### Course Meeting Times

Lectures: 2 sessions / week, 1.5 hours / session

### Objectives

- Nonlinear optimization – MATLAB implementation
- Optimization approaches: dynamic programming, Calculus of Variations
- Linear quadratic and
*H*compensators – stochastic and deterministic_{∞} - Investigate key basic control concepts and extend to advanced algorithms (MPC)
- Will focus on both the technique/approach and the control result

### Approximate Number of Lectures per Topic

#### Keywords

LQR = linear-quadratic regulator

LQG = linear-quadratic Gaussian

MPC = model predictive control

NUMBER OF LECTURES | TOPICS |
---|---|

2 | Nonlinear optimization |

3 | Dynamic programming |

2 | Calculus of variations – general |

3 | Calculus of variations – control |

5 | LQR/LQG - stochastic optimization |

3 |
H and robust control
_{∞} |

2 | On-line optimization and control (MPC) |

### Grades

ACTIVITIES | PERCENTAGES |
---|---|

Homework: problem sets every other Thursday due 2 weeks later (usually) at 11 am | 20% |

Two midterms: both are in class, and you are allowed 1 sheet of notes (both sides) for the first, 2 sheets for the second | 25% each |

Final exam | 30% |

### Prerequisites

- Course assumes a good working knowledge of linear algebra and differential equations. New material will be covered in depth in the class, but a strong background will be necessary.
- Solid background in control design is best to fully understand this material, but not essential.
- Course material and homework assume a good working knowledge of MATLAB.

### Policies

- You are encouraged to discuss the homework and problem sets.
**However, your submitted work must be your own.** - Late homework will not be accepted unless prior approval is obtained from Professor How. Grade on all late homework will be reduced 25% per day. No homework will be accepted for credit after the solutions have been handed out.