English | 2023 | ISBN: 0367750082 | 363 pages | True PDF | 11.3 MB

"One of the main problems in science and eeering is to provide a quantitative description of the systems under investigation, leveraging collected noisy data. Such a description may be a complete mathematical model or a mechanism to return controllers corresponding to new, unseen inputs. Recent advances in the theories are described in detail, along with their applications in eeering. The book aims to develop model-free system analysis and control strats, i.e., data-driven control from theoretical analysis and eeering applications based only on measured data. The study aims to develop system identification, and combination in advanced control theory, i.e., data-driven control strategy as system and controller are generated from measured data directly. The book covers the development of system identification and its combination in advanced control theory, i.e., data-driven control strategy, as they all depend on measured data. Firstly, data-driven identification is developed for the closed-loop, nonlinear system and model validation, i.e., obtaining model descriptions from measured data. Secondly, the data-driven idea is combined with some control strats to be considered data-driven control strats, such as data-driven model predictive control, data-driven iterative tuning control, and data-driven subspace predictive control. Thirdly data-driven identification and data-driven control strats are applied to interested eeering. In this context, the book provides algorithms to perform state estimation of dynamical systems from noisy data and some convex optimization algorithms through identification and control problems"