Control Decision Rules Research Articles

Multi-stage linear decision rules for stochastic control of natural gas networks with linepack

The disturbances from variable and uncertain renewable generation propagate from power systems to natural gas networks, causing gas network operators to adjust gas supply nominations to ensure operational security. To alleviate expensive supply adjustments, we develop control policies to leverage instead the flexibility of linepack – the gas stored in pipelines – to balance stochastic gas extractions. These policies are based on multi-stage linear decision rules optimized on a finite discrete horizon to guide controllable network components, such as compressors and valves, towards feasible operations. Our approach offers several control applications. First, it treats the linepack as a main source of flexibility to balance disturbances from power systems without substantial impacts on nominal gas supply. Second, these policies can be optimized to minimize the variability (due to intermittency of renewables) and variance (due to their uncertainty) of network state variables, such as pressures. Finally, it enables topology optimization to decouple network parts and prevent uncertainty propagation through the network. This is demonstrated using illustrative numerical experiments.

Decision-based virtual metrology for advanced process control to empower smart production and an empirical study for semiconductor manufacturing

Virtual metrology (VM) has been employed to improve the performance of advanced process control for semiconductor manufacturing. A number of VM models have been proposed to predict the quality characteristics for the wafers that have not been sampled and measured. However, little research has been done to address the interrelations between the VM model and associated decisions for advanced process control and yield enhancement. There is a research need for developing a framework that can integrate the confidence level of VM prediction and domain knowledge to derive appropriate decisions for real-time control. To fill the gaps, this study aims to develop a decision-based virtual metrology framework that integrates clustering and regression models to enhance the prediction and ensure the decision quality for the R2R controller. In particular, Isolation Forest is employed to cluster the data group for multi-recipes and multi-tools. Random Forest Regression is developed for the prediction model for each category respectively to enhance the accuracy of predicted results. Furthermore, this approach designs an overall confidence score based on data integrity and predicted results to suggest the optimal decision rules for R2R control in real time. This approach is validated with an empirical study in a leading semiconductor manufacturing company in Taiwan. Indeed, the results have demonstrated practical viability and the developed solution has been implemented.

Segregated Linear Decision Rules for Inverter Watt-VAr Control

The modern photovoltaic inverter has an active power-reactive power (Watt-VAr) control curve that is part of its management modes. The Watt-VAr curve, whose settings can be remotely modified, regulates the inverter reactive power output in function of its active power. This paper proposes the optimization of inverter segregated linear decision rules (LDRs), starting from the legacy Volt-VAr optimization in distribution networks. As opposed to classical LDRs that are interfaced with Volt-VAr optimization, the segregated LDRs are piecewise linear continuous curves that directly satisfy the reactive power capability of inverters, and their multiple segments can capture the correlation in solar power variability. The paper presents linear programming formulations for optimizing the segregated linear decision rules using stochastic optimization, robust optimization, and distributionally robust optimization. The optimization schemes are demonstrated on both meshed and radial networks, and their performance is validated via Monte-Carlo simulation.

A Two-Stage Chance Constrained Volt/Var Control Scheme for Active Distribution Networks With Nodal Power Uncertainties

Volt/var control (VVC) is one of the primary functions of the distribution management system aiming at optimum operation of power distribution networks while respecting all of their operational and security constraints. However, the recent huge integration of highly stochastic distributed generation (DG) sources with the grid presents a significant challenge to the traditional VVC schemes, which assume the future to be perfectly known. This paper presents a two-stage chance constrained optimization scheme to handle these nodal power uncertainties and guarantees that the operational and security constraints are respected for almost all realizations of the uncertainty. The chance constrained model is solved by collecting enough randomly chosen samples from the probability spaces of the uncertain parameters so that the class of the problem, a mixed integer second-order cone program, is not elevated. The algorithm not only dispatches the optimum schedule for discrete controlling devices like transformers and shunt capacitors but also optimizes the predefined decision rules for reactive power control of DG sources, thus falling in line with the requirement laid down in the revised IEEE 1547 Standard. Numerical simulations on three different test systems show the superiority of the proposed algorithm over the traditional deterministic methods.

Linear Decision Rules for Control of Reactive Power by Distributed Photovoltaic Generators

The revised IEEE 1547 Standard together with the grid codes from several European countries require the contribution of photovoltaic inverters to the reactive power and voltage control of distribution networks. This paper presents a decentralized approach for controlling reactive power from a photovoltaic (PV) inverter through a linear decision rule that is in terms of the PV generated real power, conforming to standard characteristic curves in the German grid code. The linear decision rules are computed using an affinely adjustable robust counterpart of an optimal power flow type formulation, with the PV real power generation specified in an uncertainty interval. The robust solution, which is computed via convex quadratic programming, guarantees that both the voltage deviations and power loss will remain within their optimized bounds for all realizations of PV real power sampled from the uncertainty set. The proposed linear decision rules are compared with a local control method that seeks the same type of multiobjective optimization. Monte Carlo simulation results on meshed distribution networks with up to 830 nodes show the superiority of the proposed approach.

