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Feedback control design using model predictive control formulation and Carleman approximation method
Author(s) -
Hashemian Negar,
Armaou Antonios
Publication year - 2019
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16666
Subject(s) - hessian matrix , nonlinear system , control theory (sociology) , model predictive control , computation , stability (learning theory) , sensitivity (control systems) , computer science , bilinear interpolation , mathematical optimization , mathematics , control (management) , algorithm , engineering , physics , quantum mechanics , artificial intelligence , machine learning , electronic engineering , computer vision
Model predictive control (MPC) for nonlinear systems involves a nonlinear dynamic optimization (NDO) step, which is required to be solved repeatedly. This step is computationally demanding, specially in dealing with constrained and/or nonlinear large‐scale systems. This paper presents a method for accelerating the NDO in state‐feedback regulation problems. Exploiting Carleman approximation, this method represents the nonlinear dynamics in a bilinear form and discretizes the resulting system in the time domain. The gradient and Hessian of the cost function with respect to the feedback gains are also analytically derived. The Carleman approximation of the nonlinear system may introduce errors in the prediction and sensitivity analysis. The manuscript derives a criterion under which the input‐to‐state stability of the new design is guaranteed. The proposed MPC is implemented in a chemical reactor example. Simulation results show that replacing conventional MPC schemes by the presented method reduces the computation time by an order of magnitude.