(371m) The Development of Multiparametric Model Predictive Controllers Via the Argonaut Framework
AIChE Annual Meeting
2019 AIChE Annual Meeting
Computing and Systems Technology Division
Interactive Session: Systems and Process Control
Tuesday, November 12, 2019 - 3:30pm to 5:00pm
In this work, a nonlinear dynamic optimization control scheme is directly approximated using the data-driven framework ARGONAUT. In contrast to (i) standard approaches which utilize an approximate linear model of the nonlinear dynamic system as the basis of a model predictive control scheme, and (ii) data-driven approaches which identify the relationship between the current state and the manipulated action, the proposed methodology treats the original nonlinear optimization problem as a black box to develop an approximate quadratic optimization formulation. In this way, ARGONAUT can explicitly build approximate surrogate formulations for the objective function and the feasible space of the original nonlinear optimization formulation separately. To ensure the developed objective function and constraint set that define the quadratic formulation are satisfactory, ARGONAUT utilizes optimal sampling strategies, global parameter estimation, and model validation [4, 5]. The constructed model predictive controller is then recast as its multiparametric counterpart and solved using the Parametric Optimization (POP) toolbox, providing the explicit set of optimal control laws as an affine function of bounded uncertain parameters [6, 7]. The proposed methodology is tested on a continuously stirred tank reactor and a distillation column.
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