Admission control scheme for handover service in high-speed train communication system

Admission control in high-speed train communication system is quite different from admission control in traditional cellular networks. Conventional admission control strategies cannot be directly applied to this special communication scenario. In this paper, the problem of admission control for handover service is investigated in high-speed train communication environment. An admission control scheme considering bit error rate (BER) and bandwidth borrowing strategy is proposed. On the basis of admission control decision rule taking BER into account, a part of bandwidth obtained by compressing variable rate service in the networks is provided for handover services. The admission control scheme can admit handover services as more as possible while it guarantees the lowest data rate of different services in the networks. Simulation results show that the proposed admission control scheme has a better performance than existing admission control schemes.

Marking and retention of harlequin bug, Murgantia histrionica (Hahn) (Hemiptera: Pentatomidae), on pheromone-baited and unbaited plants

Harlequin bug (Murgantia histrionica )i s an important pest of cole crops in the USA. The adults and nymphs feed on aboveground plant tissues by sucking cell contents and can seriously damage the host. Current insect control measures on cole crops target mainly lepidopteran pests, and the insecticides generally used do not control harlequin bug, so alternative management practices need to be explored. Previous research has established the existence of a male-produced pheromone attractive to both sexes and nymphs of M. histrionica. In this work, two systems of marking bugs were tested to verify if the mark affected fitness traits such as survival and host location. In a second phase, marked individuals were placed on trap host plants baited with synthetic pheromone lures to test whether mi- gration rates were related to M. histrionica density on the trap plants and the presence of the attractants. Neither marking system affected the survival or orientation of the subjects compared to unmarked individuals. The pher- omone lures significantly increased the attractiveness of the trap plants, but did not increase the retention time of the plants compared to unbaited plants. Emigration from the trap plants showed a constant rate and seemed unrelated to bug density on the plants. However, a mean peak density of ca. 36 bugs/plant was calculated. Beyond this number, density tended to decrease. These successful marking methods and retention time models support development of M. histrionica management with trap crops, by providing tentative control thresholds and decision rules.

Learning decision rules for energy efficient building control

While rule based control (RBC) is current practice in most building automation systems that issue discrete control signals, recent simulation studies suggest that advanced, optimization based control methods such as hybrid model predictive control (HMPC) can potentially outperform RBC in terms of energy efficiency and occupancy comfort. However, HMPC requires a more complex IT infrastructure and numerical optimization in the loop, which makes commissioning, operation of the building, and error handling significantly more involved than in the rule based setting. In this paper, we suggest an automated RBC synthesis procedure for binary decisions that extracts prevalent information from simulation data with HMPC controllers. The result is a set of simple decision rules that preserves much of the control performance of HMPC. The methods are based on standard machine learning algorithms, in particular support vector machines (SVMs) and adaptive boosting (AdaBoost). We consider also the ranking and selection of measurements which are used for a decision and show that this feature selection is useful in both complexity reduction and reduction of investment costs by pruning unnecessary sensors. The suggested methods are evaluated in simulation for six different case studies and shown to maintain the performance of HMPC despite a tremendous reduction in complexity.

Estimating economic thresholds for site-specific weed control using manual weed counts and sensor technology: An example based on three winter wheat trials

Precision experimental design uses the natural heterogeneity of agricultural fields and combines sensor technology with linear mixed models to estimate the effect of weeds, soil properties and herbicide on yield. These estimates can be used to derive economic thresholds. Three field trials are presented using the precision experimental design in winter wheat. Weed densities were determined by manual sampling and bi-spectral cameras, yield and soil properties were mapped. Galium aparine, other broad-leaved weeds and Alopecurus myosuroides reduced yield by 17.5, 1.2 and 12.4 kg ha(-1) plant(-1) m(2) in one trial. The determined thresholds for site-specific weed control with independently applied herbicides were 4, 48 and 12 plants m(-2), respectively. Spring drought reduced yield effects of weeds considerably in one trial, since water became yield limiting. A negative herbicide effect on the crop was negligible, except in one trial, in which the herbicide mixture tended to reduce yield by 0.6 t ha(-1). Bi-spectral cameras for weed counting were of limited use and still need improvement. Nevertheless, large weed patches were correctly identified. The current paper presents a new approach to conducting field trials and deriving decision rules for weed control in farmers' fields.

