MULTIMODEL-BASED MINIMUM BIAS CONTROL OF A BENCHMARK PAPER-MACHINE PROCESS

This paper presents a multimodel-based minimum bias (MB), long-range p redictive control algorithm that minimizes the bias in the output usin g a multiobjective optimization method. To enhance the robustness of t he control system, the MB control scheme is extended to the multimodel - or multicontroller- based MB control scheme, which establishes sever al models simultaneously. Models with relatively small prediction erro rs are selected to form a group of acceptable models. The final contro l is chosen to be the weighted average of the MB controls. Models with significant or large errors are reinitialized. The proposed MB contro l scheme is evaluated by application to a paper machine benchmark prob lem and its performance is compared with that of other controllers.