Control

Manual flight control system design for fighter aircraft is one of the most demanding problems in automatic control. Fighter aircraft dynamics generally have highly coupled uncertain and nonlinear dynamics. Multivariable control design techniques offer a solution to this problem. Robust Multivariable Flight Control provides the background, theory and examples for full envelope manual flight control system design. It gives a versatile framework for the application of advanced multivariable control theory to aircraft control problems. Two design case studies are presented for the manual flight control of lateral/directional axes of the VISTA-F-16 test vehicle and an F-18 trust vectoring system. They demonstrate the interplay between theory and the physical features of the systems.

Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented. Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented.

NetSupport Manager continues to deliver the very latest in remote access, PC support and desktop management capabilities. From a desktop, laptop, tablet or smartphone, monitor multiple systems in a single action, deliver hands-on remote support, collaborate and even record or play back sessions. When needed, gather real-time hardware and software inventory, monitor services and even view system config remotely to help resolve issues quickly.

This book focuses on pneumatic servo systems analysis, control and application in robotic systems. The pneumatic servo systems are composed by pneumatic artificial muscles or cylinders, which are two important pneumatic actuators in industrial application. The active disturbance rejection control technique is used effectively to solve strong nonlinearity and uncertain factors for the pneumatic servo systems.

Recent years have seen a rapid development of neural network control tech­ niques and their successful applications. Numerous simulation studies and actual industrial implementations show that artificial neural network is a good candidate for function approximation and control system design in solving the control problems of complex nonlinear systems in the presence of different kinds of uncertainties. Many control approaches/methods, reporting inventions and control applications within the fields of adaptive control, neural control and fuzzy systems, have been published in various books, journals and conference proceedings. In spite of these remarkable advances in neural control field, due to the complexity of nonlinear systems, the present research on adaptive neural control is still focused on the development of fundamental methodologies. From a theoretical viewpoint, there is, in general, lack of a firmly mathematical basis in stability, robustness, and performance analysis of neural network adaptive control systems.

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.