Mazen Alamir – författare

While conceptually elegant, the generic formulations of nonlinear model predictive control are not ready to use for the stabilization of fast systems. Dr. Alamir presents a successful approach to this problem based on a co-operation between structural considerations and on-line optimization. The balance between structural and optimization aspects of the method is dependent on the system being considered so the many examples aim to transmit a mode of thought rather than a ready-to-use recipe; they include:- double inverted pendulum; - non-holonomic systems in chained form; - snake board; - missile in intercept mission; - polymerization reactor; - walking robot; - under-actuated satellite in failure mode. In addition, the basic stability results under receding horizon control schemes are revisited using a sampled-time, low-dimensional control parameterization that is mandatory for fast computation and some novel formulations are proposed which offer promising directions for future research.

This book provides scalable, effective and real-world-compatible methods and algorithms for the control and extended estimation of uncertain nonlinear systems against a backdrop of often-unconventional problems. The author provides advice on choosing which solution is most relevant to the desired control objectives, the nature of present uncertainties and the impact on closed-loop performance.The book introduces its key paradigms step by step and then presents the family of candidate solutions in detail along with associated python scripts. It helps the reader develop a critical and comparative point of view and thus to distinguish the best choice of solutions, some of which prove to be conventional and others to employ advanced learning-based methods. This book shows how each category applies to specific groups of problems, but the choice is made based on pragmatic assessments of efficiency and efficacy rather than on dogmatic adherence to the benefits of one or the other.All of the concepts and solutions described in the text are illustrated using significantly challenging problems, wherever possible with real-world relevance. Solutions are implemented using Python scripts, freely downloadable from the author’s GitHub account. Practical features such as messages, cautions, summaries and important comments are clearly presented to aid reading, retention and recall.Nonlinear Control of Uncertain Systems appeals to both academics and professional practitioners studying and developing nonlinear industrial control systems; its critical comparative appraisal and detailed range of solutions help readers to navigate a complex taxonomy of systems and to find the right solution—learning-based or conventional—for the problems before them.