A voluntary harvest control rule for a New Zealand rock lobster (Jasus edwardsii) stock

This paper describes a study to develop and test a simple harvest control (decision) rule that could be used by the commercial rock lobster industry in the CRA 5 quota management area in New Zealand. Time contraints prevented a full stock assessment, so alternatively the history of annual production was estimated from catch and catch‐per‐unit‐of‐effort (CPUE) data, and two versions of an operating model were based on the results: one used a fitted relation between biomass and production, and the other used constant production. Production was simulated with autocorrelated stochastic variation, and CPUE was simulated with observation error. Using a base case and two sensitivity trials for each model gave six operating models. Six candidate harvest control rules were developed, and sets of 1000 simulations were made for each combination of rule and model. The rules were then compared on the basis of fishery and stock indicators. Although choosing a specific rule is difficult, having a voluntary harvest control rule that reduces catches when CPUE is low maintains a higher mean biomass and prevents low biomass episodes.

Oracle and Adaptive Compound Decision Rules for False Discovery Rate Control

We develop a compound decision theory framework for multiple-testing problems and derive an oracle rule based on the z values that minimizes the false nondiscovery rate (FNR) subject to a constraint on the false discovery rate (FDR). We show that many commonly used multiple-testing procedures, which are p value–based, are inefficient, and propose an adaptive procedure based on the z values. The z value–based adaptive procedure asymptotically attains the performance of the z value oracle procedure and is more efficient than the conventional p value–based methods. We investigate the numerical performance of the adaptive procedure using both simulated and real data. In particular, we demonstrate our method in an analysis of the microarray data from a human immunodeficiency virus study that involves testing a large number of hypotheses simultaneously.

Control Decision Rule for European Chafer (Coleoptera: Scarabaeidae) Larvae in Field Corn

After greenhouse and outdoor microplot experiments, a critical density of two third instars per microplot for third instars of European chafer, Rhizotrogus (Amphimallon) majalis (Razoumowsky), in corn, Zea mays L., was derived. On average, the number of missing or damaged plants increased approximately 8% from zero to two larvae per 900 cm2. Furthermore, 23 fields in 2 yr were sampled for larvae along transects by using a golf cup cutter as the sampling tool and the critical density of 0.2 larva per sampling unit as the critical density. The sampling unit was one golf cup cutter with a diameter of 10.8 cm or 91.4 cm2 (10 sampling units approximately 900 cm2 approximately 1 foot2). Fieldwide means and variation were modeled to Taylor's power law, a = 1.42 and b = 1.47, and 20 of 23 fields fit the negative binomial probability distribution. Wald's formula for a sequential sampling plan was most accurate and least time-consuming, according to the operating characteristic and the average sample number function, relative to Iwao's and converging lines formulae. Percentage of sand, topography, soil bulk density, and proximity to trees were measured as potential predictors of areas with high larval density. Percentage of sand and soil bulk density were significant predictors, and topography and proximity to trees were not significant predictors. Field areas where the percentage of sand is high and the soil bulk density is low to moderate or where the percentage of sand is moderate and the soil bulk density is low should be chosen as sampling locations.

Control Decision Rule for European Chafer (Coleoptera: Scarabaeidae) Larvae in Field Corn

After greenhouse and outdoor microplot experiments, a critical density of two third instars per microplot for third instars of European chafer, Rhizotrogus (Amphimallon) majalis (Razoumowsky), in corn, Zea mays L., was derived. On average, the number of missing or damaged plants increased approximately 8% from zero to two larvae per 900 cm2. Furthermore, 23 fields in 2 yr were sampled for larvae along transects by using a golf cup cutter as the sampling tool and the critical density of 0.2 larva per sampling unit as the critical density. The sampling unit was one golf cup cutter with a diameter of 10.8 cm or 91.4 cm2 (10 sampling units approximately 900 cm2 approximately 1 foot2). Fieldwide means and variation were modeled to Taylor's power law, a = 1.42 and b = 1.47, and 20 of 23 fields fit the negative binomial probability distribution. Wald's formula for a sequential sampling plan was most accurate and least time-consuming, according to the operating characteristic and the average sample number function, relative to Iwao's and converging lines formulae. Percentage of sand, topography, soil bulk density, and proximity to trees were measured as potential predictors of areas with high larval density. Percentage of sand and soil bulk density were significant predictors, and topography and proximity to trees were not significant predictors. Field areas where the percentage of sand is high and the soil bulk density is low to moderate or where the percentage of sand is moderate and the soil bulk density is low should be chosen as sampling locations.

Towards efficient decision rules for admission control based on the many sources asymptotics

This paper introduces new admission criteria that enable the use of algorithms based on the many sources asymptotics in real-life applications. This is achieved by a significant reduction in the computational requirements and by moving the computationally intensive tasks away from the timing-sensitive decision instant. It is shown that the traditional overflow probability type admission control method can be reformulated into a bandwidth requirement type and into a buffer requirement type method and that these methods are equivalent when used for admission control. The original and the two proposed methods are compared through the example of fractional Brownian motion (fBm) traffic.

