scholarly journals Pole Location and Input Constrained Robust Fuzzy Control for T-S Fuzzy Models Subject to Passivity and Variance Requirements

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 787
Author(s):  
Hong-Yu Qiao ◽  
Wen-Jer Chang ◽  
Yann-Horng Lin ◽  
Yu-Wei Lin
Keyword(s):  
Nonlinear Systems ◽  
Control Theory ◽  
Passive Control ◽  
Control Method ◽  
Fuzzy Controller ◽  
Sufficient Conditions ◽  
Pole Placement ◽  
Stochastic Nonlinear Systems ◽  
Generator System ◽  
Performance Constraints

In this paper, a robust fuzzy controller for stochastic nonlinear systems subject to multiple performance constraints is discussed. To solve the problem of stochastic behaviors in nonlinear systems, the covariance control theory and passive control theory are applied based on the concept of energy. Additionally, the pole placement method is considered for the better transient behaviors of the system responses. However, it is known that the control inputs and maximum overshoot of the system responses may become bigger at the same time when the settling time or converge rate of the system is required to be faster. Due to this reason, the input constraint is also considered in the fuzzy controller design method to limit the value of the control gain. Moreover, an effective robust control method is applied to deal with the perturbation of the nonlinear systems. Based on the above performance constraints, the sufficient conditions can be obtained to achieve the stability in the sense of mean square and the multi-performance requirements. Finally, simulation results of the nonlinear synchronous generator system are presented to verify the feasibility and efficiency of the proposed control method.

scholarly journals The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Xiaoyan Qin
Keyword(s):  
Nonlinear Systems ◽  
Neural Control ◽  
Closed Loop ◽  
Control Method ◽  
High Order ◽  
Stochastic Nonlinear Systems ◽  
Closed Loop System ◽  
Adaptive Neural Control ◽  
Control Scheme ◽  
Approximation Capability

This paper studies the problem of the adaptive neural control for a class of high-order uncertain stochastic nonlinear systems. By using some techniques such as the backstepping recursive technique, Young’s inequality, and approximation capability, a novel adaptive neural control scheme is constructed. The proposed control method can guarantee that the signals of the closed-loop system are bounded in probability, and only one parameter needs to be updated online. One example is given to show the effectiveness of the proposed control method.

scholarly journals Resilient Finite-Time Controller Design of a Class of Stochastic Nonlinear Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Zhiguo Yan
Keyword(s):  
Nonlinear Systems ◽  
Finite Time ◽  
State Feedback ◽  
Controller Design ◽  
Sufficient Conditions ◽  
Stochastic Nonlinear Systems ◽  
Matrix Inequalities ◽  
Annular Domain ◽  
Stabilizing Controller ◽  
Time Control

This paper deals with the problem of resilient finite-time control for a class of stochastic nonlinear systems. The notion of finite-time annular domain stability of stochastic nonlinear systems is first introduced. Then, some sufficient conditions are given for the existence of resilient state feedback finite-time annular domain stabilizing controller, which are expressed in terms of matrix inequalities. A double-parameter searching algorithm is proposed to solve these obtained matrix inequalities. Finally, an example is given to illustrate the effectiveness of the developed method.

scholarly journals Fault-Tolerant Control of Doubly-Fed Wind Turbine Generation Systems under Sensor Fault Conditions

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3239 ◽  
Author(s):  
Guodong You ◽  
Tao Xu ◽  
Honglin Su ◽  
Xiaoxin Hou ◽  
Xue Wang ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Control Method ◽  
Fuzzy Controller ◽  
Fault Tolerant ◽  
Sufficient Conditions ◽  
Fuzzy Model ◽  
Fault Tolerant Control ◽  
Fault Detection And Isolation ◽  
Fuzzy Observer ◽  
Doubly Fed

This paper studies the fault-tolerant control problem of uncertain doubly-fed wind turbine generation systems with sensor faults. Considering the uncertainty of the system, a fault-tolerant control strategy based on a T-S fuzzy observer is proposed. The fuzzy observer is established based on the T-S fuzzy model of the uncertain nonlinear system. According to the comparison and analysis of residual between the state estimation of the fuzzy observer output and the measured value of the real sensor, a fault detection and isolation (FDI) based on T-S fuzzy observer is designed. Then by using a Parallel Distributed Compensation (PDC) method we design the robust fuzzy controller. Finally, the necessary and sufficient conditions for the stability of the closed-loop system are proved by quoting Lyapunov stability theory. The simulation results verify the effectiveness of the proposed control method.

scholarly journals A novel adaptive control design method for stochastic nonlinear systems using neural network

2021 ◽  
Author(s):  
Mohammad Mahdi Aghajary ◽  
Arash Gharehbaghi
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Nonlinear Systems ◽  
Stochastic Systems ◽  
Control Method ◽  
Dead Zone ◽  
Stochastic Nonlinear Systems ◽  
Adaptation Period ◽  
Dynamic Surface ◽  
Actuator Nonlinearity

AbstractThis paper presents a novel method for designing an adaptive control system using radial basis function neural network. The method is capable of dealing with nonlinear stochastic systems in strict-feedback form with any unknown dynamics. The proposed neural network allows the method not only to approximate any unknown dynamic of stochastic nonlinear systems, but also to compensate actuator nonlinearity. By employing dynamic surface control method, a common problem that intrinsically exists in the back-stepping design, called “explosion of complexity”, is resolved. The proposed method is applied to the control systems comprising various types of the actuator nonlinearities such as Prandtl–Ishlinskii (PI) hysteresis, and dead-zone nonlinearity. The performance of the proposed method is compared to two different baseline methods: a direct form of backstepping method, and an adaptation of the proposed method, named APIC-DSC, in which the neural network is not contributed in compensating the actuator nonlinearity. It is observed that the proposed method improves the failure-free tracking performance in terms of the Integrated Mean Square Error (IMSE) by 25%/11% as compared to the backstepping/APIC-DSC method. This depression in IMSE is further improved by 76%/38% and 32%/49%, when it comes with the actuator nonlinearity of PI hysteresis and dead-zone, respectively. The proposed method also demands shorter adaptation period compared with the baseline methods.

