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Summary: This article addresses the plant‐model mismatch estimation problem for linear multiple‐input and multiple‐output systems operating under the dynamic matrix control (DMC) implementation of model predictive control. An autocovariance‐based method is proposed, aiming to identify parameter values that minimize the discrepancy between the theoretical autocovariance matrices derived from implementing the (explicit) DMC control law and the sampled autocovariance matrices calculated from operating data. We provide proof that the method results in unbiased estimates. A means for dealing with potential overfitting issues caused by the finite step response models used in DMC in practice is proposed. Several examples are presented to illustrated the theoretical developments. [ABSTRACT FROM AUTHOR]

• We present a novel framework for completely integrated scheduling and model predictive control of chemical processes. • The controller behavior is represented fully and explicitly by including the MPC KKT conditions directly in the scheduling formulation. • Two case studies demonstrate that schedules obtained using knowledge of closed-loop dynamics lead to increased profit compared with "open-loop" scheduling approaches. Competitive global market conditions and the availability of real-time pricing data call for agile and flexible operations in the chemical process industries. As the operating paradigm shifts, process scheduling decisions may consider a shorter time scale, and interactions with the process control layer become significant. The integration of these two decision-making layers is an active area of research. In this work, we propose a novel framework for the integration of production scheduling and linear model predictive control. Our approach explicitly represents the closed-loop response of the system by embedding the KKT conditions of the controller in a nonlinear programming formulation of the optimal scheduling problem. We present two case studies which demonstrate that control-informed scheduling leads to superior realized performance compared with a hierarchical, sequential decision-making structure. [ABSTRACT FROM AUTHOR]

The area of controller performance monitoring, assessment, and diagnosis for model predictive control (MPC) has seen an increase in interest in recent years. A frequently identified cause of degraded performance is mismatch between the plant model used in the controller and the true dynamics of the plant. Most recent research focuses on locating plant-model mismatch in order to reduce the considerable effort required to re-identify the model. In this paper, we present a novel autocovariance-based plant-model mismatch estimation approach for unconstrained MPC based on linear state space models. We show that (additive) plant-model mismatch can be quantified as the solution of an optimization problem which minimizes the discrepancy between the sample autocovariance of plant outputs and the corresponding value obtained from theoretical predictions. We illustrate our theoretical results with two simulation case studies, demonstrating good performance in estimating parametric mismatch. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel autocovariance-based plant-model mismatch estimation approach for linear MPC MIMO control loops with changing setpoints and measurable disturbances. Assuming a noise model is available and that there are of periods of operating data where the active set of the controller is fixed and the plant-model mismatch is invariant, we establish an explicit relation between the autocovariance matrices of the mean-centered process outputs and the plant-model mismatch. We then formulate the mismatch estimation problem as an optimization aimed at minimizing the difference between the theoretical autocovariance, computed from the established relation, and actual output autocovariances, calculated from the plant data. We elaborate our results for step-response models typically used in MPC, as well as for parametric (transfer function models) in both continuous time and discrete time. A simulation case study for an unconstrained MPC controller with measurable disturbances is used to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, we present autocovariance-based estimation as a novel methodology for determining plant-model mismatch for multiple-input, multiple-output systems operating under model predictive control. Considering discrete-time, linear time invariant systems under reasonable assumptions, we derive explicit expressions of the autocovariances of the system inputs and outputs as functions of the plant-model mismatch. We then formulate the mismatch estimation problem as a global optimization aimed at minimizing the discrepancy between the theoretical autocovariance estimates and the corresponding values computed from historical closed-loop operating data. Practical considerations related to implementing these ideas are discussed, and the results are illustrated with a chemical process case study. [ABSTRACT FROM AUTHOR]

• A framework for integrated scheduling and control of chemical processes. • Data-driven Hammerstein–Wiener models are used to represent schedule-relevant variables. • Parametric representations of Hammerstein–Wiener models are derived to reduce computational effort. • Two case studies demonstrate significant reduction in solution times compared with prior works. Global competition and increasingly complex product slates and supply chains motivate a continuous drive towards enterprise-wide optimization and integrated decision-making in the chemical process industries. Integration of production scheduling and process control poses particular challenges: the resulting optimization problems tend to be high-dimensional and nonlinear, calling for development of new computational methods. In this work, we propose a novel modeling framework for integrated scheduling and control. We build on existing methods which use data-driven Hammerstein–Wiener models to represent the dynamics of (scheduling-relevant) process variables. This model structure is leveraged to reduce the size of the scheduling optimization problem, by identifying parsimonious parametric representations of the underlying dynamics. The advantages of the approach are demonstrated on two case studies, in which the computational effort is shown to be significantly reduced compared to existing methods, while still capturing the relevant process dynamics. [ABSTRACT FROM AUTHOR]