TAILIEUCHUNG - Lecture Signals, systems & inference – Lecture 10: Observers, state feedback

Lecture Signals, systems & inference – Lecture 10: Observers, state feedback. The following will be discussed in this chapter: System (“plant”), a good model, observer configuration, observer performance (with measurement noise), observer for ship heading error, state feedback. | Lecture Signals, systems & inference – Lecture 10: Observers, state feedback Observers, state feedback , Spring 2018 Lec 10 1 Observers 2 System (“plant”) w[n] x[n] q[n] y[n] + A, b, cT, d 1[n] 3 A good model w[n] x[n] b[n q [n] yb[n] y[n] + A, b, cT, d 1[n] 4 Observer configuration w[n] x[n] q[n] y[n] + A, b, cT Plant Z[n] y[n] q[n] q[n] y[n] - + T A, b, c Observer B 5 Observer performance (with measurement noise) 3 Actual 2 q1 Estimate q1 1 0 Pendulum angle (q1) -1 -2 -3 -4 -5 -6 Time 6 (s) Observer for ship heading error Desired heading Actual q1[n] heading x[n] Rudder angle q1 [n + 1] 1 q1 [n] ⇢ q[n + 1] = = + x[n] q2 [n + 1] 0 ↵ q2 [n] = Aq[n] + bx[n] . 7 State feedback x[n] p[n] + A, b, cT q[n] gT 8 MIT OpenCourseWare Signals, Systems and Inference Spring 2018 For information about citing these materials or our Terms of Use, visit: . 9

• Kĩ thuật Viễn thông
• Signals systems
• Lecture Signals systems
• Time invariant system models
• System models in continuous
• Linear system models
• State–space models
• State feedback
• 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
• Observer based feedback
• Probabilistic models
• Random variables
• Correlation coefficient
• LTI filtering
• WSS processes
• Power Spectral Density
• Wiener filtering
• Wiener filtering illustrations
• Hypothesis testing