This textbook is a good starting point to learn about optimal control for people with less engineering experience. The first asset of this book is that it shows the maths only after explaining what we are trying to do, rather than before. Maths can be a bit of a scarecrow, but when we know what a formula is trying to achieve before we even get to it, it is usually easier to appreciate what it really represents.

The second asset of this book is that, well, it still shows the maths. Many books go at lengths trying to build a maths-free intuition for the concepts discussed and then just don't bother showing the math at all, under the logic that an intuitive understanding is already conveyed. While this is a reasonable logic, it also misses on a great opportunity to link the now-gained intuitive understanding to its mathematical formalization, which is also a worthy skill to developp.

The third asset of this book is its exercice section at the end of each chapter. They are very well crafted, reasonably progressive in difficulty, and overall a great mean of playing with newly acquired concepts.

Chapter 4 provides a well-laid explanation of Kalman filtering. It starts with a simple least-square extimator, then adds propagation over time of state estimate uncertainty to yield the Kalman filter. This is then followed by an extension to the continuous-time case (Kalman-Bucy filter) and non-linear case (extended Kalman-Bucy filter, Quasilinear Filter).