Showing papers on "Fuzzy control system published in 1988"

Aggregating Template Or Rule Antecedents In Real-time Expert Systems With Fuzzy Set Logic

Abstract: An algorithm based on fuzzy set logic and nonlinear program- ming optimization is proposed for aggregating antecedents within a template or rule into a single valued entity for use in a detachment or implication operator for forward chaining in an expert system. The method assumes that confidences of the observed antecedents can be sorted, otherwise a paired comparison weighting developed by Saaty [1] must be employed to equalize the relative importances of the arguments before sorting is performed. The method is based on a new type of fuzzy Ordered Weighted Average (OWA) operator proposed by Yager [2,3]. An offline nonlinear program (geometric program) involving only two optimization variables is used to develop the weights using a formulation by O'Hagan [4]. This formulation is equivalent to the well-known Gibbs free energy problem of chemical engineering [5,6]. The proposed aggregation method is computationally efficient involving only an 0(n - In(n)) sort and an 0(n) inner product when aggregating n antecedents and is ideally suited for real-time expert system or fuzzy multi- objective decision applications.

Fuzzy concepts in expert systems

Abstract: The authors present a comprehensive expert-system building tool, called System Z-II, that can deal with exact, fuzzy (or inexact), and combined reasoning, allowing fuzzy and normal terms to be freely mixed in the rules and facts of an expert system. This fully implemented tool has been used to build several expert systems in the fields of student curriculum advisement, medical diagnosis, psychoanalysis, and risk analysis. System Z-II is a rule-based system that uses fuzzy logic and fuzzy numbers for its inexact reasoning. It uses two basic inexact concepts, fuzziness and uncertainty, which are distinct from each other in the system. >

Fuzzy control system for automatic transmission

Abstract: A shift control system for an automatic transmission includes a sensing section for sensing various vehicle conditions, such as vehicle speed, acceleration, throttle opening, time rate of change of the throttle opening, and a vehicle running resistance, and fuzzy control section for determining a desired gear ratio by fuzzy inference using predetermined membership functions of the vehicle conditions.

Fuzzy Logic in Knowledge-Based Systems, Decision and Control

Abstract: Theory of Fuzzy Logic. (Papers by: M.M. Gupta E. Hisdal M.M. Gupta, G.K. Knopf, P.N. Nikiforuk B. Bouchon M. Zeleny H.J. Skala S.T. Wierzchon J.J. Buckley, W. Siler E.E. Kerre, A. van Schooten S. Gottwald W. Pedrycz L. Hongxing D. Butnariu M.K. Chakraborty J. Montero W. Ostasiewicz). Applications in Knowledge-Based Systems, Decision and Control. (Papers by: A.F. Rocha, M. Theoto, P. Torasso T. Xiangchu, T. Chengyuan T. Onisawa X. Chunxi, C. Shiquan H. Nojiri A. Kandel, B. Heshmathy F. Azorin). Appendices.

Automatic train operation system based on predictive fuzzy control

Abstract: A predictive fuzzy control including control rules to achieve desirable conditions has been proposed and applied to an automatic train operation (ATO) system. The capability of the fuzzy ATO to control train operation as skilfully as experienced operators was confirmed by a trial run made in the Sendai municipal subway system. The control rules of the fuzzy ATO and results of the test run are described. >

Probabilistic fuzzy model for dynamic systems

Abstract: We present a model, based on a fuzzy relation obtained from fuzzy referential sets on the input and output spaces, for predicting the behaviour of nonlinear dynamic systems. The model can be made to learn from experience, and the computing requirements are modest, making online application feasible. Some numerical results are compared with those of earlier models.