scholarly journals Variance and Passivity Constrained Fuzzy Control for Nonlinear Ship Steering Systems with State Multiplicative Noises

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Wen-Jer Chang ◽  
Bo-Jyun Huang
Keyword(s):  
Fuzzy Control ◽  
Control Method ◽  
Fuzzy Controller ◽  
Sufficient Conditions ◽  
Fuzzy Model ◽  
Lyapunov Theory ◽  
Linear Matrix ◽  
Multiplicative Noises ◽  
Ship Steering ◽  
The Matrix

The variance and passivity constrained fuzzy control problem for the nonlinear ship steering systems with state multiplicative noises is investigated. The continuous-time Takagi-Sugeno fuzzy model is used to represent the nonlinear ship steering systems with state multiplicative noises. In order to simultaneously achieve variance, passivity, and stability performances, some sufficient conditions are derived based on the Lyapunov theory. Employing the matrix transformation technique, these sufficient conditions can be expressed in terms of linear matrix inequalities. By solving the corresponding linear matrix inequality conditions, a parallel distributed compensation based fuzzy controller can be obtained to guarantee the stability of the closed-loop nonlinear ship steering systems subject to variance and passivity performance constraints. Finally, a numerical simulation example is provided to illustrate the usefulness and applicability of the proposed multiple performance constrained fuzzy control method.

Semi-Active Control on Stay Cable Vibration Based on Fuzzy Logical Theory

2010 ◽  
Vol 163-167 ◽  
pp. 2815-2818
Author(s):  
Zhi Rong Xiao
Keyword(s):  
Active Control ◽  
Passive Control ◽  
Control Method ◽  
Fuzzy Controller ◽  
Simulation Analysis ◽  
Mr Damper ◽  
Stay Cable ◽  
Logical Theory ◽  
Better Than ◽  
Fuzzy Logical

A new method of semi-active control on cable-MR system based on fuzzy logical theory in this article is presented. In the method, the voltage to drive MR damper is determined by fuzzy controller, which avoids the difficulty of calculating the voltage from the strong nonlinear MR model. In order to testify the effectiveness of the proposed method, the simulation analysis to a typical cable is made and its results are compared to the passive control (passive-on, passive-off) of MR and active control (LQR). The conclusion is that the operation of fuzzy control method is simple and practical. Its effect is better than the passive methods, but is a little worse than LQR.

scholarly journals Stability analysis and H∞ control of discrete T–S fuzzy hyperbolic systems

2016 ◽  
Vol 26 (1) ◽  
pp. 133-145 ◽  
Author(s):  
Ruirui Duan ◽  
Junmin Li ◽  
Yanni Zhang ◽  
Ying Yang ◽  
Guopei Chen
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Control Method ◽  
Hyperbolic Systems ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Hyperbolic Model ◽  
External Disturbances ◽  
Diagonally Dominant ◽  
Feasible Solutions

Abstract This paper focuses on the problem of constraint control for a class of discrete-time nonlinear systems. Firstly, a new discrete T–S fuzzy hyperbolic model is proposed to represent a class of discrete-time nonlinear systems. By means of the parallel distributed compensation (PDC) method, a novel asymptotic stabilizing control law with the “soft” constraint property is designed. The main advantage is that the proposed control method may achieve a small control amplitude. Secondly, for an uncertain discrete T–S fuzzy hyperbolic system with external disturbances, by the proposed control method, the robust stability and H∞ performance are developed by using a Lyapunov function, and some sufficient conditions are established through seeking feasible solutions of some linear matrix inequalities (LMIs) to obtain several positive diagonally dominant (PDD) matrices. Finally, the validity and feasibility of the proposed schemes are demonstrated by a numerical example and a Van de Vusse one, and some comparisons of the discrete T–S fuzzy hyperbolic model with the discrete T–S fuzzy linear one are also given to illustrate the advantage of our approach.

scholarly journals Stabilization Using a Discrete Fuzzy PDC Control with PID Controllers and Pole Placement: Application to an Experimental Greenhouse

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Amine Chouchaine ◽  
Elyes Feki ◽  
Abdelkader Mami
Keyword(s):  
Nonlinear Systems ◽  
Control Strategy ◽  
Stability Problem ◽  
Discrete System ◽  
Fuzzy Controller ◽  
Ventilation Rate ◽  
Pole Placement ◽  
Pid Controllers ◽  
Robust Nonlinear Control ◽  
Takagi Sugeno

This paper proposes a control strategy for complex and nonlinear systems, based on a parallel distributed compensation (PDC) controller. A solution is presented to solve a stability problem that arises when dealing with a Takagi-Sugeno discrete system with great numbers of rules. The PDC controller will use a classical controller like a PI, PID, or RST in each rule with a pole placement strategy to avoid causing instability. The fuzzy controller presented combines the multicontrol approach and the performance of the classical controllers to obtain a robust nonlinear control action that can also deal with time-variant systems. The presented method was applied to a small greenhouse to control its inside temperature by variation in ventilation rate inside the process. The results obtained will show the efficiency of the adopted method to control the nonlinear and complex systems.

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