Set Of Control Rules Research Articles

Design of a Fuzzy Logic Controller for Water Level Control in an Advanced Boiling Water Reactor Based on Input-Output Data

A fuzzy logic controller (FLC) has been designed to control the water level in an advanced boiling water reactor (ABWR). The performance was comparable to that of a proportional-integral controller. However, the feedwater flow rate did not change smoothly to the steady state. Therefore, a method based on input-output data was adopted to prevent this problem. The data required for deriving the fuzzy rules were the results of various instances of satisfactory manual control of an ABWR simulation model. To construct the membership functions of the linguistic variable, the data were clustered using the fuzzy C-means method. The fuzzy rules were then generated from the data. Because the control actions were not guaranteed to be proper in all the cases and the data were not complete for all the possible operation conditions, the fuzzy rules were modified and extra rules were added based on human knowledge so that satisfactory performance can be achieved. Nevertheless, the method is helpful in deriving a set of important control rules at the beginning stage of design, especially when the importance of the linguistic variables is not clear. The simulation results showed that the designed controller followed the desired control action, which was imposed on the designed data, and the performance of the controller was better than the previously designed FLC.

A traffic priority language for collision-free navigation of autonomous mobile robots in dynamic environments

This paper presents a generic traffic priority language, called KYKLOFORTA, used by autonomous robots for collision-free navigation in a dynamic unknown or known navigation space. In a previous work by X. Grossmman (1988), a set of traffic control rules was developed for the navigation of the robots on the lines of a two-dimensional (2-D) grid and a control center coordinated and synchronized their movements. In this work, the robots are considered autonomous: they are moving anywhere and in any direction inside the free space, and there is no need of a central control to coordinate and synchronize them. The requirements for each robot are i) visual perception, ii) range sensors, and iii) the ability of each robot to detect other moving objects in the same free navigation space, define the other objects perceived size, their velocity and their directions. Based on these assumptions, a traffic priority language is needed for each robot, making it able to decide during the navigation and avoid possible collision with other moving objects. The traffic priority language proposed here is based on a set of primitive traffic priority alphabet and rules which compose pattern of corridors for the application of the traffic priority rules.

QC Validator, Version 2.0. James Westgard. Ogunquit, ME: Westgard Quality Corporation, 1996. $995.00. ISBN 0-886958-05-X. Available through AACC Customer Service (800-892-1400 or 202-857-0717).

QC Validator 2.0 (Westgard Quality Corp., Ogunquit, ME) is a Windows application that facilitates rational selection of quality-control (QC) rules. The user provides the performance characteristics of the analytical system (imprecision, inaccuracy, etc.) and the analytical or clinical goal (total allowable error). The program displays performance characteristics—in the form of OPSpecs charts, critical error graphs, and power function graphs—for various sets of QC rules. Optionally, the program also automatically chooses a set of control rules on the basis of user-defined selection criteria. Users should be aware that the performance characteristics reflect the particular manner in which the …

A fuzzy navigation system for mobile construction robots

Fuzzy navigation systems control a robot by implementing a fuzzy logic controller (FLC). Fuzzy navigation systems are simpler to implement than other navigation systems because they can handle infinite navigation situations with a finite set of rules. Existing fuzzy navigation systems for path finding in an unknown environment tend to find the shortest path in convex obstacle fields, but fail when obstacles are concave or placed continuously in certain configurations. This paper presents a fuzzy navigation system that can escape from concave and maze-like obstacle fields in an unknown environment. The system combines a tangent algorithm for path planning with sets of linguistic fuzzy control rules. In particular, we introduce the control rules for a Tracking mode of the FLC and improvements to the commonly used, intuitively reasonable tangent algorithm.

Automated Selection of Statistical Quality-Control Procedures to Assure Meeting Clinical or Analytical Quality Requirements

