Python and Scilab for Control Systems Engineering is a comprehensive practical guide for students, engineers, researchers and teachers who want to understand control systems and implement them using open-source computational tools.

Beginning with the fundamentals of control systems, the book progressively develops modelling, simulation and analysis techniques using Python and Scilab. It covers transfer functions, state-space representations, time-domain analysis, frequency-domain analysis, stability, root locus, controllability, observability, controller design, digital control and practical implementation.

The Python sections introduce the scientific computing ecosystem, including NumPy, SciPy, Matplotlib, SymPy, pandas, scikit-learn and the python-control library. The Scilab sections develop programming, numerical computation, the Control System Toolbox and Xcos graphical modelling and simulation.

The book follows a complete engineering workflow: define the problem, develop the mathematical model, implement the model, simulate the system, evaluate performance, design the controller, validate the result and consider deployment and maintenance.

Practical examples, programming exercises, mini-projects and research problems are included throughout. Detailed solutions provide additional support for independent learning and classroom use.

Applications extend to robotics, process control, aerospace, automotive systems, power systems, manufacturing, medical devices, digital twins, machine learning and cyber-physical systems.

The book is particularly useful for undergraduate and postgraduate students of electrical engineering, electronics, instrumentation, mechanical engineering, mechatronics, robotics, aerospace engineering and related disciplines.

It is also suitable for practicing engineers who want to explore open-source alternatives to proprietary control-engineering software.