Predictive Learning Control For Unknown Nonaffine Nonlinear Systems

This book investigates both theory and various applications of predictive learning control (PLC) which is an advanced technology for complex nonlinear systems. To avoid the difficult modeling problem for complex nonlinear systems, this book begins with the design and theoretical analysis of PLC method without using mechanism model information of the system, and then a series of PLC methods is designed that can cope with system constraints, varying trial lengths, unknown time delay, and available and unavailable system states sequentially. Applications of the PLC on both railway and urban road transportation systems are also studied. The book is intended for researchers, engineers, and graduate students who are interested in predictive control, learning control, intelligent transportation systems and related fields.

This book belongs to the subject of control and systems theory. It studies a novel data-driven framework for the design and analysis of iterative learning control (ILC) for nonlinear discrete-time systems. A series of iterative dynamic linearization methods is discussed firstly to build a linear data mapping with respect of the system’s output and input between two consecutive iterations. On this basis, this work presents a series of data-driven ILC (DDILC) approaches with rigorous analysis. After that, this work also conducts significant extensions to the cases with incomplete data information, specified point tracking, higher order law, system constraint, nonrepetitive uncertainty, and event-triggered strategy to facilitate the real applications. The readers can learn the recent progress on DDILC for complex systems in practical applications. This book is intended for academic scholars, engineers, and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.

This book intends to report new optimal control results with critic intelligence for complex discrete-time systems, which covers the novel control theory, advanced control methods, and typical applications for wastewater treatment systems. Therein, combining with artificial intelligence techniques, such as neural networks and reinforcement learning, the novel intelligent critic control theory as well as a series of advanced optimal regulation and trajectory tracking strategies are established for discrete-time nonlinear systems, followed by application verifications to complex wastewater treatment processes. Consequently, developing such kind of critic intelligence approaches is of great significance for nonlinear optimization and wastewater recycling. The book is likely to be of interest to researchers and practitioners as well as graduate students in automation, computer science, and process industry who wish to learn core principles, methods, algorithms, and applications in the field of intelligent optimal control. It is beneficial to promote the development of intelligent optimal control approaches and the construction of high-level intelligent systems.

This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets. In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN). The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science. The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS) The book chapters present various techniques of deep learning for robotic applications. The book chapters contain a good literature survey with a long list of references. The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques. The book chapters are lucidly illustrated with numerical examples and simulations. The book chapters discuss details of applications and future research areas.

This book studies selected discrete-time flight control schemes for fixed-wing unmanned aerial vehicle (UAV) systems in the presence of system uncertainties, external disturbances and input saturation. The main contributions of this book for UAV systems are as follows: (i) the proposed integer-order discrete-time control schemes are based on the designed discrete-time disturbance observers (DTDOs) and the neural network (NN); and (ii) the fractional-order discrete-time control schemes are developed by using the fractional-order calculus theory, the NN and the DTDOs. The book offers readers a good understanding of how to establish discrete-time tracking control schemes for fixed-wing UAV systems subject to system uncertainties, external wind disturbances and input saturation. It represents a valuable reference guide for academic research on uncertain UAV systems, and can also support advanced / Ph.D. studies on control theory and engineering.

This book presents the proceedings of the Third International Conference on Electrical Engineering and Control (ICEECA2017). It covers new control system models and troubleshooting tips, and also addresses complex system requirements, such as increased speed, precision and remote capabilities, bridging the gap between the complex, math-heavy controls theory taught in formal courses, and the efficient implementation required in real-world industry settings. Further, it considers both the engineering aspects of signal processing and the practical issues in the broad field of information transmission and novel technologies for communication networks and modern antenna design. This book is intended for researchers, engineers, and advanced postgraduate students in control and electrical engineering, computer science, signal processing, as well as mechanical and chemical engineering.

Parameter Estimation and Adaptive Control for Nonlinear Servo Systems presents the latest advances in observer-based control design, focusing on adaptive control for nonlinear systems such as adaptive neural network control, adaptive parameter estimation, and system identification. This book offers an array of new real-world applications in the field. Written by eminent scientists in the field of control theory, this book covers the latest advances in observer-based control design. It provides fundamentals, algorithms, and it discusses key applications in the fields of power systems, robotics and mechatronics, flight and automotive systems. Presents a clear and concise introduction to the latest advances in parameter estimation and adaptive control with several concise applications for servo systems Covers a wide range of applications usually not found in similar books, such as power systems, robotics, mechatronics, aeronautics, and industrial systems Contains worked examples which make it ideal for advanced courses as well as for researchers starting to work in the field, particularly suitable for engineers wishing to enter the field quickly and efficiently

The two-volume Proceedings set CCIS 1637 and 1638 constitutes the refereed proceedings of the Third International Conference on Neural Computing for Advanced Applications, NCAA 2022, held in Jinan, China, during July 8–10, 2022. The 77 papers included in these proceedings were carefully reviewed and selected from 205 submissions. These papers were categorized into 10 technical tracks, i.e., neural network theory, and cognitive sciences, machine learning, data mining, data security & privacy protection, and data-driven applications, computational intelligence, nature-inspired optimizers, and their engineering applications, cloud/edge/fog computing, the Internet of Things/Vehicles (IoT/IoV), and their system optimization, control systems, network synchronization, system integration, and industrial artificial intelligence, fuzzy logic, neuro-fuzzy systems, decision making, and their applications in management sciences, computer vision, image processing, and their industrial applications, natural language processing, machine translation, knowledge graphs, and their applications, Neural computing-based fault diagnosis, fault forecasting, prognostic management, and system modeling, and Spreading dynamics, forecasting, and other intelligent techniques against coronavirus disease (COVID-19).