A Novel Approach to Implement Takagi-Sugeno Fuzzy Models

Abstract

This paper proposes new algorithms based on the fuzzy c-regressing model algorithm for Takagi-Sugeno (T-S) fuzzy modeling of the complex nonlinear systems. A fuzzy c-regression state model (FCRSM) algorithm is a T-S fuzzy model in which the functional antecedent and the state-space-model-type consequent are considered with the available input-output data. The antecedent and consequent forms of the proposed FCRSM consists mainly of two advantages: one is that the FCRSM has low computation load due to only one input variable is considered in the antecedent part; another is that the unknown system can be modeled to not only the polynomial form but also the state-space form. Moreover, the FCRSM can be extended to FCRSM-ND and FCRSM-Free algorithms. An algorithm FCRSM-ND is presented to find the T-S fuzzy state-space model of the nonlinear system when the input-output data cannot be precollected and an assumed effective controller is available. In the practical applications, the mathematical model of controller may be hard to be obtained. In this case, an online tuning algorithm, FCRSM-FREE, is designed such that the parameters of a T-S fuzzy controller and the T-S fuzzy state model of an unknown system can be online tuned simultaneously. Four numerical simulations are given to demonstrate the effectiveness of the proposed approach.

A Novel FEM Based T-S Fuzzy Particle Filtering for Bearings-Only Maneuvering Target Tracking

“…Suppose that zk={zk,l}l=1C is a measurement set and zfalse^k={zfalse^ki}i=1Nf is a predictive measurement set, zk,l denotes the l th measurement, and zfalse^ki denotes the predictive measurement based on the i th fuzzy rule at time k. On the basis of the traditional FCRM [33], the weighted entropy is introduced to balance the membership degree, which is called the entropy adjustment method. Meanwhile, the target feature information reflects the target motion trend in real time.…”

“…Comparison: All of the samples were used to train the fuzzy model parameters in the conventional T-S fuzzy model described in [33,34], after which the trained fuzzy model was used to classify or estimate the model state. In our proposed algorithm, the parameters of the T-S fuzzy model were updated by using the recursive mechanism of the algorithm, which required the fast convergence.…”

“…The theory of fuzzy models is a general concept proposed in the last decade. Among the various types of fuzzy models, there is a very important T-S [33,34] fuzzy model. Due to its special rules of consequence-structure and its success in function approximation, it has been widely used recently.…”

Labeled Multi‐Bernoulli Maneuvering Target Tracking Algorithm via TSK Iterative Regression Model

“…In summary, it is a problem to be solved urgently to research an adaptive modeling and tracking algorithm suitable for the case of strong target maneuvering. The TSK fuzzy model [ 23–25 ] is the key technology to solve such problem. In the case of single target strong maneuver, we have proposed TSK iterative regression multiple model [ 26–29 ] , which effectively solved the problem of uncertainty of target motion in nonlinear systems.…”

An Approach to the Design of Nonlinear State-Space Control Systems

“…A nonlinear state observer for the estimation of different gas species concentration profiles is proposed in [18] and a state-space approach to predictive control is given in [17]. Robustness features are added in [40] to state-space control, while the combination with fuzzy modelling and control is treated in [1,5,12,20,32].…”