FUZZY PREDICTIVE CONTROLLER FOR UNKNOWN DISCRETE CHAOTIC SYSTEMS

In this paper, a fuzzy logic-based approach is taken for modeling and prediction-based control of unknown chaotic system using measured input–output data obtained from the underlying system. Under this framework, a Takagi–Sugeno (TS) fuzzy system is used with a general structure of a linear combination of Gaussian basis function in conjunction with the Levenberg–Marquardt algorithm for the optimization of model parameters. A real-time one-pass learning algorithm is developed for identifying the unknown chaotic system. Based on the fuzzy model above, a predictive controller is achieved for the stabilization of the fuzzy model on unknown unstable fixed points. Several simulation examples are included to illustrate the effectiveness and the feasibility of the proposed method for both fuzzy modeling and predictive control phases.

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Q-learning trajectory planning based on Takagi–Sugeno fuzzy parallel distributed compensation structure of humanoid manipulator

International Journal of Advanced Robotic Systems ◽

10.1177/1729881419830204 ◽

2019 ◽

Vol 16 (1) ◽

pp. 172988141983020 ◽

Cited By ~ 3

Author(s):

Shuhuan Wen ◽

Xueheng Hu ◽

Xiaohan Lv ◽

Zongtao Wang ◽

Yong Peng

Keyword(s):

Reinforcement Learning ◽

Trajectory Planning ◽

Trajectory Tracking ◽

Fuzzy Controller ◽

Learning Algorithm ◽

Fuzzy Model ◽

Large Error ◽

Learning Trajectory ◽

Tracking Accuracy ◽

Takagi Sugeno

NAO is the first robot created by SoftBank Robotics. Famous around the world, NAO is a tremendous programming tool and he has especially become a standard in education and research. Aiming at the large error and poor stability of the humanoid robot NAO manipulator during trajectory tracking, a novel framework based on fuzzy controller reinforcement learning trajectory planning strategy is proposed. Firstly, the Takagi–Sugeno fuzzy model based on the dynamic equation of the NAO right arm is established. Secondly, the design and the gain solution of the state feedback controller based on the parallel feedback compensation strategy are studied. Finally, the ideal trajectory of the motion is planned by reinforcement learning algorithm so that the end of the manipulator can track the desired trajectory and realize the valid obstacle avoidance. Simulation and experiment shows that the end of the manipulator based on this scheme has good controllability and stability and can meet the accuracy requirements of trajectory tracking accuracy, which verifies the effectiveness of the proposed framework.

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Impulsive synchronization for Takagi–Sugeno fuzzy model and its application to continuous chaotic system

Physics Letters A ◽

10.1016/j.physleta.2005.03.039 ◽

2005 ◽

Vol 339 (3-5) ◽

pp. 325-332 ◽

Cited By ~ 68

Author(s):

Yan-Wu Wang ◽

Zhi-Hong Guan ◽

Hua O. Wang

Keyword(s):

Chaotic System ◽

Fuzzy Model ◽

Impulsive Synchronization ◽

Takagi Sugeno

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Takagi-Sugeno fuzzy model parameters identification based on fuzzy c-regression model clustering algorithm and particle swarm optimization

2012 16th IEEE Mediterranean Electrotechnical Conference ◽

10.1109/melcon.2012.6196610 ◽

2012 ◽

Cited By ~ 4

Author(s):

Moez Soltani ◽

Abdelkader Chaari ◽

Faycal BenHmida

Keyword(s):

Particle Swarm Optimization ◽

Regression Model ◽

Clustering Algorithm ◽

Particle Swarm ◽

Fuzzy Model ◽

Parameters Identification ◽

Model Parameters ◽

Swarm Optimization ◽

Takagi Sugeno

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MODEL PREDICTIVE CONTROL BASED ON FUZZY SYSTEM, APPLICATION FOR A CONTINUOUS STIRRED TANK REACTOR (CSTR)

Science and Technology Development Journal ◽

10.32508/stdj.v13i1.2064 ◽

2010 ◽

Vol 13 (1) ◽

pp. 16-23

Author(s):

Tuan Quang Tran ◽

Minh Xuan Phan

Keyword(s):

Fuzzy Model ◽

Stirred Tank ◽

Continuous Stirred Tank Reactor ◽

Stirred Tank Reactor ◽

Tank Reactor ◽

Predictive Controller ◽

Continuous Stirred Tank ◽

Takagi Sugeno ◽

Predictive Controllers

The paper presents one method to design the Model Predictive Controller based on Fuzzy Model. The Plant is simulated by Takagi-Sugeno Fuzzy Model and the Optimisation Problem is solved by the Genetic Algorithms. By using the Fuzzy Model and Genetic Algorithm this MPC gives better quality than the other General Predictive Controllers. The case study of a continuous stirred tank reactor (CSTR) control is presented in this paper.

