#### Topic |
#### Percentage of Course |

1. Introduction and Review |
10% |

2. Linear Quadratic Regulator (LQR) |
25% |

a. Derivation from Dynamic Programming Theory |
% |

b. Derivation from Least Squares Theory |
% |

c. Penalty Matrix Selection |
% |

d. Application to Disturbance Rejection and Tracking |
% |

3. Robustness |
15% |

a. Singular Values and the Multivariable Nyquist Test |
% |

b. Gain and Phase Margin Properties of LQR |
% |

c. General Uncertainty Bounds |
% |

4. Kalman Filtering |
10% |

a. Stochastic Dynamical Systems |
% |

b. Derivation as Linear, Minimum-Variance Estimator |
% |

c. Properties |
% |

5. Linear-Quadratic-Gaussian Control |
25% |

a. Stochastic Dynamic Programming |
% |

b. Derivation of LQR with Additive or Multiplicative Noise |
% |

c. Separation Principle |
% |

d. Loss of Robustness and Loop-Transfer Recovery |
% |

e. Approaches to Robustness with Structured Uncertainty |
% |

6. Advanced Topics |
15% |