Control and Estimation matlab files

These files accompany the text Control and Estimation with MATLAB, 5th Edition

The text is composed of eight chapters. The 1st chapter has to do with state estimation and data smoothing. The chapter includes Luenberger observers, alpha-beta-gamma filters, Kalman filters, extended Kalman filters, proportional-integral Kalman filters, H∞ filters, unscented Kalman filters, sliding mode observers, Inertial Measurement Unit estimation, data fusion ideas, and zero phase filters. It is given at the beginning of the text as it is a necessary interface between control algorithms and sensors. Chapter 2 describes several approaches to data smoothing. Data smoothing is performed by using algorithms to remove random variation or noise from data taken over time. This allows important patterns to stand out. Chapter 3 describes RLS and Kalman filter state estimation approaches to fault detection and includes an example. Chapter 4 has to do with control system design to mitigate the effects of disturbances, including disturbance accommodating control, H∞, Active Disturbance Rejection Control and harmonic oscillation control. Chapter 5 has a few adaptive control methods that are described including model reference adaptive control, L1 Adaptive Control, and model free adaptive control. Chapter 6 describes ways to tune proportional integral derivative (PID) control algorithms. This is the most commonly used and, therefore, most important control algorithm. Chapter 7 describes several adaptive and non-adaptive feedforward control techniques. Chapter 8 has a few applications that may be of interest to the reader. It shows a few of the techniques explained in the text by using control system and estimation methods