun exploring the animations created by Prof. Dan Russell, Penn State 7 MIT OpenCourseWare Signals, Systems and Inference Spring 2018 For information about citing these materials or our Terms of Use, visit: . 8

• Kĩ thuật Viễn thông
• Signals systems
• Lecture Signals systems
• Time invariant system models
• System models in continuous
• Linear system models
• State–space models
• Lecture ELEC2400 signals and systems
• ELEC2400 signals and systems
• Signal types and operations
• Operations on Signals
• Types of Signals
• Signals and systems using MATLAB
• Systems using MATLAB
• From the ground up
• Continuous time signals
• Continuous time systems
• The laplace transform
• Frequency analysis
• Frequency domain analysis
• Fourier transform
• Frequency response of LTI systems
• Energy spectral density
• State Space Models
• Matrix exponential
• Transfer function
• Observers for state estimation
• State feedback
• Observer based feedback
• Probabilistic models
• Random variables
• Correlation coefficient
• LTI filtering
• WSS processes
• Power Spectral Density
• Wiener filtering
• Wiener filtering illustrations
• Hypothesis testing