Adaptive fuzzy control: experiments and comparative analyses

Abstract

Abstract-Advances in nonlinear control theory have provided the mathematical foundations necessary to establish conditions for stability of several types of adaptive fuzzy controllers. However, very few, if any, of these techniques have been compared to conventional adaptive or nonadaptive nonlinear controllers or tested beyond simulation; therefore, many of them remain as purely theoretical developments whose practical value is difficult to ascertain. In this paper we will develop three case studies where we perform a comparative analysis between the adaptive fuzzy techniques in [1]-[3] and some conventional adaptive and nonadaptive nonlinear control techniques. In each case, the analysis will be performed both in simulation and in implementation, in order to show practical examples of how the performance of these controllers compares to conventional controllers in real systems.

Adaptive fuzzy sliding mode controller for a class of SISO nonlinear time-delay systems

“…However, these methods use neural networks or fuzzy logic systems to parameterize the unknown nonlinearities. In the literature, two mains approaches of adaptive control have been expanded (Narendra and Annaswamy 1989): the first is recognized as indirect adaptive control (IAC) (Ordóñez et al 1997 Chtourou 2006), whereas the second is known as direct adaptive control (DAC) (Ordóñez et al 1997;Abid et al 2012). All of the previously mentioned studies do not cover the state time delays, which often exist in various engineering systems such as communication networks, biological reactors, manufacturing systems and chemical processes.…”

On the sliding mode control of a Ball on a Beam system

“…For example, the application of neural networks for the real time control of a Ball on a Beam system can be found in [20][21][22][23]. While the application of fuzzy logic for realtime control of a Ball on a Beam system can be found in [24][25][26][27]. Recently, conventional and fuzzy PD controllers, which asymptotically stabilize the Ball on a Beam system using the complete model of the system, were proposed in [28] and [29].…”

PFC Fuzzy Decision-Making Control and Its Application to Car-Like Mobile Vehicle

“…Fuzzy set is a means of representing and manipulating data that was not precise, but rather fuzzy. Fuzzy logic guarantees a theoretic foundation for combining multi-targets with fuzzy set to form a target set with membership values reflecting satisfaction degree (how good or bad) of its elements (Ordonez & Zumberge, 1997). The target set is defined as "soft target" in this chapter (Chen & Yasunobu, 2007b).…”