The major themes of this course are estimation and control of dynamic systems. Preliminary topics begin with reviews of probability and random variables. Next, classical and state-space descriptions of random processes and their propagation through linear systems are introduced, followed by frequency domain design of filters and compensators. And Kalman filter is employed to estimate the states of dynamic systems will be discussed.
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer need to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
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Course Credit: MIT
|Expectation, Averages and Characteristic Function||00:15:00|
|Correlation, Covariance, and Orthogonality||00:15:00|
|Some Common Distributions||00:15:00|
|More Common Distributions||00:10:00|
|Linearized Error Propagation||00:15:00|
|More Linearized Error Propagation||00:10:00|
|Concept of a Random Process||00:15:00|
|Power Spectral Density Function||00:15:00|
|Determination of Autocorrelation||00:15:00|
|Introduction: The Analysis Problem||00:15:00|
|Pure White Noise and Bandlimited Systems||00:10:00|
|Nonstationary (Transient) Analysis – Initial Condition Response||00:15:00|
|The Wiener Filter Problem||00:10:00|
|State Space Description||00:15:00|
|Monte Carlo Simulation of Discrete-Time Systems||00:10:00|
|Transition from the Discrete to Continuous Filter Equations||00:10:00|
|Submit Your Assignment||00:00:00|
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