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Multi-similarity based integrated learning for working condition matching modeling

Process controller performance evaluation systems have been widely used in modern industries with increasingly complex control objects. However, there is a prerequisite for their implementation: the models obtained from the system identification are all from the same stable operating conditions, because when the operating conditions are not unique, the performance evaluation strategy cannot determine whether the controller performance degradation is due to real uncertainties or changes in the operating conditions. Therefore, it is necessary to identify the corresponding model according to the specific operating conditions and use it to solve the optimization problem to obtain a specific performance benchmark for accurate performance evaluation. In this paper, we propose an online data modeling strategy based on an improved just-in-time learning algorithm, which proposes an integrated learning framework for similar sample selection using a variety of different similarity measures in the selection of local modeling neighborhoods. Then, the local prediction models are trained, and finally, the final results are obtained by integrating the strategy to synthesize multiple local prediction models. The effectiveness of the proposed algorithm in this paper is demonstrated by Wood-Berry simulation experiments.