Efforts to totally automate laboratory testing processes must address the issue of how to assure the quality of the final test result. A recent survey by Tetrault and Steindel (1) reported that laboratories are using the same quality-control (QC) procedures today that they used 10 years ago. Actually, more than half of the laboratories indicated they are still using control limits set as the mean ± 2s (the 12s rule), a practice that dates to the 1950s and ’60s (2)(3), when statistical QC was first used with manual methods and the first generation of automated Technicon AutoAnalyzer systems. Other laboratories indicated they are using variations of the multirule type of QC procedure introduced in the early 1980s (4). Tetrault and Steindel (1) recommend that “the best set of control rules will vary from method to method and cannot be determined through simple algorithms or formulas. The laboratorian has to balance true error-detection capabilities against the probabilities of falsely rejecting a good run.” The information needed about the error-detection and false-rejection characteristics of stable QC procedures became available in the clinical chemistry literature almost 20 years ago (5)(6)(7)(8) and was extended by Cembrowski et al. (9)(10)(11)(12) to consider patient data algorithms. This information was incorporated into laboratory QC texts (13)(14) and clinical chemistry texts (15)(16) in the 1990s and continues to be expanded and improved by Parvin (17)(18)(19)(20) and by Smith and Kroft (21). Guidelines and strategies for selecting QC procedures were described in 1986 (13), and some detailed applications have been published to demonstrate the selection and design of QC procedures (22)(23)(24). QC selection grids were introduced in 1990 …

Fuzzy control of multi-rate traffic in multimedia cell-based networks

In this paper, an intelligent scheme for controlling the QoS in multimedia, multi?rate networks is presented. The scheme implements a dynamic control mechanism, based on buffers with vaguely defined thresholds represented with fuzzy sets. This approach guarantees the QoS of real?time traffic and delivers non?realtime traffic at acceptable performance levels. The fuzzy mechanism, based on the principles of fuzzy control, dynamically responds to the alternating traffic patterns at the access node using a set of fuzzy control rules. The fuzzy control rules are carefully formulated to reduce the starvation of non?realtime traffic which is usually assigned a low priority. We also show in this paper how the proposed schemes offer an improved performance over other access control mechanisms such as moving boundaries and other static priority strategies.

Statistical Applications in Process Control

An overview and perspective on control charting a rule-based approach to multiple statistical test analysis of binary data adaptive hierarchical Bayesian Kalman filtering with applications to quality control a very simple set of process control rules the OCAP - predetermined responses to out-of-control conditions economic control charts - relating the model and finding the optimal design optimization and sensitivity analysis with an economic model control chart model using the

Design of stability-guaranteed neurofuzzy logic controller for nuclear steam generators

A neurofuzzy logic controller (NFLC), which is implemented by using a multilayer neural network with special types of fuzzifier, inference engine and defuzzifier, is applied to the water level control of a nuclear steam generator (SG). This type of NFLC has the structural advantage that arbitrary two-input, single-output linear controllers can be adequately mapped into a set of specific control rules of the NFLC. In order to design a stability-guaranteed NFLC, the stable sector of the given linear gain is obtained from Lyapunov's stability criteria. Then this sector is mapped into two linear rule tables that are used as the limits of NFLC control rules. The automatic generation of NFLC rule tables is accomplished by using the back-error-propagation (BEP) algorithm. There are two separate paths for the error back propagation in the SG. One considers the level dynamics depending on the tank capacity and the other takes into account the reverse dynamics of the SG. The amounts of error back propagated through these paths show opposite effects in the BEP algorithm from each other for the swell-shrink phenomenon. Through computer simulation it is found that the BEP algorithm adequately generates NFLC rule tables according to given learning parameters.

Pressure control of a Moscow water supply system using expert system technology

Automating the pressure control of typical Russian types of water distribution systems can result in greater than average benefits. The potential exists for improving the system efficiency and reliability while reducing the on-line operational manning requirements. To this end the paper presents modelling and simulation studies of the current semi-manual and the proposed fully automatic pressure control schemes for the Zelenograd water supply system. Dynamic operational simulations are an essential feature of the project. However, it was necessary to enhance the network hydraulic simulator, to include pressure feedback controls, in order to produce acceptable results. Analyses of the operational requirements and the simulation results provide a basis for the design of an improved controller using an expert system approach. A major part of the paper covers the methods in use for obtaining an adequate set of control rules for the Zelenograd system. Extended time period operational simulations are used to verify the rules and to compare the results with those of existing practice. There are expectations of immediate benefits in terms of operating economies. Typically these will arise from reductions in water leakage, electricity charges and control manpower. Work is now proceeding on actual implementation using a combination of expert system and fuzzy logic methods.

