Fuzzy Passivity Non-Fragile Control of Flexible Joint Robot

2013 ◽  
Vol 448-453 ◽  
pp. 3571-3575
Author(s):  
Bin Zhang
Keyword(s):  
Robot Control ◽  
Simulation Experiment ◽  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Flexible Joint ◽  
Control Approach ◽  
Compensation Principle ◽  
The Stability

The paper proposes a fuzzy passivity non-fragile control approach for flexible joint robot. The T-S fuzzy model is applied to approximate the flexible joint robot at first, and then the fuzzy controller is developed based on parallel distributed compensation principle. The passivity non-fragile performance of controller is also employed to limit the influence of model error. The conditions for the stability of the flexible joint robot control system are proposed by using Lyapunov function, and linear matrix inequality is applied to resolve the controller parameter. The simulation experiment results show the effectiveness of the proposed method.

H∞ Control of Flexible Joint Robot via T-S Fuzzy Model

2011 ◽  
Vol 128-129 ◽  
pp. 894-897
Author(s):  
Feng Wang ◽  
Xiao Ping Liu
Keyword(s):  
Robot Control ◽  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Stability Conditions ◽  
Modeling Error ◽  
Flexible Joint ◽  
Control Approach ◽  
Compensation Principle ◽  
Simulation Results ◽  
The Stability

In this paper, a H∞ control approach based on T-S fuzzy model for flexible joint robot is proposed. First, the Takagi and Sugeno (T-S) fuzzy model is applied to approximate the flexible joint robot. Then, a fuzzy controller is developed based on parallel distributed compensation principle (PDC), and H∞ performance is employed to restrain the influence of modeling error caused by variety of stiffness. The stability conditions for the flexible joint robot control system are proposed by Lyapunov function. Finally, the simulation results are given to show the effectiveness of the proposed method.

Fuzzy Passivity Control of Flexible Joint Robot

2013 ◽  
Vol 834-836 ◽  
pp. 1229-1233
Author(s):  
Bin Zhang
Keyword(s):  
Control Method ◽  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Model Error ◽  
Stability Control ◽  
Linear Matrix ◽  
Matrix Inequalities ◽  
Flexible Joint ◽  
Single Link ◽  
The Stability

This paper researches a fuzzy control method for passive flexible joint robot. Stability control method applying T-S fuzzy model is employed for single-link flexible joint robot. T-S fuzzy model is used to approximate the flexible joint robot firstly, and then fuzzy controller is developed based on the principle of parallel distributed compensation. The fuzzy controller is also applied to the passive properties of model error. The stability conditions are proposed by Lyapunov function and linear matrix inequalities are also applied to solve the controller parameters. Simulation results show that the proposed method of application value.

Reliable Robust H∞ Fuzzy Control for Uncertain Nonlinear Systems With Markovian Jumping Actuator Faults

2006 ◽  
Vol 129 (3) ◽  
pp. 252-261 ◽  
Author(s):  
Huai-Ning Wu
Keyword(s):  
Fuzzy Control ◽  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Disturbance Attenuation ◽  
Linear Matrix ◽  
Actuator Faults ◽  
Markovian Jumping ◽  
Stochastically Stable ◽  
The Stability ◽  
Time Systems

This paper is concerned with the design of reliable robust H∞ fuzzy control for uncertain nonlinear continuous-time systems with Markovian jumping actuator faults. The Takagi and Sugeno fuzzy model is employed to represent an uncertain nonlinear system with Markovian jumping actuator faults. First, based on the parallel distributed compensation (PDC) scheme, a sufficient condition such that the closed-loop fuzzy system is robustly stochastically stable and satisfies a prescribed level of H∞-disturbance attenuation is derived. In the derivation process, a stochastic Lyapunov function is used to test the stability and H∞ performance of the system. Then, a new improved linear matrix inequality (LMI) formulation is applied to this condition to alleviate the interrelation between the stochastic Lyapunov matrix and system matrices containing controller variables, which results in a tractable LMI-based condition for the existence of reliable and robust H∞ fuzzy controllers. A suboptimal fuzzy controller is proposed to minimize the level of disturbance attenuation subject to the LMI constraints. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.

scholarly journals Improved T-S Fuzzy Control for Uncertain Time-Delay Coronary Artery System

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Zhenyu Zhu ◽  
Zhanshan Zhao ◽  
Haoliang Cui ◽  
Fengdong Shi
Keyword(s):  
Coronary Artery ◽  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Practical Significance ◽  
Linear Matrix ◽  
System State ◽  
Fuzzy Models ◽  
The Stability ◽  
Uncertain Time ◽  
Takagi Sugeno

This paper is based on the Takagi-Sugeno (T-S) fuzzy models to construct a coronary artery system (CAS) T-S fuzzy controller and considers the uncertainties of system state parameters in CAS. We propose the fuzzy model of CAS with uncertainties. By using T-S fuzzy model of CAS and the use of parallel distributed compensation (PDC) concept, the same fuzzy set is assigned to T-S fuzzy controller. Based on this, a PDC controller whose fuzzy rules correspond to the fuzzy model is designed. By constructing a suitable Lyapunov-Krasovskii function (LKF), the stability conditions of the linear matrix inequality (LMI) are exported. Simulation results show that the method proposed in this paper is correct and effective and has certain practical significance.

scholarly journals Control Strategies for the Grid Side Converter in a Wind Generation System Based on a Fuzzy Approach

2018 ◽  
Vol 28 (2) ◽  
pp. 323-333 ◽  
Author(s):  
Naziha Harrabi ◽  
Maher Kharrat ◽  
Abdel Aitouche ◽  
Mansour Souissi
Keyword(s):  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Control Strategies ◽  
Fuzzy Model ◽  
Synchronous Generator ◽  
Linear Matrix ◽  
Fuzzy Approach ◽  
The Stability ◽  
Grid Side ◽  
Grid Side Converter

