In control system design there are often a number of design objectives to be considered. The objectives are sometimes conflicting and no design exists which can be considered best with respect to all objectives. Hence, there is an inevitable trade-off between design objectives, for example, between certain performance objectives and stability robustness. These considerations have led to the study of multiobjective optimization methods for control systems. During the 1980s and 1990s multiobjective control has been considered in the design process. The control system objectives are described by a set of performance indices. This type of control problem appears in flight control design, in the control of space structures and in industrial process control. The concept of a generic multiobjective control problem which involves different types of performance indices is generating increasing research interest. This work explores how practical control systems can be designed to accommodate different and often conflicting performance objectives, such as closed-loop stability, low feedback gains and insensitivity to model parameter variations. It presents fundamental theory, a number of design methods and algorithms, and some practical applications in multiobjective optimization and control, including recent research work on this subject. Traditional approaches to control system design objectives make use of scalar summation of all weighted objectives in one cost function. In contrast, this book seeks to clarify how each objective is affected by the controller.

Table of Contents:
Nonlinear optimization; constrained optimization; multiple objective optimization; genetic algorithms and optimization; robust control system design by mixed optimization; multiobjective control of critical systems; multiobjective control using eigenstructure assignment; multiobjective PID controller implementation design; multiobjective PI controller design for a gasifier; multiobjective nonlinerar identification; multiobjective fault diagnosis.