Supervisory Control Algorithm Research Articles

Analysis of Battery Lifetime Extension in a Small-Scale Wind-Energy System Using Supercapacitors

Due to the variable characteristics of renewable generation, batteries used in renewable-power systems can undergo many irregular, partial charge/discharge cycles. In turn, this can also have a detrimental effect on battery lifetime and can increase project costs. This study presents a method of improving battery lifetime in a small-scale remote-area wind-power system by the use of a battery/supercapacitor hybrid energy storage system. The supervisory control algorithm and hardware implementation are described and projected long-term benefits of the proposed system are assessed by simulation. A representative dynamic model of the overall system, incorporating realistic wind-speed and load-power variations has been developed. An analysis is presented of the potential improvement in battery lifetime that is achievable by diverting short-term charge/discharge cycles to a supercapacitor energy-storage system. This study introduces a method by which supercapacitor energy storage systems and control algorithms can be evaluated and implemented in the application area considered. The composition of a prototype test system is described and experimental results are presented to demonstrate system feasibility.

Using Formal Methods to Improve Home-Use Medical Device Safety

Using Formal Methods to Improve Home-Use Medical Device Safety

DC-Link Based Micro-grid System

Design and evaluation of dynamic model of multi-input/multi-output power converter consisting of six converters connected by dc-link will be presented in the paper. The model was created in Matlab/Simulink based on the existing system, with the goal of evaluating various power management strategies, where only transient behavior in case of changed operating regime is of interest. In future the model will be used as a Software-In-the-Loop development tool for the design of supervisory control algorithms.

Synergization of air handling units for high energy efficiency in office buildings: Implementation methodology and performance evaluation

An integrating air-handling unit (IAHU) control theory has been proposed to improve the energy efficiency in office buildings by utilizing the regional and operation differences among multiple AHUs. Unlike the conventional AHU operation, where the units are controlled as independent systems without interaction, IAHU coordinates the AHUs based on the dynamic outside air conditions and system operation modes to achieve synergized energy performance and maintain the indoor air quality. The synergization strategy allows the outside air intake and the airflows to be orderly re-allocated among the AHUs when conditions are appropriate. This paper presents the implementation methodology and performance evaluation of IAHU in an open-plan office building with multiple AHUs. The allocation of airflows among the AHUs is described first to illustrate how IAHU deals with multiple AHUs in a building. The supervisory level control algorithm is then detailed and easy-to-follow flowcharts are provided based on the decision-making schema. A two-step hourly evaluation method and the energy simulation model are introduced. An office building with multiple AHUs is selected to assess the performance of IAHU. The study concludes that the innovative IAHU with the easy-to-implement strategy can be readily implemented to achieve high energy efficiency in open space office buildings.

Thermostatic Control for Series Hydraulic Hybrid Vehicle (SHHV) Energy Management

The main contributions of this paper are the development of forward-facing model of a series hydraulic hybrid vehicle (SHHV) power-train for medium size trucks, of which the fundamental architecture is described, together with dynamic equations and basic features of subsystem modules. A thermostatic SoC supervisory power management control algorithm is assessed, with the expectation that series configuration would maximize the fuel economy as engine is decoupled from the wheels. Simulation results over the urban driving cycle represent a significant departure from the conventional wisdom of operating the engine near its sweet spot and indicate what is preferred from the system stand-point, and also demonstrate the potential of the selected hybrid system to substantially improve vehicle fuel economy.

Methodology of designing adaptive supervisory control system for railway freight terminals

The paper discusses the methodology of designing an adaptive supervisory control system for railway freight terminals which are the sources of origination and dying of freight traffic. The setting of the mentioned problem of adaptive control and solution to it with the application of mathematical tools of automatic control and neural networks are presented; and the fundamental supervisory control algorithm as a set of sequentially operated blocks, each of them executing one main task, is also described. The proposed methodology can be applied to management of operation of freight terminals located both at the stations of JSCo "Russian Railways" and logistics centers and stations belonged to other railway infrastructure owners.

