Sustainability consider ations have placed increasing emphasis on t he energy efficient operation and control of temperature control systems. It is estimated t hat t he use of advanced control structures could lead to valuable savings in energy expenditure (up to 15-20 %) .This work considers t he problem of developing a model predictive control (NIP C) algorit hm for temperature cont rol in buildings . To this end, a cascade control structure was designed to regulate t he room temperature subj ect to heat load disturbances, such as outdoor condit ions or cha nges in the internal gains (i .e., number of people in a room). The inner loop of t he cascade control structure involved controlling key variables of a vapor compression cycle (VCC), namely the superheat and supply air temperature (from the evaporator), by manipulating t he compressor speed and valve opening (components in the VCC). Linear inputoutput models were appropriately identified for the vee using a detailed first-principles model (adapted from T hermosys) for event ual utilization in a predictive control design .Then, closed loop simulations were performed by interfacing the VCC model wit h EnergyPlus (developed by the U.S . Department of Energy) , which was used to model realistic room temperature behavior. The control performance using a predictive controller (in t he inner loop) was t hen evaluated against PI control.

The problem of driving a batch process to a specified product quality using data-driven model predictive control (MPC) is described. To address the problem of unavailability of online quality measurements, an inferential quality model, which relates the process conditions over the entire batch duration to the final quality, is required. The accuracy of this type of quality model, however, is sensitive to the prediction of the future batch behavior until batch termination. In this work, we handle this "missing data" problem by integrating a previously developed data-driven modeling methodology, which combines multiple local linear models with an appropriate weighting function to describe nonlinearities, with the inferential model in a MPC framework. The key feature of this approach is that the causality and nonlinear relationships between the future inputs and outputs are accounted for in predicting the final quality and computing the manipulated input trajectory. The efficacy of the proposed predictive control design is illustrated via closed-loop simulations of a nylon-6,6 batch polymerization process with limited measurements.

This work considers the problem of controlling batch processes to achieve a desired final product quality subject to input constraints and faults in the control actuators. Specifically, faults are considered that cannot be handled via robust control approaches, and preclude the ability to reach the desired end-point, necessitating fault-rectification. A safe-steering framework is developed to address the problem of determining how to utilize the functioning inputs during fault rectification to ensure that after fault-rectification, the desired product properties can be reached upon batch termination. To this end, first a novel reverse-time reachability region (we define the reverse time reachability region as the set of states from where the desired end point can be reached by batch termination) based MPC is formulated that reduces online computations, as well as provides a useful tool for handling faults. Next, a safe-steering framework is developed that utilizes the reverse-time reachability region based MPC in steering the state trajectory during fault rectification to enable (upon fault recovery) the achieving of the desired end point properties by batch termination. The proposed controller and safe-steering framework are illustrated using a fed-batch process example. V

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This Article addresses the problem of integrating subspace-based model identification with first-principles modeling for handling scenarios where the subspace model identifies spurious relationships between inputs and outputs. The key motivation is to suitably synergize the two approaches while retaining the simplicity of subspace-based model identification. In the proposed methodology, as is done with traditional subspace identification, state trajectories that best describe the input−output data are first computed (which implicitly correspond to an underlying linear time invariant model). In computing the system matrices using the state trajectories, constraints derived from first-principles understanding are incorporated into the optimization problem. To reconcile the resulting mismatch between the state trajectories and the system matrices, an iterative process is utilized. First, the system matrices computed from the optimization problem are utilized to re-estimate the state trajectories (this time utilizing a state estimator and the input and output trajectories). The state trajectories are, in turn, utilized to resolve the system matrices using the input−output data. The process is repeated until convergence occurs between successive state trajectories, thus yielding state trajectories and "consistent" system matrices. The efficacy of the proposed approach is shown via simulations using a nonlinear process example.