Mathematical Tools for Automation Systems II: Optimization, Estimation, Decision, and Control

This chapter is a continuation of the previous chapter, and deals with the mathematical tools and methods developed and used for system optimization, parameter and state estimation, decision making, and feedback control. Optimization of the system operation and performance is always the main goal of any design, where the performance characterization and evaluation varies from case to case. In human–automation systems the optimization problem involves (and must involve) the system’s point of view, the human’s point of view, and the nature’s point of view. Quality, productivity, energy consumption, reliability, safety, competence, human satisfaction, and impact on the environment must be considered in a holistic way for the optimum system design. The methods to be discussed here use the mathematical models presented in the previous chapter. Obviously, the more accurate the available system model is, the more pragmatic (realistic) the resulting estimators, decision rules and control algorithms are.