Explicit Storage and Analysis of Billions of States using Commodity Computers

The objective of this paper is to develop a framework and associated algorithms for explicit state space exploration of discrete event systems that can scale to very large state spaces. We consider classes of resource allocation systems (RAS), where a set of resources are shared by concurrent processes. In particular, we focus on Gadara RAS, whose Petri net representations have recently been used for liveness enforcement in multithreaded software. We present a framework where each reachable state of the RAS is represented by a single bit. We show how single-bit representations can lead to efficient implementations of supervisory control algorithms. In order to support single-bit state representations, we develop two indexing functions that map each state to a unique integer that serves as the corresponding index of the state in the large bit array. These functions exploit the invariants of the given RAS. Experimental results show that our techniques scale up to exploration and analysis of billions of states on commodity computers.

Robust adaptive control using multiple models, switching and tuning

Fil: Giovanini, Leonardo Luis. Consejo Nacional de Investigaciones Cientificas y Tecnicas; Argentina. Universidad Nacional del Litoral. Facultad de Ingenieria y Ciencias Hidricas; Argentina

Supervisory Control of Parallel Hybrid Electric Vehicles for Fuel and Emission Reduction

Past research on hybrid electric vehicles (HEVs) focused primarily on improving their fuel economy. Emission reduction is another important performance attribute that needs to be addressed. When emissions are considered for hybrid vehicles with a gasoline engine, horizon-based optimization methodologies should be used because the light-off of the three-way catalytic converter heavily depends on the warming-up of catalyst temperature. In this paper, we propose a systematic design method for a cold-start supervisory control algorithm based on the dynamic programming (DP) methodology. First, a system-level parallel HEV model is developed to efficiently predict tailpipe emissions as well as fuel economy. The optimal control problem for minimization of cold-start emissions and fuel consumption is then solved via DP. Since DP solution cannot be directly implemented as a real-time controller, more useful control strategies are extracted from DP solutions over the entire state space via the comprehensive extraction method. The extracted DP results indicate that the engine on/off, gear-shift, and power-split strategies must be properly adjusted to achieve fast catalyst warm-up and low cold-start tailpipe emissions. Based on DP results, we proposed a rule-based control algorithm that is easy to implement and adjust while achieving near-optimal fuel economy and emission performance.

Supervisory Control of Dynamical Systems With Uncertain Time Delays

A study of controlling dynamical systems with uncertain and varying time delays is presented in this paper. The uncertain time delay is assumed to fall in a range with known upper and lower bounds. We apply the supervisory control algorithm to deal with uncertainties in the time delay. An index is defined for each of the predetermined controls for a discrete set of time delays sampled from the range. Based on this index, a hysteretic switching rule selects a control from the predetermined controls with optimal feedback gains. Each predetermined control must be stable for any time delay in the range. Two control design methods are discussed, namely, the mapping method and a higher order approach. Examples of linear systems are used to demonstrate the theoretical work.

On-line identification of language measure parameters for discrete-event supervisory control

The discrete-event dynamic behavior of physical plants is often represented by regular languages that can be realized as deterministic finite state automata (DFSA). The concept and construction of signed real measures of regular languages have been recently reported in literature. Major applications of the language measure are: quantitative evaluation of the discrete-event dynamic behavior of unsupervised and supervised plants; and analysis and synthesis of optimal supervisory control algorithms in the discrete-event setting. This paper formulates and experimentally validates an on-line procedure for identification of the language measure parameters based on a DFSA model of the physical plant. The recursive algorithm of this identification procedure relies on observed simulation and/or experimental data. Efficacy of the parameter identification procedure is demonstrated on the test bed of a mobile robotic system, whose dynamic behavior is modelled as a DFSA for discrete-event supervisory control.

