Industrial Predictive

Model Predictive Control System Design and Implementation Using MATLAB® proposes methods for design and implementation of MPC systems using basis functions that confer the following advantages: - continuous- and discrete-time MPC problems solved in similar design frameworks; - a parsimonious parametric representation of the control trajectory gives rise to computationally efficient algorithms and better on-line performance; and - a more general discrete-time representation of MPC design that becomes identical to the traditional approach for an appropriate choice of parameters.

The new edition of the prime reference on the tools of statistics used in industry and services, integrating theoretical, practical, and computer-based approaches

This book focuses on the distributed control and estimation of large-scale networked distributed systems and the approach of distributed model predictive and moving horizon estimation. Both principles and engineering practice have been addressed, with more weight placed on engineering practice. This is achieved by providing an in-depth study on several major topics such as the state estimation and control design for the networked system with considering time-delay, data-drop, etc., Distributed MPC design for improving the performance of the overall networked system, which includes several classic strategies for different scenarios, details of the application of the distributed model predictive control to smart grid system and distributed water network. The comprehensive and systematic treatment of theoretical and practical issues in distributed MPC for networked systems is one of the major features of the book, which is particularly suited for readers who are interested to learn practical solutions in distributed estimation and optimization of distributed networked systems. The book benefits researchers, engineers, and graduate students in the fields of chemical engineering, control theory and engineering, electrical and electronic engineering, chemical engineering, and computer engineering, etc.

Sebastian K. Crönert presents a new, automated process that makes it possible to obtain all the fuel properties required for combustion simulation. If necessary, these are then transferred - also automatically - into specially created correlation equations through which they are then made available again at simulation runtime. This method makes it possible to represent even more complex correlations and cross-influences on calculation variables in a resource-optimised way (memory requirements and access time) while maintaining the same accuracy. The procedure is validated using test bench measurement data for the pure fuels anisole and cyclopentanone and their blends with regular petrol (RON95E10). Additional validations include more established synthetic fuels and hydrogen. It is shown that an extraordinarily high prediction quality can be achieved for the model class.

The advances in industrial edge artificial intelligence (AI) are transforming the way industrial equipment and machine interact with the real world, with other machines and humans during manufacturing processes. These advances allow industrial internet of things (IIoT) and edge devices to make decisions during the manufacturing processes using sensors and actuators data.

This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key theoretical and practical considerations including recursive feasibility, closed-loop stability, closed-loop performance, and computational efficiency. Specifically, the book proposes:

In today's competitive global environment, manufacturers are offered with unprecedented opportunities to build hyper-efficient and highly flexible plants, towards meeting variable market demand, while at the same time supporting new production models such as make-to-order (MTO), configure-to-order (CTO) and engineer-to-order (ETO). During the last couple of years, the digital transformation of industrial processes is propelled by the emergence and rise of the fourth industrial revolution (Industry 4.0).