Steady-state target optimization designs for integrating real-time optimization and model predictive control |
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Affiliation: | 1. French-Argentine International Center for Information and Systems Sciences (CIFASIS), CONICET-Universidad Nacional de Rosario (UNR), 27 de Febrero 210bis, S2000EZP Rosario, Argentina;2. Institute of Technological Development for the Chemical Industry (INTEC), CONICET-Universidad Nacional del Litoral (UNL), Güemes 3450, 3000 Santa Fe, Argentina;1. Faculty of Engineering and Natural Sciences, Aalesund University College, Ålesund, Norway;2. Islamic Azad University of Kazeroon, Kazeroon, Iran;1. College of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, PR China;2. College of Electronics and Information Engineering, Tongji University, Shanghai 201804, PR China;1. Medicinal Plants Research Center, Yasuj University of Medical Science, Yasuj, Iran;2. Department of Chemistry, Abadan Branch, Islamic Azad University, Abadan, Iran |
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Abstract: | In industrial practice, the optimal steady-state operation of continuous-time processes is typically addressed by a control hierarchy involving various layers. Therein, the real-time optimization (RTO) layer computes the optimal operating point based on a nonlinear steady-state model of the plant. The optimal point is implemented by means of the model predictive control (MPC) layer, which typically uses a linear dynamical model of the plant. The MPC layer usually includes two stages: a steady-state target optimization (SSTO) followed by the MPC dynamic regulator. In this work, we consider the integration of RTO with MPC in the presence of plant-model mismatch and constraints, by focusing on the design of the SSTO problem. Three different quadratic program (QP) designs are considered: (i) the standard design that finds steady-state targets that are as close as possible to the RTO setpoints; (ii) a novel optimizing control design that tracks the active constraints and the optimal inputs for the remaining degrees of freedom; and (iii) an improved QP approximation design were the SSTO problem approximates the RTO problem. The main advantage of the strategies (ii) and (iii) is in the improved optimality of the stationary operating points reached by the SSTO-MPC control system. The performance of the different SSTO designs is illustrated in simulation for several case studies. |
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Keywords: | Real-time optimization Steady-state optimization Target optimization Constraint control Model predictive control |
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