Daniel E. Rivera

Health behavior models in the age of mobile interventions: are our theories up to the task?

Mobile technologies are being used to deliver health behavior interventions. The study aims to determine how health behavior theories are applied to mobile interventions. This is a review of the theoretical basis and interactivity of mobile health behavior interventions. Many of the mobile health behavior interventions reviewed were predominately one way (i.e., mostly data input or informational output), but some have leveraged mobile technologies to provide just-in-time, interactive, and adaptive interventions. Most smoking and weight loss studies reported a theoretical basis for the mobile intervention, but most of the adherence and disease management studies did not. Mobile health behavior intervention development could benefit from greater application of health behavior theories. Current theories, however, appear inadequate to inform mobile intervention development as these interventions become more interactive and adaptive. Dynamic feedback system theories of health behavior can be developed utilizing longitudinal data from mobile devices and control systems engineering models.

Simulation-based optimization of process control policies for inventory management in supply chains

A simulation-based optimization framework involving simultaneous perturbation stochastic approximation (SPSA) is presented as a means for optimally specifying parameters of internal model control (IMC) and model predictive control (MPC)-based decision policies for inventory management in supply chains under conditions involving supply and demand uncertainty. The effective use of the SPSA technique serves to enhance the performance and functionality of this class of decision algorithms and is illustrated with case studies involving the simultaneous optimization of controller tuning parameters and safety stock levels for supply chain networks inspired from semiconductor manufacturing. The results of the case studies demonstrate that safety stock levels can be significantly reduced and financial benefits achieved while maintaining satisfactory operating performance in the supply chain.

A MODEL PREDICTIVE CONTROL FRAMEWORK FOR ROBUST MANAGEMENT OF MULTI-PRODUCT, MULTI-ECHELON DEMAND NETWORKS

Abstract: Model Predictive Control (MPC) is presented as a robust, flexible decision framework for dynamically managing inventories and meeting customer demand in demand networks (a.k.a. supply chains). Ultimately, required safety stock levels in demand networks can be significantly reduced as a result of the performance demonstrated by the MPC approach. The translation of available information in the supply chain problem into MPC variables is demonstrated with a two-node supply chain example. A six-node, two-product, three-echelon demand network problem proposed by Intel is well managed by a partially decentralized MPC implementation under simultaneous demand forecast inaccuracies and plant-model mismatch.

Control-relevant prefiltering: a systematic design approach and case study

The authors examine the use of control-relevant prefiltering applied to parameter estimation using prediction-error methods. The prefiltering step ensures that the estimated model retains those plant characteristics that are most significant with regards to the users control requirements. They describe how to systematically build the prefilter in terms of the estimated model structure, the desired closed-loop speed-of-response, and the setpoint/disturbance characteristics of the control problem. Two implementation algorithms are presented which are applied to the plant data obtained from a distillation column. The results show that substantial improvements are obtained from control-relevant prefiltering in output error and partial least-squares estimation, while some caution must be exercised when applied to FIR and low-order ARX estimation. >

A hierarchical approach to production control of reentrant semiconductor manufacturing lines

A three-layer hierarchical approach for inventory control and production optimization of semiconductor reentrant manufacturing lines is developed. At the top layer, the parameters of an aggregated model are obtained online while, at the intermediate layer, production optimization and inventory control via model predictive control are performed. The aim of these two layers is aggregated (or averaged) supervisory control. The bottom layer consists of a distributed control policy which issues discrete-event decisions to track the aggregated targets issued by the optimizer. This layer accomplishes shop-floor control. The algorithm is applied to a discrete-event manufacturing line problem developed by Intel Corporation, which captures the main challenges posed by reentrant manufacturing lines.