Fuzzy Versus Stochastic Approaches to Multicriteria Linear Programming under Uncertainty

Abstract: Recently, both authors independently proposed two different approaches to multicriteria linear programming under uncertainty with a view of an application to some long term planning problems. Slowinski [11] has developed a method called FLIP (Fuzzy LInear P rogramming) based on the application of fuzzy numbers for modeling imprecise data. On the other hand, Teghem et al. [17] have proposed the method STRANGE (STRAtegy for Nuclear Generation of Electricity), a stochastic approach to the same problem. Both methods are interactive and at each step present to the decision maker (DM) a large representation of efficient solutions. The aim of this study is to compare FLIP and STRANGE. A didactic example is first defined and resolved by both methods. Next, every stage of both procedures is analyzed and compared on the basis of this example; taking into account imprecise data, formulation of deterministic multicriteria problems associated with the original problem, getting the first compromise solution, the role of the DM in the interactive decision-making steps, etc. For each of these stages, possible limitations, advantages, and inconveniences of both methods are emphasized. General conclusions following from this comparison are finally drawn.

On the design and performance evaluation of adaptive fuzzy controllers

Abstract: An automated design technique to optimize membership functions for a fuzzy controller numerically is discussed. The technique is applied to the design of a fuzzy controller for regulating mean aerial blood pressure in a noisy environment (e.g. during surgery or while a patient is being treated in the ICU). The result is extended to construct an adaptive fuzzy controller, and its performance is evaluated through simulation. >

Speed change controller for automatic transmission

Abstract: PURPOSE:To carry out speed change conformable to the intension of a driver by making fuzzy reasoning based on signals indicating vehicle speed, acceleration, engine load, variation rates thereof and running resistance, then determining a gear position. CONSTITUTION:Operating condition signals of a vehicle detected through means for detecting vehicle speed, engine load, variation rate of engine load and running resistance are provided to a speed change ratio determining means. The speed change ratio determining means makes fuzzy reasoning based on a membership function of a fuzzy set preset by a membership function setting means and determines a specific speed change ratio. A speed change actuator is driven through a drive means based on the determined speed change ratio so as to carry out specific speed change. Consequently, the vehicle can be operated through fuzzy control with such speed change ratio as can be obtained through human judged operation.

Expert-system-based fuzzy control of arterial pressure by drug infusion.

Abstract: This paper proposes a new type of fuzzy controller which is based on the general-purpose Fuzzy LOgic Production System shell FLOPS [1, 2, 7, 12, 13]. Human mean arterial pressure was controlled by the proposed fuzzy controller, in digital computer stimulation, by regulating the infusion rate of the drug, sodium nitroprusside. Under nonstationary arterial background pressure noise, mean arterial pressures of different patients with different sensitivities to sodium nitroprusside were controlled satisfactorily. Furthermore, the effects of changing the adjustable parameters of the proposed fuzzy controller on the control results were studied. Finally, the performance of the proposed fuzzy controller was compared to that of a fine-tuned Proportional-Integral (PI) controller by using a first-order linear model.

Design of a Self-Tuning Expert Controller for a Gasoline Refinery Catalytic Reformer

Abstract: The objective is to explore design concepts for self-tuning expert controllers. To accomplish this, two interacting rule based controllers for supervisory control and system optimization are constructed to control a gasoline catalytic reformer. The knowledge bases for the controllers are established from human operator experience and basic engineering knowledge about the process dynamics. Inference is provided by a fuzzy logic engine. After manual tuning of the expert controller scaling coefficients is accomplished, a crisp heuristic is developed for self-tuning. The performance of the self-tuning expert controller is tested against perturbations of a simulation model of the catalytic reformer.

Fuzzy sets and t -norms in the light of fuzzy logic

Abstract: The paper is a discussion of the many-valued fuzzy logic, which is syntactico-semantically complete and its impact on the fuzzy set theory, namely on the operations with fuzzy sets. Arguments that all the operations with membership grades must fulfil the so called fitting condition are given. It follows that general t -norms are not suitable to be basis of the operations with fuzzy sets. Some general classes of operations with membership grades are presented.

