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This monograph introduces the authors’ work on model predictive control system design using extended state space and extended non-minimal state space approaches. It systematically describes model predictive control design for chemical processes, including the basic control algorithms, the extension to predictive functional control, constrained control, closed-loop system analysis, model predictive control optimization-based PID control, genetic algorithm optimization-based model predictive control, and industrial applications. Providing important insights, useful methods and practical algorithms that can be used in chemical process control and optimization, it offers a valuable resource for researchers, scientists and engineers in the field of process system engineering and control engineering. .

Predictive control. --- Systems theory. --- Mathematical optimization. --- Control and Systems Theory. --- Systems Theory, Control. --- Calculus of Variations and Optimal Control; Optimization. --- Energy Efficiency. --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis

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This book is based on the authors’ research on the stabilization and fault-tolerant control of batch processes, which are flourishing topics in the field of control system engineering. It introduces iterative learning control for linear/nonlinear single/multi-phase batch processes; iterative learning optimal guaranteed cost control; delay-dependent iterative learning control; and iterative learning fault-tolerant control for linear/nonlinear single/multi-phase batch processes. Providing important insights and useful methods and practical algorithms that can potentially be applied in batch process control and optimization, it is a valuable resource for researchers, scientists, and engineers in the field of process system engineering and control engineering.

Control engineering. --- Computational intelligence. --- Electrical engineering. --- Computer communication systems. --- Calculus of variations. --- Control and Systems Theory. --- Computational Intelligence. --- Communications Engineering, Networks. --- Computer Communication Networks. --- Calculus of Variations and Optimal Control; Optimization. --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Electric engineering --- Engineering --- Control engineering --- Control equipment --- Control theory --- Engineering instruments --- Automation --- Programmable controllers --- Isoperimetrical problems --- Variations, Calculus of --- Maxima and minima --- Communication systems, Computer --- Computer communication systems --- Data networks, Computer --- ECNs (Electronic communication networks) --- Electronic communication networks --- Networks, Computer --- Teleprocessing networks --- Data transmission systems --- Digital communications --- Electronic systems --- Information networks --- Telecommunication --- Cyberinfrastructure --- Electronic data processing --- Network computers --- Distributed processing --- Intelligent control systems. --- Machine learning. --- Process control. --- Control of industrial processes --- Industrial process control --- Automatic control --- Manufacturing processes --- Quality control --- Learning, Machine --- Machine theory --- Intelligent control --- Intelligent controllers --- Automatic control. --- Computer networks.

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This book focuses on the implementation, evaluation and application of DNA/RNA-based genetic algorithms in connection with neural network modeling, fuzzy control, the Q-learning algorithm and CNN deep learning classifier. It presents several DNA/RNA-based genetic algorithms and their modifications, which are tested using benchmarks, as well as detailed information on the implementation steps and program code. In addition to single-objective optimization, here genetic algorithms are also used to solve multi-objective optimization for neural network modeling, fuzzy control, model predictive control and PID control. In closing, new topics such as Q-learning and CNN are introduced. The book offers a valuable reference guide for researchers and designers in system modeling and control, and for senior undergraduate and graduate students at colleges and universities. .

Mathematics --- Electrical engineering --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- computers --- KI (kunstmatige intelligentie) --- automatische regeltechniek --- AI (artificiële intelligentie)