Parallel Estimation Respecting Constraints of Parametric Models of Cold Rolling

Model-based predictors and controllers frequently depend on efficient recursive estimation of model parameters. Similarly often, there are known hard bounds on parameter values. Adaptive control applied for rolling mills represents a typical example of such case. While common estimation algorithms are elaborated enough to be utilized in industrial practice, it is difficult to find implementation of bounded estimation, which is both formally consistent and suitable for reliable applications. Solution offered in this paper is based on simultaneous run of two or more proven estimators different in applied process models. Both simulated and real data examples are provided.