TY - CHAP

T1 - Reconfigurable Control System for Plants with Variable Structure

AU - Fuhrhop, Carlos

N1 - Conference code: 20

PY - 2016/12/16

Y1 - 2016/12/16

N2 - Define intelligent control is not an easy task, in the literature we find different definitions of it, which depend on the area of application. Here we will define or view the intelligent control in the context of the control of complex systems. In this context, a control is intelligent when it has the ability to operate and act to control in the best way a complex system which changed suddenly and significantly, due to external and internal disturbances. This work presents the reconfigurable control method based on adaptive and conventional control combined with the 'multiples models, switching and tuning' (MMST), which can identify the model which best represent the complex system. The approach was first introduced by Narendra and Balakrishnan. Here we propose two different control strategies, one is based on conventional control using MMST and the second is based on the combination of adaptive and conventional control using MMST, to control a complex system which varies its structure and parameters. The proof of the Lyapunov stability is not part of this work, for a proof see the work of Narendra and Balakrishnan. We assume that the models of the variable plant were identified before. In the simulation we used it to represent the dynamic change of the plant. The aim of the work is to investigate if the reconfigurable control can act in an intelligent way and control in a stable manner the time variable structure system. We present some simulations results to show the efficacy of the proposed control strategies.

AB - Define intelligent control is not an easy task, in the literature we find different definitions of it, which depend on the area of application. Here we will define or view the intelligent control in the context of the control of complex systems. In this context, a control is intelligent when it has the ability to operate and act to control in the best way a complex system which changed suddenly and significantly, due to external and internal disturbances. This work presents the reconfigurable control method based on adaptive and conventional control combined with the 'multiples models, switching and tuning' (MMST), which can identify the model which best represent the complex system. The approach was first introduced by Narendra and Balakrishnan. Here we propose two different control strategies, one is based on conventional control using MMST and the second is based on the combination of adaptive and conventional control using MMST, to control a complex system which varies its structure and parameters. The proof of the Lyapunov stability is not part of this work, for a proof see the work of Narendra and Balakrishnan. We assume that the models of the variable plant were identified before. In the simulation we used it to represent the dynamic change of the plant. The aim of the work is to investigate if the reconfigurable control can act in an intelligent way and control in a stable manner the time variable structure system. We present some simulations results to show the efficacy of the proposed control strategies.

KW - Engineering

KW - Regelungstechnik

KW - Adaptive Control

KW - Control theory

KW - Adaptive Control

KW - Multiple models

KW - Reconfigurable Control

KW - switching and Tuning

UR - http://www.scopus.com/inward/record.url?scp=85010451155&partnerID=8YFLogxK

U2 - 10.1109/ICSTCC.2016.7790638

DO - 10.1109/ICSTCC.2016.7790638

M3 - Article in conference proceedings

SN - 978-1-5090-2720-0

T3 - 2016 20th International Conference on System Theory, Control and Computing, ICSTCC 2016 - Joint Conference of SINTES 20, SACCS 16, SIMSIS 20 - Proceedings

SP - 41

EP - 46

BT - 2016 20th International Conference on System Theory, Control and Computing (ICSTCC)

A2 - Petre, Emil

A2 - Brezovan, Marius

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - 20th International Conference on System Theory, Control and Computing Joint Conference SINTES 20, SACCS 16, SIMSIS 20

Y2 - 13 October 2016 through 15 October 2016

ER -