Multi-Model Control of Nonlinear Systems Using an Improved Procedure for Nominal Local Models Selection

This paper is focused on selecting of nominal local models (NLMs) to design the multi-model based controller for nonlinear systems. Also gap metric is used as a powerful tool to measure the distance between local linear models. In order to decompose the nonlinear system into smaller number of NLMs, selecting of NLMs and designing the local controllers are integrated. The designed local controllers for the primary sub-regions may be able to stabilize other local models of the next sub-regions while the traditional successive search methods disregard the efficacy of the former NLMs and corresponding local controllers. Therefore, these methods suffer from the redundancy problem and complexity of the multi-model controller. It is shown that improving of NLMs selection procedure by considering the efficacy of local controllers in the entire operating range can intensely reduce the number of NLMs and local controllers. Therefore, improving the decomposition procedure, screening the efficacy of the local controllers in the entire operating range, and reducing the number of local controllers are the main advantages of the proposed decomposition procedure in this paper. To evaluate the effectiveness of the proposed method, a highly nonlinear process, pH neutralization, is simulated. The results prove the effectiveness of the proposed method in set-point tracking and disturbance rejection while the number of NLMs is half in comparison to other traditional successive search methods.