Embedded model control calls for disturbance modeling and rejection

• Published in: ISA transactions 2012-09, Vol.51 (5), p.584-595
• Main Authors: Canuto, Enrico, Acuna-Bravo, Wilber, Molano-Jimenez, Andrés, Perez Montenegro, Carlos
• Format: Article
• Language: English
• Subjects: Adaptative systems; Algorithms; Applied sciences; Computer science; control theory; systems; Computer Simulation; Control system synthesis; Control theory. Systems; Disturbance; Embedded model control; Exact sciences and technology; Feedback; Model error; Modelling and identification; Models, Statistical; Noise estimator; Rejection
• ISSN: 0019-0578; 1879-2022
• DOI: 10.1016/j.isatra.2012.04.002

Robust control design guarantees closed-loop stability of a model-based control law in the presence of parametric uncertainties. Stability is guaranteed by introducing some ignorance coefficients and restricting the feedback control effort. Embedded Model Control shows that the model-based control law can be kept intact in the case of uncertainty, if the controllable dynamics is complemented by a suitable disturbance dynamics. The disturbance state must be driven by an unpredictable input vector, the noise, which can only be estimated from the model error i.e. the difference between plant and model output. The uncertainty-based design concerns the noise estimator, so as to prevent the model error from conveying uncertainty components which are command-dependent and thus prone to destabilizing the controlled plant. Separation of the components in the low and high frequency domain by the noise estimator itself allows stability recovery and guarantee, and the rejection of low frequency components. Two simple case studies help to understand the key assets of the methodology. ► Embedded Model Control shows that a model-based control law can be kept intact under uncertainty, if controllable dynamics is complemented by a suitable disturbance dynamics. ► To be real-time updated the disturbance state is driven by an unpredictable input vector, the noise, which can be only estimated from model error. ► The uncertainty-based design concerns noise estimator. ► Separation of the model error in the frequency domain allows stability recovery and guarantee, and the rejection of the low frequency uncertainty.