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A review of continuous-time and discrete-time control systems.
State-space analysis is stressed, and Matlab control-system design
procedures are reviewed (sections 1–3). |
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“Optimal control” (LQR) of systems when complete, perfect,
measurements are available (section 4). |
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A review of random processes (section 5). |
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A development of least-squares (optimal) estimation theory for
estimating a static state, given noisy measurements (section 6). |
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An application of least-squares estimation in system identification
(section 7). |
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A development of least-squares estimation theory for estimating the
state of a dynamic system, leading to development of the Kalman
filter (sections 8 and 9). |
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Application of the Kalman filter to control theory, developing
optimal control (LQR and LQG) of systems with incomplete, noisy
measurements (section 10). |
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An introduction to robust control theory, explaining why LQG is
not the ultimate control-system design, and why the control designer
needs to take care when using it (section 11).
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I typeset the lecture notes. |