On Markovian Fragments of COCOLOG for Logic Control Systems

The COCOLOG (Conditional Observer and Controller Logic) system is a partially ordered family of first-order logical theories expressed in the typed first-order languages $\{ \mathcal{L}_k ;k \geq 0\} $ describing the controlled evolution of the state of a given partially observed finite machine $\mathcal{M}$. The initial theory of the system, denoted $Th_0 $, gives the theory of $\mathcal{M}$ with no data being given on the initial state. Later theories, $\{ Th(o_1^k );k \geq 1\} $, depend upon the (partially ordered lists of) observed input-output trajectories $\{ o_1^k ;k \geq 1\} $, where new data are accepted in the form of the new axioms $AXM^{obs} (\mathcal{L}_k )$, $k \geq 1$. A feedback control input $U(k)$ is determined by the solution of a collection of control problems posed in the form of a set of conditional control rules $CCR(\mathcal{L}_k )$, such a set being paired with the theory $Th(o_1^k )$ for each $k \geq 1$. In this paper, we introduce a restricted version of COCOLOG, called a system of Markovian fragments of COCOLOG, in which a smaller amount of information is communicated from one theory to the next. Such fragment theories are associated with a restricted set of candidate control problems, denoted $CCR(\mathcal{L}_k^m )$, $k \geq 1$. It is shown that a Markovian fragment theory $MTh(o_1^k )$ contains a large subset of $Th(o_1^k )$, which includes, in particular, the state estimation theorems of the corresponding full COCOLOG system and, for the set of control rules $CCR(\mathcal{L}_k^m )$, has what is termed the same control reasoning power. Next, it is shown that proofs of theorems in the fragment systems are necessarily shorter than their proofs in the full COCOLOG systems. Finally some computer-generated examples are given, illustrating this increased theorem-proving efficiency.

A GA paradigm for learning fuzzy rules

In this paper, we describe a paradigm for learning fuzzy rules using genetic algorithms (GA). We formulate our problem of learning as follows: given a set of linguistic values that characterize the input and output state variables of the system in consideration, derive an n-rule fuzzy control algorithm. The value n represents a specified constraint of the GA in searching for a functional ruleset. The GA learning paradigm is powerful since it requires no prior knowledge about the system's behavior in order to formulate a set of functional control rules through adaptive learning. We present our simulation results using the classical inverted pendulum control problem to demonstrate the effectiveness of the GA learning scheme. Results have shown that the approach has great potential as a tool for the learning of fuzzy control rules, particularly in situations where the knowledge from a human expert is not easily accessible.

Robust, near time-optimal control of power system oscillations with fuzzy logic

This paper presents a fuzzy logic controller for series reactance switching to damp power system electromechanical oscillations. A set of control rules are constructed and inference is provided by fuzzy logic reasoning. The knowledge base for the controller is established from observation of the dynamical behaviour of a simple power system and the general engineering knowledge about the system dynamics. The performance of the controller is shown to be robust and comparable to that of a minimum-time optimal controller.

Predictive fuzzy control of an autonomous mobile robot with forecast learning function

This paper deals with the drive control of an autonomous mobile robot. An autonomous mobile robot is one of the intelligent robots that need abilities to recognize and to adapt to surrounding environment. We propose a new approach to meeting these needs. This approach is based on a forecast learning fuzzy control. The environment can be classified into several characteristic patterns and our robot has sets of control rules for each pattern beforehand. The robot integrates these sets into a single set using degrees of matching between the current environment and each pattern. The robot forecasts whether it will drive safely or not by prediction, by using the integrated control rules. The robot considers the results of the forecast, and then adjusts the conclusion parts of the integrated control rules in order to drive more safely in such an environment. In this paper, to find the efficacy of our new approach, the simulation results of the drive control of the robot and the experimental results on indoor routes are shown.

Decision-Theoretic Derivation of Rule Structures for Knowledge-Based Control and Its Application to Coordinated Control of Sewage Disposal Plant

This paper presents a method for designing a set of rules for a general knowledge-based controller. For this purpose, we first introduce a representation and analysis scheme called influence diagram from a field of the decision theory, and provide a generic model for a sensor-based control. We show that a rationally-designed control rule set can be obtained by performing probabilistic reasoning and by compiling the solutions obtained from this model. Next, as a practical application, we apply the method to the design of the knowledge-based control system for a wastewater treatment (i. e., sewage disposal) plant. We attemp to explicate the expert operator's cognitive map concerning with this plant by influence diagrams and show that our method derives a set of control rules that is rationally designed for establishing the coordinate control of multiple processes. Finally, the derived control rules are applied to an actual plant for the sewage disposal and the control result is shown.

