## 1 Introduction  
Along with the development of sensor technology, electro mechanical system and digital communication network, both the basis structure and run mode of engineering control system changed enormously [1; 2]. These components of the engineering control systems are more intelligent, they will never act as the single role of controlled object or controller [3; 4], instead an agent integrates sense, compute, execution and communication together. However, it also brings more uncertainty to the system, such as time variation and nonlinear characteristics. Therefore, the investigation of agent based fuzzy T-S multi-model system by the way of data information of the process is studied.  
Many works have mentioned the idea of agent based modelling [5; 6; 7; 8]. Different from the traditional model, the agent based modelling is formed by agents that lie in different layers [9]. There exists the interaction between the agents, and the input signals for an agent may be affected by the feedback information of others. Affecting other agents and affected by the others at the same time, the essence of the cooperation and interaction between different agents is the communication process of data information [10]. The communication channels between agents form the real time dynamic network. Within the network, each agent can execute the task alone or collaborate with other agents to obtain a fixed goal [11; 12]. However, the control-oriented models of engineering systems call for further investigation.  
Few attempts have been initiated in the study of the agent based fuzzy T-S multi-model system. In [13], a multi-agent consensus problem with an active leader and variable interconnection topology is studied. A neighbor-based local controller together with a neighbor-based state-estimation rule is given for each autonomous agent to track the leader. Then, a cooperative output regulation problem for linear uncertain multi-agent problem is studied in [14], and it shows a simple way of cooperation of the system if all subsystems of the agents have the same nominal dynamics. In [15], the fuzzy modelling and consensus problem of multi-agent nonlinear systems is analyzed. The T-S fuzzy models are proposed to express the system with variable structures. However, the agent based fuzzy multi-model strategy remains preliminary because how to construct the agents and lead all the agents in a multi-model system to reach a consensus is still a problem. More endeavors should be conducted in order to use the modelling strategy widely.  
Starting from the basic concepts of agent and fuzzy T-S model, the agent based fuzzy T-S multi-model (ABFT-SMM) system is proposed in this paper. In this system, the agent that is described with a dynamic equation will be the local part of the multi-model, and the consensus of the agents accomplished by the center average defuzzifier. Moreover, we proved the ABFT-SMM could approximate any linear or nonlinear systems at arbitrary accuracy.  
The structure of the rest of this paper is as following. In Section 2, several concepts of agents and fuzzy T-S model are introduced. In Section 3, the method for building agent based fuzzy multi-model is described in detail. In section 4, the approximation capability of the agent based fuzzy multi-model system is analyzed. Three different application examples are given in Section 5. Lastly, the conclusion is drawn in Section 6.