A controller synthesis algorithm for building self-adaptive software

A novel approach for building self-adaptive software based on a controller synthesis algorithm is presented. Self-adaptive software is a relatively new idea aiming at producing applications that can readily adapt in the face of changing user needs, desires and environment. Self-adaptive software has multiple ways of accomplishing its purpose, enough knowledge of its construction and is capable of changing behaviour when it does not accomplish its goal or when better functionality or performance is possible. The presented approach for building self-adaptive software uses ontological models of software components, which represent the environment, the composition, the required behaviour, and the possible configurations for the self-adaptive software. Self-adaptation is based on a supervisory control algorithm that reconfigures and controls software components in order to achieve their required behaviour.

Supervisory control of dynamic voltage restorers

The impact of system issues on the performance and power rating of dynamics voltage restorers is examined. Algorithms to restore a network voltage sag or swell to given set points have been the subject of significant research in the past. The supervisory control scheme is examined to determine these set points. The control scheme's effect on device power rating (cost) and its interaction with active loads are investigated using analytical methods, simulation and experimental tests. The influence of the choice of the supervisory control algorithm on the selection of protection scheme and the interaction between the protection scheme and the device power rating are also examined.

Implementation procedure of an advanced supervisory and control strategy in the pharmaceutical industry

This paper describes the different steps followed in implementing a temperature controller and a supervisory controller in a Supervisory Control And Data Acquisition (SCADA) system to control a 60 and 160 l reactor in a pharmaceutical factory. These reactors are used to produce the initial quantities of drugs needed to perform clinical tests. The chemical reactions involved are often changed (several times a week). The different steps of the project will be presented: • design of a new heating–cooling system, • design of the predictive and Supervisory Control Algorithm (SCA), • connection of the SCA to the SCADA system, • experimental validation according to Good Manufacturing Practice (GMP). This SCA has been in daily use as the standard control system for more than 2 years.

Supervisory field‐oriented control of induction motors with uncertain rotor resistance

It is well known that the performance of the (industry standard) field-oriented control (FOC) for induction motors is highly sensitive to uncertainties in the rotor resistance. In this paper we describe how to use supervisory control to obtain an adaptive implementation of FOC for current-fed machines. The unknown rotor resistance is assumed to belong to a discrete set, while the uncertain load torque ranges in a given compact set. Even though no restrictions are a priori imposed on the size of these sets, their definitions reflect the prior knowledge of the designer, which is effectively incorporated in the supervisory control algorithm. The supervisor selects from these sets values for the parameters to be applied to the FOC, a choice that is made by continuously comparing suitably defined performance signals. We prove that the proposed supervisor achieves global stabilization of the system when the load torque is known to belong to a given finite set of values. Apparently, this is the first globally convergent adaptive algorithm for current-fed machines which simply adds adaptation to the widely popular FOC and is not a radically new complicated controller, hence it is more likely to be adopted by practitioners. Some simulation results illustrate the properties of the algorithm. Copyright © 2001 John Wiley & Sons, Ltd.

Hierarchical adaptive and supervisory control of continuous venovenous hemofiltration

Continuous venovenous hemofiltration (CVVH) is a lifesaving renal replacement therapy used in clinical intensive care settings. Patients undergoing a life threatening illness often develop kidney failure. CVVH performs the blood filtering process while the kidneys recover. Hemofiltration devices which are currently available operate through open-loop fluid flows induced by peristaltic pumps, and possess precisions of /spl plusmn/30 ml of fluid removed per hour. This precision is inappropriate for a neonate, who can have a total blood volume of only 150 ml, and a 15 ml blood volume reduction could cause hypovolemic shock. Because of the patient risk involved in using such equipment, most medical centers measure the fluid removed from the patient on an hourly basis. This measurement step leads to a significant increase in nursing care and therefore increases the cost of the therapy. Presented in the paper is an intelligent CVVH system that deals with accuracy and cost issues while improving the care delivered by timely responding to changes in a patient's cardiovascular state. The intelligence aspect of the system is achieved through a hierarchical control architecture for CVVH. The control methodology uses a direct adaptive control scheme for the peristaltic pumps and a supervisory control algorithm for high-level decisions on the safe operation of the system. Adaptive control of the pumps results in improved accuracy of fluid flow, while the supervisory controller provides greater autonomy which reduces the burden on clinical personnel. The performance of the proposed hierarchical controller is illustrated by experiments on a hemofiltration machine using a simulated patient. These experimental simulations showed that the measured cumulative ultrafiltration after one hour of simulation was only 1.7 ml different from the target value, which is well within the acceptable medical range.

