Nonlinear Industrial Control Systems presents a range of mostly optimisation-based methods for severely nonlinear systems; it discusses feedforward and feedback control and tracking control systems design. The plant models and design algorithms are provided in a MATLAB (R) toolbox that enable both academic examples and industrial application studies to be repeated and evaluated, taking into account practical application and implementation problems. The text makes nonlinear control theory accessible to readers having only a background in linear systems, and concentrates on real applications of nonlinear control. It covers: different ways of modelling nonlinear systems including state space, polynomial-based, linear parameter varying, state-dependent and hybrid; design techniques for nonlinear optimal control including generalised-minimum-variance, model predictive control, quadratic-Gaussian, factorised and H design methods; design philosophies that are suitable for aerospace, automotive, marine, process-control, energy systems, robotics, servo systems and manufacturing; steps in design procedures that are illustrated in design studies to define cost-functions and cope with problems such as disturbance rejection, uncertainties and integral wind-up; and baseline non-optimal control techniques such as nonlinear Smith predictors, feedback linearization, sliding mode control and nonlinear PID. Nonlinear Industrial Control Systems is valuable to engineers in industry dealing with actual nonlinear systems. It provides students with a comprehensive range of techniques and examples for solving real nonlinear control design problems.

Many large-scale processes like refineries or power generation plant are constructed using the multi-vendor system and a main co-ordinating engineering contractor. With such a methodology. the key process units are installed complete with local proprietary control systems in place. Re-assessing the so called lower level control loop design or structure is becoming less feasible or desirable. Consequently, future comp~titive gains in large-scale industrial systems will arise from the closer and optimised global integration of the process sub-units. This is one of the inherent commercial themes which motivated the research reported in this monograph. To access the efficiency and feasibility of different large-scale system designs, the traditional tool has been the global steady-state analysis and energy balance. The process industries have many such tools encapsu lated as proprietary design software. However, to obtain a vital and critical insight into global process operation a dynamic model and simulation is necessary. Over the last decade, the whole state of the art in system simulation has irrevocably changed. The Graphical User Interface (G UI) and icon based simulation approach is now standard with hardware platforms becoming more and more powerful. This immediately opens the way to some new and advanced large-scale dynamic simulation developments. For example, click-together blocks from standard or specialised libraries of process units are perfectly feasible now.

This monograph was motivated by a very successful workshop held before the 3rd IEEE Conference on Decision and Control held at the Buena Vista Hotel, lake Buena Vista, Florida, USA. The workshop was held to provide an overview of polynomial system methods in LQG (or H ) and Hoo optimal control and 2 estimation. The speakers at the workshop were chosen to reflect the important contributions polynomial techniques have made to systems theory and also to show the potential benefits which should arise in real applications. An introduction to H2 control theory for continuous-time systems is included in chapter 1. Three different approaches are considered covering state-space model descriptions, Wiener-Hopf transfer function methods and finally polyno mial equation based transfer function solutions. The differences and similarities between the techniques are explored and the different assumptions employed in the solutions are discussed. The standard control system description is intro duced in this chapter and the use of Hardy spaces for optimization. Both control and estimation problems are considered in the context of the standard system description. The tutorial chapter concludes with a number of fully worked ex amples."