Supervisory control based on closed-loop adaptive control approach of nonlinear systems: application to CSTR process

2011 ◽  
Vol 14 (1) ◽  
pp. 258-270 ◽  
Author(s):  
H. Lehouche ◽  
H. Guéguen ◽  
B. Mendil
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
Supervisory Control ◽  
Closed Loop ◽  
Control Approach

scholarly journals Adaptive Stabilization Control for a Class of Complex Nonlinear Systems Based on T-S Fuzzy Bilinear Model

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Jinsheng Xing ◽  
Naizheng Shi
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
Complex Systems ◽  
Closed Loop ◽  
Loop System ◽  
Asymptotically Stable ◽  
Bilinear Model ◽  
Closed Loop System ◽  
Fuzzy Modelling ◽  
Control Scheme

This paper proposes a stable adaptive fuzzy control scheme for a class of nonlinear systems with multiple inputs. The multiple inputs T-S fuzzy bilinear model is established to represent the unknown complex systems. A parallel distributed compensation (PDC) method is utilized to design the fuzzy controller without considering the error due to fuzzy modelling and the sufficient conditions of the closed-loop system stability with respect to decay rateαare derived by linear matrix inequalities (LMIs). Then the errors caused by fuzzy modelling are considered and the method of adaptive control is used to reduce the effect of the modelling errors, and dynamic performance of the closed-loop system is improved. By Lyapunov stability criterion, the resulting closed-loop system is proved to be asymptotically stable. The main contribution is to deal with the differences between the T-S fuzzy bilinear model and the real system; a global asymptotically stable adaptive control scheme is presented for real complex systems. Finally, illustrative examples are provided to demonstrate the effectiveness of the results proposed in this paper.

Event-Triggered Adaptive Control

2015 ◽  
Author(s):  
Ali Albattat ◽  
Benjamin C. Gruenwald ◽  
Tansel Yucelen
Keyword(s):  
Adaptive Control ◽  
Lyapunov Stability ◽  
Data Exchange ◽  
Closed Loop ◽  
Closed Loop System ◽  
Control Approach ◽  
Event Triggering ◽  
Control Methodology ◽  
Tools And Methods ◽  
Event Triggered

In this paper, we present a new adaptive control methodology that allows a desirable command performance while the proposed controller exchanges data with the physical system through a real-time network. Specifically, we utilize tools and methods from event-triggering control theory to schedule data exchange dependent upon errors exceeding user-defined thresholds and show the boundedness of the overall closed-loop system using Lyapunov stability. An illustrative numerical example is provided to demonstrate the efficacy of the proposed adaptive control approach.

Impacts of soil-structure interaction on the structural control of nonlinear systems using adaptive control approach

2018 ◽  
Vol 157 ◽  
pp. 1-13 ◽  
Author(s):  
Fereidoun Amini ◽  
Maryam Bitaraf ◽  
Mohammad Seddiq Eskandari Nasab ◽  
Mohammad Mahdi Javidan
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
Structural Control ◽  
Soil Structure ◽  
Soil Structure Interaction ◽  
Control Approach ◽  
Structure Interaction

Multivariable fuzzy adaptive control approach of nonlinear systems 1

1999 ◽  
Vol 32 (2) ◽  
pp. 2494-2499
Author(s):  
Zhang Huaguang ◽  
Cai Lilong ◽  
Zeungnam Bien
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
Fuzzy Adaptive Control ◽  
Control Approach ◽  
Fuzzy Adaptive

A semi-adaptive control approach to closed-loop medication infusion

2016 ◽  
Vol 31 (2) ◽  
pp. 240-254 ◽  
Author(s):  
Xin Jin ◽  
Chang-Sei Kim ◽  
Guy A. Dumont ◽  
J. Mark Ansermino ◽  
Jin-Oh Hahn
Keyword(s):  
Adaptive Control ◽  
Closed Loop ◽  
Control Approach

A Novel Higher-Order Model-Free Adaptive Control for a Class of Discrete-Time SISO Nonlinear Systems

2013 ◽  
Vol 135 (4) ◽  
Author(s):  
Shangtai Jin ◽  
Zhongsheng Hou ◽  
Ronghu Chi
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
Discrete Time ◽  
Higher Order ◽  
Control Input ◽  
Order Model ◽  
Control Approach ◽  
Model Free ◽  
Input And Output ◽  
Control Scheme

In this work, a novel higher-order model-free adaptive control scheme is presented based on a dynamic linearization approach for a class of discrete-time single input and single output (SISO) nonlinear systems. The control scheme consists of an adaptive control law, a parameter estimation law, and a reset mechanism. The design and analysis of the proposed control approach depends merely on the measured input and output data of the controlled plant. The control performance is improved by using more information of control input and output error measured from previous sampling time instants. Rigorous mathematical analysis is developed to show the bounded input and bounded output (BIBO) stability of the closed-loop system. Two simulation comparisons show the effectiveness of the proposed control scheme.

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