Abstract Two techniques for the control of a grid side converter in a wind energy conversion system. The system is composed of a fixed pitch angle wind turbine followed by a permanent magnet synchronous generator and power electronic converters AC-DC-AC. The main interest is in how to control the inverter in order to ensure the stability of the DC link voltage. Two control methods based on the fuzzy approach are applied and compared. First, a direct Mamdani fuzzy logic controller is presented. Then, a T-S fuzzy controller is elaborated based on a T-S fuzzy model. The Lyapunov theorem and H-infinity performance are exploited for stability analysis. Besides, the feedback controller gains are determined using linear matrix inequality tools. Simulation results are derived in order to prove the robustness of the proposed control algorithms and to compare their performances.

scholarly journals Actuator Saturated Fuzzy Controller Design for Interval Type-2 Takagi-Sugeno Fuzzy Models with Multiplicative Noises

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Wen-Jer Chang ◽  
Yu-Wei Lin ◽  
Yann-Horng Lin ◽  
Chin-Lin Pen ◽  
Ming-Hsuan Tsai
Keyword(s):  
Nonlinear Systems ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Actuator Saturation ◽  
Design Method ◽  
Fuzzy Model ◽  
The Stability ◽  
Interval Type ◽  
Takagi Sugeno

In many practical systems, stochastic behaviors usually occur and need to be considered in the controller design. To ensure the system performance under the effect of stochastic behaviors, the controller may become bigger even beyond the capacity of practical applications. Therefore, the actuator saturation problem also must be considered in the controller design. The type-2 Takagi-Sugeno (T-S) fuzzy model can describe the parameter uncertainties more completely than the type-1 T-S fuzzy model for a class of nonlinear systems. A fuzzy controller design method is proposed in this paper based on the Interval Type-2 (IT2) T-S fuzzy model for stochastic nonlinear systems subject to actuator saturation. The stability analysis and some corresponding sufficient conditions for the IT2 T-S fuzzy model are developed using Lyapunov theory. Via transferring the stability and control problem into Linear Matrix Inequality (LMI) problem, the proposed fuzzy control problem can be solved by the convex optimization algorithm. Finally, a nonlinear ship steering system is considered in the simulations to verify the feasibility and efficiency of the proposed fuzzy controller design method.

Exponential stability and periodicity of memristor-based recurrent neural networks with time-varying delays

2017 ◽  
Vol 10 (02) ◽  
pp. 1750027 ◽  
Author(s):  
Wei Zhang ◽  
Chuandong Li ◽  
Tingwen Huang
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Sufficient Conditions ◽  
Functional Approach ◽  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequality ◽  
Matrix Inequalities ◽  
Inclusion Theory ◽  
The Stability

In this paper, the stability and periodicity of memristor-based neural networks with time-varying delays are studied. Based on linear matrix inequalities, differential inclusion theory and by constructing proper Lyapunov functional approach and using linear matrix inequality, some sufficient conditions are obtained for the global exponential stability and periodic solutions of memristor-based neural networks. Finally, two illustrative examples are given to demonstrate the results.

scholarly journals Fuzzy Stabilization for Nonlinear Discrete Ship Steering Stochastic Systems Subject to State Variance and Passivity Constraints

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Wen-Jer Chang ◽  
Bo-Jyun Huang ◽  
Po-Hsun Chen
Keyword(s):  
Linear Matrix Inequality ◽  
Discrete Time ◽  
Stochastic Systems ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Sufficient Conditions ◽  
Control Technique ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Ship Steering

For nonlinear discrete-time stochastic systems, a fuzzy controller design methodology is developed in this paper subject to state variance constraint and passivity constraint. According to fuzzy model based control technique, the nonlinear discrete-time stochastic systems considered in this paper are represented by the discrete-time Takagi-Sugeno fuzzy models with multiplicative noise. Employing Lyapunov stability theory, upper bound covariance control theory, and passivity theory, some sufficient conditions are derived to find parallel distributed compensation based fuzzy controllers. In order to solve these sufficient conditions, an iterative linear matrix inequality algorithm is applied based on the linear matrix inequality technique. Finally, the fuzzy stabilization problem for nonlinear discrete ship steering stochastic systems is investigated in the numerical example to illustrate the feasibility and validity of proposed fuzzy controller design method.

scholarly journals Further results on robust fuzzy dynamic systems with LMI D-stability constraints

2014 ◽  
Vol 24 (4) ◽  
pp. 785-794 ◽  
Author(s):  
Wudhichai Assawinchaichote
Keyword(s):  
Dynamic Systems ◽  
Fuzzy Controller ◽  
Sufficient Conditions ◽  
Fuzzy Model ◽  
Local System ◽  
Linear Matrix ◽  
Fuzzy Modelling ◽  
Ts Fuzzy Model ◽  
Input Noise ◽  
Takagi Sugeno

Abstract This paper examines the problem of designing a robust H∞ fuzzy controller with D-stability constraints for a class of nonlinear dynamic systems which is described by a Takagi-Sugeno (TS) fuzzy model. Fuzzy modelling is a multi-model approach in which simple sub-models are combined to determine the global behavior of the system. Based on a linear matrix inequality (LMI) approach, we develop a robust H∞ fuzzy controller that guarantees (i) the L2-gain of the mapping from the exogenous input noise to the regulated output to be less than some prescribed value, and (ii) the closed-loop poles of each local system to be within a specified stability region. Sufficient conditions for the controller are given in terms of LMIs. Finally, to show the effectiveness of the designed approach, an example is provided to illustrate the use of the proposed methodology.

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