A moving horizon approach to multivariable input design in general linear systems with constraints

Abstract The quality of a model determines the closed loop performance of model predictive controllers. However, identification of high quality multivariable models is a time and energy intensive exercise. The industrial model predictive controllers are designed using large dimensional multivariable models and they are often identified using ad-hoc single input bump tests. A novel multivariable input design approach is developed using a modified model predictive control objective function. It is shown that the proposed input design approach is trace optimal with respect to the covariance of model parameters. The approach is shown to work well in closed loop on both well and ill-conditioned processes even under model-plant mismatch while meeting input and output constraints.