In order to improve the robust performance for a type of nonlinear multivariable system, a new fuzzy variable structure control method is introduced in detail, which combined the features of traditional variable structure control method with the advantages of the fuzzy control theory. The present work realized the linearization of the nonlinear multivariable system, and then the new fuzzy varia...

A multivariable laboratory process of four interconnected water tanks is considered for modeling and robust control. The objective of the current study is to design and implement a variety of control algorithms and present corresponding robust control analysis on a multivariable laboratory four tank process. The control methodologies considered are (i) decentralized PI control, (ii) “inner-oute...

This paper presents a direct multivariable adaptive controller using neural network which adapts to the changing parameters of the multivariable nonlinear system with nonminimum phase behavior, mutual interactions and time delays. It base on the theory which a nonlinear multivariable systems to be controlled is divided a linear part and a nonlinear part. The controller parameters of the linear ...

Abstract. The one variable Krawtchouk polynomials, a special case of the 2F1 function did appear in the spectral representation of the transition kernel for a Markov chain studied a long time ago by M. Hoare and M. Rahman. A multivariable extension of this Markov chain was considered in a later paper by these authors where a certain two variable extension of the F1 Appel function shows up in th...

This paper proposes a novel linear recurrent neural network for multivariable system identification, namely a linerec neural network (LNN). Based on this network, the transfer function matrix model of a multivariable system can be identified directly according to its input and output data. In this way, LNNs differ from existing neural networks. An LNN is constructed based on the identification ...