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In this work, we address the problem of control of nonlinear systems to deliver a prescribed closed-loop behavior. In particular, the framework allows for the practitioner to first specify the nature and specifics of the desired closed-loop behavior (e.g., first order with smallest time constant, second order with no more than a certain percentage overshoot, etc.). An optimization based formulation then computes the control action to deliver the best attainable closed loop behavior. To decouple the problems of determining the best attainable behavior and tracking it as closely as possible, the optimization problem is posed and solved in two tiers. In the first tier, the focus is on determining the best closed-loop behavior attainable, subject to stability and tracking constraints. In the second tier, the inputs are tweaked to possibly improve the tracking of the optimal output trajectories given by the first tier. The efficacy of the proposed method and the various specific formulations needed are illustrated through implementation on a linear system subject to output feedback, a nonlinear CSTR subject to uncertainty and rate of change of input constraints, and a reactor separator system. The simulation results demonstrate significantly improved adherence to the prescribed performance criteria over a predictive controller representative of existing approaches. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of subspace-based model identification and predictive control of particulate process subject to uncertainty and time-varying parameters. To this end, subspace identification techniques are first adapted to handle the batch nature of the data. A linear model predictive controller (MPC) is next formulated to enable achieving a particle size distribution with desired characteristics subject to both manipulated input and product quality constraints. The proposed approach is implemented on a seeded batch crystallizer process and compared with an open-loop policy, as well as a traditional trajectory tracking policy, using classical control. The proposed MPC is shown to achieve superior performance and the ability to respect tighter product quality constraints, as well as robustness to uncertainty. [ABSTRACT FROM AUTHOR]

Batch process reactors are often used for products where quality is of paramount importance. To this end, this work addresses the problem of direct, data-driven, quality control for batch processes. Specifically, previous results using subspace identification for modeling dynamic evolution and making quality predictions are extended with two key novel contributions: first, a method is proposed to account for midbatch ingredient additions in both the modeling and control stages. Second, a novel model predictive control scheme is proposed that includes batch duration as a decision variable. The efficacy of the proposed modeling and control approaches are demonstrated using a simulation study of a poly(methyl methacrylate) (PMMA) reactor. Closed loop simulation results show that the proposed controller is able to reject disturbances in feed stock and drive the number-average molecular weight, weight-average molecular weight, and conversion to their respective set-points. Specifically, mean absolute percentage errors (MAPE) in these variables are reduced from 8.66%, 7.87%, and 6.13% under traditional PI control to 1.61%, 1.90%, and 1.67%, respectively. [ABSTRACT FROM AUTHOR]

Thiswork presents an offset-free model predictive control (OF-MPC)design for energy-efficient control of a heat pump. A model developedfrom a combination of first-principles and empirical components withparameters estimated using real heat pump data is used as a test bedfor the implementation of the MPC design. Open-loop system dynamicsare examined first to design and implement the appropriate controlstructure. Next, a linear model is identified using appropriate steptest simulations on the detailed heat pump model. Subsequently, amodel predictive controller formulation, which eliminates the tradeoffbetween tracking and energy objectives, is designed. The MPC includesan augmented model (including disturbance states) and an associatedLuenberger observer to estimate the disturbance (plant-model mismatchat steady state). Simulation results subject to realistic disturbancesand measurement noise demonstrate that energy savings can be achieved(anywhere between 1.7% and 1.9%), while preserving a safer operatingwindow (a 43% improvement, with respect to a safe operation measure)with the proposed OF-MPC design that achieves tighter tracking regulation(improvement of 40% and above), compared to a traditional controlapproach. [ABSTRACT FROM AUTHOR]

Thiswork considers the problem of handling the failure of purificationequipment in a hydrogen production process. In this process, naturalgas and superheated steam are fed to a heated chemical reactor termeda reformer to produce hydrogen. The effluent gas is further processedand finally purified in a pressure swing adsorber (PSA). The off-gasout of the PSA is used to provide heat to the reformer. The failureof the PSA results in the loss of the off-gas, precluding the possibilityof the continuation of nominal operation. If not properly handled,this fault can lead to a shutdown of the entire plant. To achievestable operation while meeting operating requirements, a model predictivecontrol (MPC) based safe-parking framework is designed for the handlingof the fault. The key idea is to drive the process to a feasible operatingpoint that enables stable operation in the faulty mode. MPC is usedto handle the multivariable nature of the process and operating constraints.It also guides the process to a different operating region while meetingoperating requirements. The effectiveness of the safe-parking designis demonstrated through simulations using a first-principles modelof the hydrogen production unit. [ABSTRACT FROM AUTHOR]