Supervisory field‐oriented control of induction motors with uncertain rotor resistance

AbstractIt is well known that the performance of the (industry standard) field‐oriented control (FOC) for induction motors is highly sensitive to uncertainties in the rotor resistance. In this paper we describe how to use supervisory control to obtain an adaptive implementation of FOC for current‐fed machines. The unknown rotor resistance is assumed to belong to a discrete set, while the uncertain load torque ranges in a given compact set. Even though no restrictions are a priori imposed on the size of these sets, their definitions reflect the prior knowledge of the designer, which is effectively incorporated in the supervisory control algorithm. The supervisor selects from these sets values for the parameters to be applied to the FOC, a choice that is made by continuously comparing suitably defined performance signals. We prove that the proposed supervisor achieves global stabilization of the system when the load torque is known to belong to a given finite set of values. Apparently, this is the first globally convergent adaptive algorithm for current‐fed machines which simply adds adaptation to the widely popular FOC and is not a radically new complicated controller, hence it is more likely to be adopted by practitioners. Some simulation results illustrate the properties of the algorithm. Copyright © 2001 John Wiley & Sons, Ltd.

New process control strategy used in a rapamycin fermentation

A high antibiotic expression level was obtained in a pilot-scale fermentation process with Streptomyces. With a new supervisory computer control algorithm, the agitation, aeration rate, vessel pressure, and culture temperature were automatically changed sequentially during the process, avoiding the dissolved oxygen (DO) limitation. A unique pH control strategy was also used in the process, and cells grew and produced the secondary metabolite product, rapamycin, over a wider pH range. The quantity of acid addition was very small when the pH reached the higher limit, 6.8, and then the pH was dropped and adjusted gradually by the culture itself towards the lower limit, 6.2. Due to the optimized culture environment the entire fermentation time was reduced, and the productivity was simultaneously increased. The harvested dry cell weight was over 25 g/l and intracellular rapamycin was over 110 mg/l.

CAE tools for quasi-static modeling and optimization of hybrid powertrains

Hybrid vehicles offer larger flexibility than conventional powertrains and, therefore, opportunities for improved fuel economy, but they need systematic design and optimization procedures to realize that potential. Especially choosing the best system structure, parametrization, and supervisory control algorithms is not trivial. This paper presents a tool which supports these tasks and which is based on a somewhat unusual system description. With this approach, fast simulations over entire test cycles are achieved on standard computer platforms. To demonstrate the benefits of the proposed tool, three case studies are shown, one including experimental data.

Intelligent control of via formation by photosensitive BCB for MCM-L/D applications

Via formation is a critical process sequence in multichip module (MCM) manufacturing, as it greatly impacts yield, density, and reliability. To achieve low-cost manufacturing, modeling, optimization, and control of via formation are crucial. In this paper, a model-based supervisory control algorithm is developed and applied to reduce undesirable behavior resulting from various process disturbances. A series of designed experiments are used to characterize the via formation workcell (which consists of the spin coat, soft bake, expose, develop, cure, and plasma descum unit process steps). The output characteristics considered are film thickness, uniformity, film retention, and via yield. Sequential neural network process models are used for system identification, and hybrid genetic algorithms are applied to synthesize process recipes. Computer simulation results show excellent control of output response shift and drift, resulting in a reduction of process variation. The performance limits of the supervisory control system are investigated based on these simulation results. The control algorithm is verified experimentally, and the results show 82.6, 64.4, and 17.3% improvements in maintaining target via yield, film thickness, and film uniformity, respectively, as compared to open loop operation.