Using Expert Systems for Simulation Modeling of Patient Scheduling

Modeling the scheduling of patient appointments is an important issue in simulating a health care delivery facility. A simulation model must in clude the control logic of appointment scheduling software and the explicit and implicit decision rules used by the human scheduler in selecting an appointment time. Expert systems provide one way of modeling such control logic and decision rules. We describe a structure for an expert sys tem that models patient appointment scheduling and the integration of such an expert system within a simulation model. An example expert system for a small animal veterinary clinic is presented.

Optimal weed control under static and dynamic decision rules

AbstractDynamic and static weed control decision rules are derived analytically and compared. The dynamic rule leads to increased farm profits and greater control of weeds and weed seeds than the static rule, while total herbicide use is unchanged. The magnitude of the differences is estimated for atrazine control of foxtail and cocklebur in corn production. Incorporating weed dynamics into weed control strategies increases farm profits between 1.0 and 1.4%.

Efficiency based decision rules for production planning and control

Holt et al.'s (1960) model (the HMMS model) was one of the earliest mathematical programming approaches to production planning and control. The model uses quadratic cost functions and the coefficients of the functions are determined by curve fitting techniques from data obtained on previous operation of the plant. Regression-based approaches fit a curve which represents the average trend prevailing in the observed data, whereas, using the technique of Data Envelopment Analysis, it is possible to identify a cost efficient frontier. Estimated coefficients based on such a cost efficient frontier represent the best performance prevailing in the observed data. The two approaches for estimating the coefficients of the cost functions and the corresponding production plans are compared using a numerical example.

Solving zero-one mixed integer programming problems using tabu search

We describe a tabu search (TS) approach for solving general zero-one mixed integer programming (MIP) problems that exploits the extreme point property of zero-one solutions. Specialized choice rules and aspiration criteria are identified for the problems, expressed as functions of integer infeasibility measures and objective function values. The first-level TS mechanisms are then extended with advanced level strategies and learning. We also look at probabilistic measures in this framework, and examine how the learning tool Target Analysis (TA) can be applied to identify better control structures and decision rules. Computational results are reported on a portfolio of multiconstraint knapsack problems. Our approach is designed to solve thoroughly general 0/1 MIP problems and thus contains no problem domain specific knowledge, yet it obtains solutions for the multiconstraint knapsack problem whose quality rivals, and in some cases surpasses, the best solutions obtained by special purpose methods that have been created to exploit the special structure of these problems.

Economic decision rules for postemergence herbicide control of barnyardgrass (Echinochloa crus-galli) in corn (Zea mays)

Economic decision rules for postemergence herbicide control of barnyardgrass in corn were developed. Damage and control functions that formed the basis of an economic model were estimated. Barnyardgrass density and time of emergence relative to the crop were fundamental to calculate the damage function. The control function described barnyardgrass dry weight response to variable doses of two herbicides. Both the biologist's and economist's weed control decision rules, derived from the economic model, were influenced by time of weed emergence relative to the crop, corn yield, and price. Inclusion of time of weed emergence relative to the crop improved our interpretative ability of derived decision rules. The biologist's threshold weed density was more sensitive to changes in parameter values than the economist's optimal herbicide dose strategy. Herbicide use with recommended label dose was greater than either the economically optimal or the biologically effective doses. Use of the biologically effective dose for postemergence weed control decisions was cost efficient and could be of practical significance to corn growers.

Decision Rules for Postemergence Control of Pigweed (Amaranthusspp.) in Soybean (Glycine max)

Weed control decision rules were derived for the application of postemergence herbicides to control pigweed species in soybean. Field experiments were conducted at two locations in 1992 and 1993 to evaluate soybean-pigweed interference. A damage function was determined that related yield loss to time of pigweed emergence, density, and soybean weed-free yield. A control function described pigweed species response to variable doses of imazethapyr and thifensulfuron. The integration of these two functions formed the basis of an economic model used to derive two weed control decision rules, the biologist's “threshold weed density” and the economist's “optimal dose.” Time of weed emergence had a more significant role than weed density in the economic model. Later-emerging pigweed caused less yield loss and therefore, decision rules lead to overuse of herbicides if emergence time is not considered. The selected herbicide dose influenced the outcome of the control function. Depending on the desired level of weed control, a herbicide could be chosen to either eradicate the escaped weed species (label or biologically-effective doses) or reduce the growth of the weed species and thereby offset interference (optimal dose). The development of a biologically-effective dose by weed species matrix was recommended. Decision rules should not be utilized as an exclusive weed management strategy but rather as a component of an integrated weed management program.