Advances in fuzzy adaptive control

Fuzzy control has proven effective for complex, nonlinear, imprecisely-defined processes for which standard models and controls are impractical or cannot be derived. Deriving fuzzy control is, however, often difficult and time-consuming. Furthermore, severe problems of high-dimensionality are usually encountered in the implementation of control for systems with multiple inputs and outputs. More efficient and systematic methods for knowledge acquisition and fuzzy controller synthesis are needed. Effective, intelligent automated systems, such as adaptive fuzzy controllers, capable of learning from process data as well as incorporating linguistic data, possess significant advantages that make them attractive candidates for the much needed technology. An adaptive fuzzy controller could automatically generate a set of fuzzy control rules and improve on them as the control process evolves. In this paper, we survey major results on the development of intelligent, adaptive fuzzy control. Particular focus is given to methods which combine the learning capabilities of neural networks with fuzzy logic control, as these appear to be most promising.

Construction of an on-line fuzzy controller for the dynamic activated sludge process

This paper presents and discusses an alternative approach, called fuzzy control theory, which can forecast and control complex, uncertain and/or highly-non-linear systems properly. Building the set of control rules is a very difficult task in fuzzy control and they are generated through on-line operation data in this study, not through experts' knowledge and/or operators' opinions, which is different from conventional applications. The fuzzy control theory can be conveniently used as an aid for automatic control, especially for complex and highly-non-linear systems. The steps using the fuzzy control technique are issued, compared and discussed in this paper and are demonstrated by a case study of a dynamic activated sludge process which is much more complex than other wastewater treatment systems. The effects of forecast and control of the fuzzy controller established from the derived fuzzy model are shown by computer simulation and verified by experiments. The results indicate that the forecast and control utilities of the fuzzy controllers are good and very helpful to the understanding of complex systems.

Concurrency control issues in nested transactions

The concept of nested transactions offers more decomposable execution units and finer-grained control over concurrency and recovery than "flat" transactions. Furthermore, it supports the decomposition of a "unit of work" into subtasks and their appropriate distribution in a computer system as a prerequisite of intratransaction parallelism. However, to exploit its full potential, suitable granules of concurrency control as well as access modes for shared data are necessary. In this article, we investigate various issues of concurrency control for nested transactions. First, the mechanisms for cooperation and communication within nested transactions should not impede parallel execution of transactions among parent and children or among siblings. Therefore, a model for nested transactions is proposed allowing for effective exploitation of intra-transaction parallelism. Starting with a set of basic locking rules, we introduce the concept of "downward inheritance of locks" to make data manipulated by a parent available to its children. To support supervised and restricted access, this concept is refined to "controlled downward inheritance." The initial concurrency control scheme was based on S-X locks for "flat," non-overlapping data objects. In order to adjust this scheme for practical applications, a set of concurrency control rules is derived for generalized lock modes described by a compatibility matrix. Also, these rules are combined with a hierarchical locking scheme to improve selective access to data granules of varying sizes. After having tied together both types of hierarchies (transaction and object), it can be shown how "controlled downward inheritance" for hierarchical objects is achieved in nested transactions. Finally, problems of deadlock detection and resolution in nested transactions are considered.

Knowledge-directed interpretation of mechanical engineering drawings

A methodology for the interpretation of images of engineering drawings is presented. The approach is based on the combination of schemata describing prototypical drawing constructs with a library of low-level image analysis routines and a set of explicit control rules applied by an LR(1) parser. The resulting system (Anon) integrates bottom-up and top-down processing strategies within a single, flexible framework modeled on the human perceptual cycle. Anon's structure and operation are described and discussed, and examples of its interpretation of real mechanical drawings are shown.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Application of fuzzy logic control to a manipulator

The authors describe a control strategy for robotic manipulators that incorporates a proportional-plus-integral (PI) controller with a simple fuzzy logic (FL) controller. In the proposed strategy, the PI controller is used to ensure fast transient response and zero steady-state error for step inputs, or end-point control, whereas the FL controller is used to enhance the damping characteristics of the overall system. The main advantage of the proposed control energy is that only the current and previous measurements and a set of simple control rules are required; as such, it can be readily implemented. The proposed FL controller is described, and its effectiveness is demonstrated through simulations involving the control of a two-arm two-link manipulator.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Application of Fuzzy Logic Control Scheme for Stability Enhancement of a Power System

This paper presents a fuzzy logic stabilizer for stability enhancement of power systems. Based on the condition of the study generator, and with the aid of two membership functions and a set of simple control rules, the stabilizing signal is determined. The generator condition is defined in terms of its speed deviation Δω and acceleration α, i.e., by the point (Fa α, Δω) where Fa is the weighting parameter for α. Results from nonlinear simulation studies show that the proposed fuzzy logic stabilizer can enhance the damping characteristics and the robustness of a power system.