A Survey of Decentralized Adaptive Control

Systems with multi inputs and multi outputs are in common controlled by centralized controllers, multivariable controllers or by a set of single input and single output controllers. The decentralized systems dominated in industry due to the following advantages: flexibility in operation, failure tolerance, simplified design and tuning (Garelli et al., 2006). Decentralized control techniques can be found in a broad spectrum of applications ranging from robotics to civil engineering. Approaches to decentralized control design differ from each other in the assumptions – kind of interaction, the model of the system, the model of information exchange and the control design technique (Keviczky et al., 2006). Bakule wrote the nice paper that reviews the past and present in the area of decentralized control (Bakule, 2008). The usefulness of decentralized control is provided in a very readable way. The paper includes the description of disjoint subsystems, overlapping subsystems, symmetric composite systems and decentralized networked control. One of the useful approaches to decentralized control problems was the parametrization (Date and Chow, 1993). This paper is extended by the work of Garelli et al. (Garelli et al., 2006) who focused on the limiting interactions in decentralized control of systems. During decentralized control might appear some problems at systems composed of two subsystems. A special linear star coupled dynamical network was proposed to limit the influence of some problems (Duan et al., 2007). During last years it was proven that it seems to be perspective to combine predictive and decentralized control, for example unconstrained networked decentralized model predictive control (Vaccarini et al., 2009) or fuzzy and neural networks, such as adaptive decentralized control using recurrent fuzzy neural networks (Hernandez and Tang, 2009). Another task is to use automatic decentralized control structure selection (Jørgensen and Jørgensen, 2000). Adaptive control enlarges the area of usage at decentralized controllers. Adaptive control does not limit at linear systems, it can deal for example with time delay (Shah et al., 1997). Another possibility is to use the predictive control with the combination with adaptive control (Clarke, 1996). Nice paper summarizing the results and applications at adaptive control of nonlinear systems was written by Marino (Marino, 1997). The chapter is organized in the following way. After introduction, there is a literary research dealing with decentralized control and adaptive control. Next part of the chapter describes chosen method of multi model decentralized control with the results of experiments.