(441h) Robust Explicit Optimization and Control within the Paroc Framework
AIChE Annual Meeting
2018 AIChE Annual Meeting
Computing and Systems Technology Division
Advances in Optimization Under Uncertainty
Wednesday, October 31, 2018 - 10:13am to 10:32am
In this work, we consider the development of closed-loop explicit robust rolling horizon solutions that correspond to MPC and scheduling problems and their applicability within the PAROC framework, i.e. their utilization in design, control, scheduling problems and their interactions. We consider box-constrained model uncertainty on linear state-space problems upon which we (i) formulate the robust counterpart in a single-step problem formulation, (ii) apply linear transformations to acquire the feasible space as a function of the initial state value realizations and the degrees of freedom and (iii) apply multi-parametric (mixed-integer) programming to acquire the explicit solution of the problem. With this approach, we show (i) how the need for a dynamic programming approach can be alleviated while (ii) guaranteeing the robust nature of the final solution and (iii) we extend the approach to the hybrid case where both integer and continuous variables can be considered via . We present the developments through examples of control, scheduling and design optimization problems.
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