Adaptive disturbance estimation for offset-free SISO Model Predictive Control

AbstractOffset-free model predictive control (MPC) algorithms for nonlinear state-space process models, with modeling errors and under asymptotically constant external disturbances, is the subject of the paper. The main result of the paper is the presentation of a novel technique based on constant state disturbance prediction. It was introduced originally by the author for linear state-space models and is generalized to the nonlinear case in the paper. First the case with measured state is considered, in this case the technique allows to avoid disturbance estimation at all. For the cases with process outputs measured only and thus the necessity of state estimation, the technique allows the process state estimation only - as opposed to conventional approach of extended process-and-disturbance state estimation. This leads to simpler design with state observer/filter of lower order and, moreover, without the need of a decision of disturbance placement in the model (under certain restrictions), as in the conventional approach. A theoretical analysis of the proposed algorithm is provided, under applicability conditions which are weaker than in the conventional approach. The presented theory is illustrated by simulation results of nonlinear processes, showing competitiveness of the proposed algorithms.

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Modelling and disturbance estimation for model predictive control in building heating systems

Energy and Buildings ◽

10.1016/j.enbuild.2016.08.077 ◽

2016 ◽

Vol 130 ◽

pp. 532-545 ◽

Cited By ~ 14

Author(s):

Edward O’Dwyer ◽

Luciano De Tommasi ◽

Konstantinos Kouramas ◽

Marcin Cychowski ◽

Gordon Lightbody

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Heating Systems ◽

Disturbance Estimation

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Offset-Free Model Predictive Control for Active Magnetic Bearing Systems

Actuators ◽

10.3390/act7030046 ◽

2018 ◽

Vol 7 (3) ◽

pp. 46 ◽

Cited By ~ 5

Author(s):

Angelo Bonfitto ◽

Luis Miguel Castellanos Molina ◽

Andrea Tonoli ◽

Nicola Amati

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Low Frequency ◽

Magnetic Bearing ◽

Active Magnetic Bearing ◽

Reference Tracking ◽

Plant Model ◽

Disturbance Estimation ◽

Model Mismatch ◽

Free Model

This paper presents the study of linear Offset-Free Model Predictive Control (OF-MPC) for an Active Magnetic Bearing (AMB) application. The method exploits the advantages of classical MPC in terms of stability and control performance and, at the same time, overcomes the effects of the plant-model mismatch on reference tracking. The proposed approach is based on a disturbance observer with an augmented plant model including an input disturbance estimation. Besides the abovementioned advantages, this architecture allows a real-time estimation of low-frequency disturbance, such as slow load variations. This property can be of great interest for a variety of AMB systems, particularly where the knowledge of the external load is important to regulate the behavior of the controlled plant. To this end, the paper describes the modeling and design of the OF-MPC architecture and its experimental validation for a one degree of freedom AMB system. The effectiveness of the method is demonstrated in terms of the reference tracking performance, cancellation of plant-model mismatch effects, and low-frequency disturbance estimation.

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