This paper deals with a new systematic multimodel controller design for nonlinear systems. The design of local controllers based on performance requirements is incorporated with the concept of local models selection as an optimization problem. Gap metric and stability margin are used as measuring tool and operation space dividing criterion, respectively. The developed method provides support to design a simple structured multiple proportional-integral (PI) controller which guarantees both robust stability and time-domain performance specifications. The main advantages of the proposed method are avoiding model redundancy, not needing a priori knowledge about system, having simple structure, and easing the implementation. To evaluate the presented multimodel controller design procedure, three benchmark nonlinear systems are studied. Both simulations and experimental results prove the effectiveness of the proposed method in set point tracking and disturbance rejection.

Recently, the multi-model controllers design was proposed in the literature based on integrating of the stability and performance criteria. Although these methods overcome the redundancy problem, the decomposition step is very complex and time consuming. In this paper, a cascade design of multi-model control is presented that is made from two sequential steps. In the first step, the nonlinear system is decomposed into a set of linear subsystems by just considering the stability criterion. In this step, the gap metric is used as a smart tool to measure the distance between linear subsystems. While the closed-loop stability is gained through the first step, the performance is improved in the second step by adding internal model controllers in a cascade structure. Therefore, the proposed idea supports designing a multi-model controller in a simple way by integrating the stability and performance criteria in two independent cascade steps. As a result, the proposed method avoids the model redundancy problem, has a simple structure, guarantees the robust stability, and improves the performance. Two nonlinear chemical processes are simulated to evaluate the proposed multi-model controller approach.

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This paper presents a new multimodel controller design approach incorporating stability and performance criteria. The gap metric is employed to measure the distance between local models. An efficient method based on state feedback strategy is introduced to improve the maximum stability margin of the local models. The proposed method avoids local model redundancy, simplifies the multimodel controller structure, and supports employing of many linear control techniques, while does not rely on a priori experience to choose the gridding threshold value. To evaluate the proposed method, three benchmark nonlinear systems are studied. Simulation results demonstrate that the method provides the closed-loop stability and performance via a simple multimodel structure in comparison with the opponents.

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