Control of interconnected systems with sensor delay based on decentralized adaptive neural dynamic surface method

2020 ◽  
pp. 095965182096652
Author(s):  
Barmak Beigzadehnoe ◽  
Zahra Rahmani ◽  
Alireza Khosravi ◽  
Behrooz Rezaie
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Chemical Reactor ◽  
Interconnected Systems ◽  
Linear Matrix ◽  
Dynamic Surface ◽  
Control Laws ◽  
Surface Design ◽  
Pendulum System ◽  
Inverted Pendulum System

In this article, an adaptive neural network is proposed for the tracking control problem of unknown nonlinear interconnected systems with inaccessible states and sensor delays based on dynamic surface strategy. The system has unknown nonlinearities and immeasurable states. Thus, a neural network state observer based on delayed outputs of subsystems is applied. The main difficulty in obtaining local observers’ gains is that undelayed outputs are not available. As a result, by utilizing proper Lyapunov–Krasovskii functionals in dynamic surface design procedures, the gains of local observers are given in terms of linear matrix inequalities. Then, appropriate changes in coordinates are defined using delayed outputs, observed states, and filtered virtual controls for the purpose of designing dynamic surface controllers. Subsequently, proper Lyapunov–Krasovskii functionals are introduced to deal with sensor delays and obtain control laws and stability criteria. Furthermore, the proposed decentralized control scheme can suitably conquer the decentralized tracking problem of unknown large-scale systems with sensor delays and guarantee that all the signals in the closed-loop interconnected systems be uniformly ultimately bounded. Finally, to show the effectiveness and efficiency of the proposed approach, the theoretic achievements are employed to design a controller for a double-inverted pendulum system and a cascade chemical reactor system.

scholarly journals Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

2011 ◽  
Vol 8 (3) ◽  
pp. 307-323 ◽  
Author(s):  
Mohamed Bahita ◽  
Khaled Belarbi
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Feedback Linearization ◽  
Pendulum System ◽  
Rbf Network ◽  
Inverted Pendulum System ◽  
Neural Network Controller ◽  
Control Gain ◽  
On Line ◽  
Neural Feedback

In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

Adaptive Neural Network Fuzzy Control Plane Double Inverted Pendulum

2013 ◽  
Vol 765-767 ◽  
pp. 2004-2007
Author(s):  
Su Ying Zhang ◽  
Ying Wang ◽  
Jie Liu ◽  
Xiao Xue Zhao
Keyword(s):  
Neural Network ◽  
Fuzzy Control ◽  
Control Theory ◽  
Inverted Pendulum ◽  
Learning Ability ◽  
Adaptive Neural Network ◽  
Pendulum System ◽  
Inverted Pendulum System ◽  
Self Learning ◽  
Controlled Object

Double inverted pendulum system is nonlinear and unstable. Fuzzy control uses some expert's experience knowledge and learns approximate reasoning algorithm. For it does not depend on the mathematical model of controlled object, it has been widely used for years. In practical engineering applications, most systems are nonlinear time-varying parameter systems. As the fuzzy control theory lacks of on-line self-learning and adaptive ability, it can not control the controlled object effectively. In order to compensate for these defects, it introduced adaptive, self-organizing, self-learning functions of neural network algorithm. We called it adaptive neural network fuzzy inference system (ANFIS). ANFIS not only takes advantage of the fuzzy control theory of abstract ability, the nonlinear processing ability, but also makes use of the autonomous learning ability of neural network, the arbitrary function approximation ability. The controller was applied to double inverted pendulum system and the simulation results showed that this method can effectively control the double inverted pendulum system.

scholarly journals Controller Design of Multiinput Multioutput Time-Delay Large-Scale System

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Chia-Wei Lin ◽  
Tzuu-Hseng S. Li ◽  
Chung-Cheng Chen
Keyword(s):  
Time Delay ◽  
Feedback Linearization ◽  
Large Scale ◽  
Stability Characteristic ◽  
Test Bed ◽  
Pendulum System ◽  
Large Scale Systems ◽  
Almost Disturbance Decoupling ◽  
Inverted Pendulum System ◽  
Disturbance Decoupling

The paper presents a novel feedback linearization controller of nonlinear multiinput multioutput time-delay large-scale systems to obtain both the tracking and almost disturbance decoupling (ADD) performances. The significant contribution of this paper is to build up a control law such that the overall closed-loop system is stable for given initial condition and bounded tracking trajectory with the input-to-state-stability characteristic and almost disturbance decoupling performance. We have simulated the two-inverted-pendulum system coupled by a spring for networked control systems which has been used as a test bed for the study of decentralized control of large-scale systems.

scholarly journals Proportional PDC Design-Based Robust Stabilization and Tracking Control Strategies for Uncertain and Disturbed T-S Model

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Chekib Ghorbel ◽  
Amira Tiga ◽  
Naceur Benhadj Braiek
Keyword(s):  
Tracking Control ◽  
Robust Stabilization ◽  
Fuzzy Model ◽  
Nonlinear Dynamic System ◽  
Disturbance Attenuation ◽  
Linear Matrix ◽  
Feedback Gain ◽  
Pendulum System ◽  
Inverted Pendulum System ◽  
S Model

This paper presents a proportional parallel distributed compensation (PPDC) design to the robust stabilization and tracking control of the nonlinear dynamic system, which is described by the uncertain and perturbed Takagi–Sugeno (T-S) fuzzy model. The proposed PPDC control design can greatly reduce the number of adjustable parameters involved in the original PDC and separate them from the feedback gain. Furthermore, the process of finding the common quadratic Lyapunov matrix is simplified. Then, the global asymptotic stability with decay rate and disturbance attenuation of the closed-loop T-S model affected by uncertainties and external disturbances are discussed using the H∞ synthesis and linear matrix inequality (LMI) tools. Finally, to illustrate the merit of our purpose, numerical simulation studies of stabilizing and tracking an inverted pendulum system are presented.

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