Model Predictive Control for Tactical Decision-Making in Semiconductor Manufacturing Supply Chain Management

Supply chain management (SCM) in semiconductor manufacturing poses significant challenges that arise from the presence of long throughput times, unique constraints, and stochasticity in throughput time, yield, and customer demand. To address these concerns, a model predictive control (MPC) algorithm is developed which relies on a control-oriented formulation to generate daily decisions on starts of factories. A multiple-degree-of-freedom observer formulated for ease of tuning is implemented to achieve robustness and performance in the presence of nonlinearity and stochasticity in both supply and demand. The control algorithm is configured to meet the requirements of meeting customer demand (both forecasted and unforecasted), and track inventory and starts targets provided by higher level decision policies. Unique features of semiconductor manufacturing, such as capacity limits, packaging, and product reconfiguration, are formally addressed by imposing different constraints related to starts and inventories. This functionality contrasts that of standard approaches to MPC and makes this controller suitable as a tactical decision tool for semiconductor manufacturing and similar forms of high-volume discrete-parts manufacturing problems. Two representative case studies are examined under diverse realistic conditions with this flexible formulation of MPC. It is demonstrated that system robustness, performance, and high levels of customer service are achieved with proper tuning of the filter gains and weights, as well as the presence of adequate capacity in the supply chain.

Control-relevant model reduction problems for SISO H2, H∞, and μ-controller synthesis

Abstract The problem of model reduction in the context of control system design is investigated. Starting from closed-loop objectives (H2,H∞, and μ), equivalent weighted open-loop plant and controller reduction problems are developed. The control-relevant weight function incorporates explicitly all the important characteristics of the control problem, such as the setpoint/disturbance spectrum and the designer requirements for the sensitivity/complementary sensitivity functions, Furthermore, these control-relevant reduction problems are complemented with validation procedures that indicate rigorously the effects of the reduction problem on the desired performance objectives. A simple algorithm that uses standard regression routines is presented to solve these problems.

A dynamical model for describing behavioural interventions for weight loss and body composition change

We present a dynamical model incorporating both physiological and psychological factors that predict changes in body mass and composition during the course of a behavioural intervention for weight loss. The model consists of a three-compartment energy balance integrated with a mechanistic psychological model inspired by the Theory of Planned Behaviour. This describes how important variables in a behavioural intervention can influence healthy eating habits and increased physical activity over time. The novelty of the approach lies in representing the behavioural intervention as a dynamical system and the integration of the psychological and energy balance models. Two simulation scenarios are presented that illustrate how the model can improve the understanding of how changes in intervention components and participant differences affect outcomes. Consequently, the model can be used to inform behavioural scientists in the design of optimized interventions for weight loss and body composition change.

An integrated identification and control design methodology for multivariable process system applications

We present a way to take advantage of the favorable asymptotic properties of ARX estimators to develop an integrated methodology for identification and controller design for multivariable process plants. This method relies on well-established numerical tools and builds on an engineers existing process and statistical intuition. Specifically, the ARX estimate serves as a suitable intermediate model for the design and analysis of MIMO process control systems. Guidelines for the design of pseudo-random binary sequence signals that take advantage of the engineers prior knowledge of the process time constants are presented. Control-relevant model reduction is performed on elements of the ARX model to obtain low-order models conforming to the IMC-PID tuning rules. A simple analysis technique is used to assess stability of the decentralized and decoupled strategies. These techniques and full multivariable control are applied to the Shell heavy oil fractionator problem and the Weischedel-McAvoy distillation column model, respectively.

Application of minimum crest factor multisinusoidal signals for “plant-friendly” identification of nonlinear process systems☆

Abstract Guidelines for specifying the design parameters of minimum crest factor multisine signals generated per the approach of Guillaume et al. are presented. These guidelines are evaluated in the identification and control of nonlinear process systems. The minimum crest factor multisine signals offer some distinct advantages over both Schroeder phased multisine signals and multi-level Pseudo-Random Sequences (multi-level PRS) with respect to “plant-friendliness” considerations. These signals can be used to reduce the effects of nonlinearity in obtaining an Empirical Transfer Function Estimate (ETFE). As an example, the ETFE of a Rapid Thermal Processing (RTP) reactor simulation is presented. The effectiveness of the minimum crest factor multisine signals is also discussed and illustrated in the identification and control of a simulated continuous stirred tank reactor using “Model-on-Demand” estimation and Model Predictive Control. Since the performance of the “Model-on-Demand” estimator is highly dependent upon the quality of the identification data, the CSTR case study provides a compelling example of the usefulness of the proposed design procedure.