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Aperiodic Sampled-Data Control for Chaotic System Based on Takagi–Sugeno Fuzzy Model

Complexity ◽

10.1155/2021/6401231 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Minjie Zheng ◽

Shenhua Yang ◽

Lina Li

Keyword(s):

Chaotic System ◽

Fuzzy Model ◽

Sampling Period ◽

Linear Matrix ◽

Sampled Data ◽

Data Control ◽

Sampled Data Control ◽

Mean Square Exponential Stability ◽

Data Controller ◽

Takagi Sugeno

This paper investigates the aperiodic sampled-data control for a chaotic system. Firstly, Takagi–Sugeno (T-S) fuzzy models for the chaotic systems are established. The lower and upper bounds of the sampling period are taken into consideration. Then, the criteria for mean square exponential stability analysis and aperiodic sampled-data controller synthesis are provided by means of linear matrix inequalities. And the real sampling patterns can be fully captured by constructing suitable Lyapunov functions. Finally, an illustrative example shows that the proposed method is effective to guarantee that the system’s states are stable with aperiodic sampled data.

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Effect of modelled noise on stabilization of a chaotic system via Takagi-Sugeno fuzzy model

2012 IEEE 4th International Conference on Adaptive Science & Technology (ICAST) ◽

10.1109/icastech.2012.6381065 ◽

2012 ◽

Cited By ~ 2

Author(s):

Edwin A. Umoh

Keyword(s):

Chaotic System ◽

Fuzzy Model ◽

Takagi Sugeno

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Constrained cuckoo search algorithm and Takagi-Sugeno fuzzy models for predictive control

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220966308 ◽

2020 ◽

pp. 014233122096630

Author(s):

Adel Taeib ◽

Hichem Salhi ◽

Abdelkader Chaari

Keyword(s):

Predictive Control ◽

Search Algorithm ◽

Fuzzy Model ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Optimization Stage ◽

Predictive Controller ◽

Action Taking ◽

Particle Swarm Optimisation Algorithm ◽

Takagi Sugeno

In this paper, a new predictive control scheme formulated by using the Takagi-Sugeno fuzzy modeling method and a new constrained cuckoo search algorithm. The cuckoo search algorithm is used to determine the predictive controls by minimizing a constrained criterion. The Takagi-Sugeno fuzzy modelling approach is applied to forecast the states of the process. At the optimization stage, the proposed cuckoo search provides the control action taking into account constraints. The performances of the developed method are tested during its application in the three-tank process. Therefore, the experimental results demonstrate that the combination of the philosophy of the fuzzy model and cuckoo search is very good in the controlling of nonlinear processes. In addition, the closed-loop performance of the developed method is compared to approach based with the particle swarm optimisation algorithm and those obtained with fuzzy model predictive controller.

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Fuzzy Based Model Predictive Control for Uncertain Nonlinear System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.383-390.2404 ◽

2011 ◽

Vol 383-390 ◽

pp. 2404-2410

Author(s):

Li Xu ◽

Fei Liu

Keyword(s):

Nonlinear System ◽

Model Predictive Control ◽

Predictive Control ◽

Fuzzy Model ◽

Linear Matrix ◽

Uncertain Nonlinear System ◽

Predictive Controller ◽

The Stability ◽

Takagi Sugeno ◽

Parameter Dependent

In this paper, a model predictive control (MPC) scheme is investigated for uncertain nonlinear system with time delay and input constraint. First, the Takagi-Sugeno (T-S) fuzzy model is used to approximate the dynamics of nonlinear processes and the parallel distributed compensation (PDC) controllers which are parameter dependent and mirror the structure of the T-S plant model are proposed. Then a novel feedback PDC predictive controller obtained from the linear matrix inequality (LMI) solutions which can guarantee the stability of the closed-loop overall fuzzy system is put forward. Finally, a numerical example is provided to demonstrate the effectiveness and feasibility of the proposed method.

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