(188m) A Multi-Parametric Bi-Level Optimization Strategy for Hierarchical Model Predictive Control
AIChE Annual Meeting
2017
2017 Annual Meeting
Computing and Systems Technology Division
CAST Rapid Fire Session II
Monday, October 30, 2017 - 5:15pm to 5:20pm
In this work, we propose the use of a novel algorithm [6, 7] capable of providing the exact, global and parametric solution of bi-level programming problems for the solution of linear or quadratic hierarchical control problems. The derivation of hierarchical explicit/multi-parametric MPC (mp-MPC) controllers through the proposed algorithm, allows the controller to only do simple function evaluations at every control step, instead of solving the full bi-level optimization problem. We are illustrating the proposed methodology through a simple example of a two-level hierarchical control of a continuous stirred tank reactor (CSTR) system, with an economic objective function in the first control level, and a set-point tracking objective function in the second control level [8, 9]. The main idea of our approach is to treat the lower control level (set-point tracking) as a multi-parametric programming problem in which the input flowrate (optimization variable of the upper level control problem) along with system disturbances and states are considered as parameters. The resulting parametric solutions are then substituted into the upper level economic control problem, which can be solved as a set of single-level parametric programming problems. The obtained hierarchical controller is then able to effectively reject disturbances and maintain the system at the given set-points (driven by the second control level) in a more economical way (driven by the first control level) than a classical MPC controller.
References:
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[5] GümüÅ, Z.H., Floudas, C.A. Global optimization of mixed-integer bilevel programming problems (2005) Computational Management Science, 2 (3), pp. 181-212.
[6] FaÃsca, N.P., Saraiva, P.M., Rustem, B., Pistikopoulos, E.N. A multi-parametric programming approach for multilevel hierarchical and decentralized optimisation problems (2009) Computational Management Science, 6 (4), pp. 377-397.
[7] Avraamidou, S., Diangelakis, N. A., Pistikopoulos, E. N. Mixed Integer Bilevel Optimization through Multi-parametric Programming (2017) Foundations of Computer Aided Process Operations / Chemical Process Control; In Press. (http://folk.ntnu.no/skoge/prost/proceedings/focapo-cpc-2017/FOCAPO-CPC%202017%20Contributed%20Papers/73_FOCAPO_Contributed.pdf)
[8] Christofides, P.D., El-Farra, N.H. Special Issue:Â Economic nonlinear model predictive control (2014) Journal of Process Control, 24 (8), p. 1155.
[9] Ellis, M., Durand, H., Christofides, P. A tutorial review of economic model predictive control methods (2014) Journal of Process Control, 24 (8), pp. 1156â1178.
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