This book aims to develop systematic design methodologies to model-based nonlinear control of aeroengines, focusing on (1) modelling of aeroengine systems—both component-level and identification-based models will be extensively studied and compared; and (2) advanced nonlinear control designs—set-point control, transient control and limit-protection control approaches will all be investigated. The model-based design has been one of the pivotal technologies to advanced control and health management of propulsion systems. It can fulfil advanced designs such as fault-tolerant control, engine modes control and direct thrust control. As a consequence, model-based design has become an important research area in the field of aeroengines due to its theoretical interests and engineering significance. One of the central issues in model-based controls is the tackling of nonlinearities. There are publications concerning with either nonlinear modelling or nonlinear controls; yet, they are scattered throughout the literature. It is time to provide a comprehensive summary of model-based nonlinear controls. Consequently, a series of important results are obtained and a systematic design methodology is developed which provides consistently enhanced performance over a large flight/operational envelope, and it is thus expected to provide useful guidance to practical engineering in aeroengine industry and research.

The objective of this research was to develop tools for modeling analysis, and control design for unsteady flow and combustion phenomena critical to operation of military aeroengines. We have made significant progress in control-oriented modeling model validation and control design for processes affecting operation of aeroengines. The progress in modeling includes control-oriented reduced order models for turbomachinery flow including compressors, diffusers, recirculation zones, and jets in cross flow. Major breakthrough was achieved in the area of nonlinear model validation and parameter identification, with particular focus on models of combustion instability. In particular, method of nonlinear model validation and parameter identification for systems with non-equilibrium attractors and noise drive systems based on comparison of long-term statistics has been transitioned to internal UTRC projects. New results in control of limit-cycling systems with bounded control were obtained. Significant progress was achieved in adaptive control of combustion instability, control of pressure recovery in diffusers, control of recirculation zone, and control of jets in cross flow. In particular, framework for control of mixing based on hierarchy of vorticity evolution models has been introduced. The framework has been used in control of mixing enhancement in jets in cross-flow and led to experimental validation of model-based analysis.

The research completed has established connections between systematic methods of nonlinear stabilization and nonlinear optimal control. The redesigns of adaptive and robust nonlinear controllers developed under this grant are inverse optimal, and thus use less control effort and possess a certain margin of robustness to some uncertainties. This grant has also produced the first methods for stabilization for stochastic nonlinear systems. The most significant among the results is disturbance attenuation and adaptive stabilization for systems with noise of unknown covariance. Finally, the grant has revived a whole research area called extremum seeking control which deals with non-model-based on-line optimization. The PI and the group obtained the first stability guarantees for extremum seeking schemes and proposed techniques for their application to much more general classes of systems than previously possible. The ultimate goal of the theoretical research was an application to nonlinear instabilities arising in aeroengines. The PI has advanced the state of the art in this area in three directions. First he developed the first nonlinear control laws with guaranteed regions of attraction and robustness to some modeling errors. Second, he pioneered the methods for seeking of the maximum of the compressor pressure rise characteristic. Third, he designed the first model-based adaptive controllers for thermoacoustic instabilities in combustion chambers. The results of the work have been published (or submitted for publication) in 1 book, 22 journal papers, a number of conference papers, and four book chapters.

This book introduces a comprehensive and mathematically rigorous controller design for families of nonlinear systems with time-varying parameters and unstructured uncertainties. Although the presented methodology is general, the specific family of systems considered is the latest, NextGen, unconventional fixed-wing unmanned aircraft with circulation control or morphing wings, or a combination of both. The approach considers various sources of model and parameter uncertainty, while the controller design depends not on a nominal plant model, but instead on a family of admissible plants. In contrast to existing controller designs that consider multiple models and multiple controllers, the proposed approach is based on the ‘one controller fits all models’ within the unstructured uncertainty interval. The book presents a modeling-based analysis and synthesis approach with additive uncertainty weighting functions for accurate realization of the candidate systems. This differs significantly from existing designs in that it is capable of handling time-varying characteristics. This research monograph is suitable for scientists, engineers, researchers and graduate students with a background in control system theory who are interested in complex engineering nonlinear systems.

This book presents the proceedings of the 17th Chinese Intelligent Systems Conference, held in Fuzhou, China, on Oct 16-17, 2021. It focuses on new theoretical results and techniques in the field of intelligent systems and control. This is achieved by providing in-depth study on a number of major topics such as Multi-Agent Systems, Complex Networks, Intelligent Robots, Complex System Theory and Swarm Behavior, Event-Triggered Control and Data-Driven Control, Robust and Adaptive Control, Big Data and Brain Science, Process Control, Intelligent Sensor and Detection Technology, Deep learning and Learning Control Guidance, Navigation and Control of Flight Vehicles and so on. The book is particularly suited for readers who are interested in learning intelligent system and control and artificial intelligence. The book can benefit researchers, engineers, and graduate students.

This five year project has brought together researchers in nonlinear control theory (University of California and Caltech) and aeroengine dynamics and control (MIT) to develop new techniques for robust nonlinear Control of aeroengines. Technology transfer to the commercial sector has been achieved through strong and synergistic coupling with Pratt & : Whitney (P & :W) via United Technologies Research Center (UTRC). The specific application areas addressed were active control of. first, compression system rotating stall and surge, and second, blade flutter and forced vibration. These complex physical phenomena, which bear heavily on engine safety and performance, are arguably the most critical considerations in modern aeroengine design. The outcomes of this project are new tools and methods promising to improve both operability and reliability, of military and commercial aeroengines through robust nonlinear control. A highly integrated multi-disciplinary effort has successfully interlaced ideas and results from fluid dynamics, structural dynamics, experiments, and nonlinear control theory. For the highly nonlinear behavior associated with rotating stall and large disturbances new analytical, numerical, and experimental nonlinear techniques have been developed, transitioned to the industrial partner and widely disseminated to the engineering community. The results have appeared in over 40 journal papers, 50 conference proceedings as well as in 2 patents 1.2. Among the important spin-offs of this project are the new AFOSR flow control and mixing projects by M. Krsitc, I. Mezic, and B. Bamieh. They are now better prepared to broaden the scope of control theory as a vital enabling technology.

Gas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks provides new approaches and novel solutions to the modeling, simulation, and control of gas turbines (GTs) using artificial neural networks (ANNs). After delivering a brief introduction to GT performance and classification, the book:Outlines important criteria to consi