Practical Control Engineering for Mechatronics and Automation

Production processes and engineered systems use continuous and discrete variables, as well as the combination of continuous and sequential operations. This volume covers both aspects, thus providing knowledge in continuous and discrete control, logic control, and hybrid control systems. It is a compilation of selected control strategies to automate processes and systems with a practical approach to ease their design, analysis and implementation. The selection of the control schemes is based on the capability to provide desired dynamical response or real time performance. Practicality is required for achieving faster development times of automation projects or system prototypes by comprehensive presentation and direct application of methodologies and techniques for efficient and structured programming of control algorithms. Considered methodologies include model-based design, hardware in the loop simulations and structured programming. Fundamental signals and systems concepts are explained. Systems and controllers are analyzed using discrete-time equations, which ease their implementation in most programmable platforms without requiring sophisticated software. PID based control, internal model control and model reference control are viewed as powerful schemes in terms of performance and suitability for mechatronics systems because of the use of the model in their architecture as a key control element. Finite state machines are presented to solve sequential requirements of direct and supervisory control of many processes and machines. Cyberphysical systems are an industrial technology and an education trend, distinguished by visual and dynamic models or digital twins of the physical systems. The discussed analysis, design and implementation practices are integrated and applied in the context of cyberphysical systems. This book aims to provide multidisciplinary support to engineers and practitioners in the design of control systems, and is a valuable tool for automation teaching and self-learning.

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