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In this paper, a new approach based on the multi-agent concept is proposed for model predictive control (MAMPC) of a nonlinear fast dynamic system. This concept achieves a transformation of the centralized MBPC in a decentralized and supervised technique. The control of the nonlinear system subject to constraints is achieved via a set of actions taken from different agents. The actions are based on an analytical solution and a fuzzy supervision is used to monitor the closed system using a supervisory loop concept. These strategies of control permits to unlock the obstacle met by the MBPC dedicated to the oscillating and fast dynamic systems and facilitate their real time implantation. The multi-agent compares favorably with respect to a numerical optimization procedure and offers a solution for non convex optimization. A simulation example shows the fast execution of the proposed concept compared to conventional procedure.

This paper describes the development of a strategy to optimally tune constrained predictive controller of Multi Inputs Multi Outputs Nonlinear system with T-S fuzzy modeling approach. The proposed method consists of using T-S fuzzy modeling to determine process model. The intelligent algorithms Particle Swam Optimization is applied to provide the controls action by solving nonlinear optimization problems which is function of the future prediction outputs and the future inputs. The performance of this strategy is evaluated through its application to Quadruple-Tank Process.

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