Application of fuzzy control for servo systems

Abstract: Application of fuzzy algorithms in a microprocessor based servomotor controller that requires faster and more accurate response than other industrial processes is discussed. The performance proportional-integral-derivative, model reference adaptive and fuzzy controllers is compared in terms of steady-state error, settling time and response time. The limitation of the fuzzy control algorithms is ascertained. >

Automatic speed control device using self-tuning fuzzy logic

Abstract: A fuzzy logic controller with a built-in function for self-tuning of control characteristics has been applied to an automatic speed control device (ASCD) to create a robust system for production vehicles. Fuzzy logic is used to predict drivers' individual preferences for control characteristics when the vehicle speed is controlled to a set level by the ASCD system. The results are then reflected in the control characteristics to provide the preferred speed control. >

An Autonomous Fuzzy Mobile Robot

Abstract: Autonomous mobile robots should have the capability of recognizing their environments and manoeuvring through those environments on the basis of their own judgement. Fuzzy control is suitable for autonomous mobile robot control where the amount of information to be handled is limited as much as possible and the processing is simple. Autonomous mobile control of a robot is derived from two kinds of controls: for obstacle avoidance and for guidance following an appropriate path to a destination point. Fuzzy control of a robot for obstacle avoidance based on finding permissible passageways using the edges between the floor and the wall or obstacles obtained by processing the image from a CCD camera in front of the robot is developed. Furthermore, guidance control of the robot over paths that are specified in terms of maps may be developed by a process that treats a wrong path as a virtual obstacle on the screen, and the robot advances in the designated direction when it reaches intersections. An autonomous f...

Weighted fuzzy logic and its applications

Abstract: A weighted fuzzy logic is presented in which the truth of a conjunction of propositions (or predicates) is a weighted sum of the truth of each proposition (or predicate). This is different from traditional logic where a conjunction would be false if only one component of the conjunction is false. The proposed logic is quite suitable for reasoning with incomplete knowledge and fuzzy retrieval or fuzzy matching. It is expected to have a very wide range of applications in knowledge engineering and many other areas. Expert systems in medical diagnosis and computer-aided design are considered as applications. >

Start-up of a catalytic reactor by fuzzy controller.

Abstract: It is difficult to control the start-up of a packed-bed catalytic reactor with exothermic reaction by conventional controller. In this paper, a controller based on the fuzzy set theory is applied to such a system. The controller treats heuristic rules by fuzzy interpretation, and it is also equipped with adaptive scaling factors. The performance of the controller is experimentally examined with a reactor for the catalytic oxidation of hydrogen.

A fuzzy blood pressure controller

Abstract: An automatic blood-pressure controller yielding satisfactory results in simulation has been constructed using fuzzy set theory. This prototype controller is not adaptive and is therefore not intended for closed-loop control in surgery. However, the fuzzy control approach is very well suited to closed-loop control in complex biomedical problems. Further study is currently under way in the area of adaptive fuzzy control, in order to manage wide variations in patients' drug responses (e.g. drug sensitivity and time delay). >

Fuzzy systolic arrays

Abstract: The design systolic arrays for the computation of the fuzzy inference process in expert systems is considered. Two arrays are designed: one for the overall fuzzy relation between the antecedent and the conclusion portions of the rules of the system, and the other one for the compositional rule of inference. A comparison between the proposed arrays and a uniprocessor is presented. >

The Analysis Of Fuzzy Dynamic Systems Using Cell-to-cell Mapping

Abstract: Since Zadeh's first paper about fuzzy control, many developments have been done in this area. Many researchers tried to create a well structured methodology for building and analyzing fuzzy control systems. However, the efforts are more successful on the applicational aspect than the theoretical one. There are especially few satisfactory results in stability analysis of fuzzy control systems because of the great uncertainty and nonlinearity involved. In this paper, a Cell- to-Cell Mapping method which was successfully used in the nonlinear system analysis is applied to analyze a fuzzy dynamic system. By first partitioning a state - to a Cell State Space, a fuzzy mapping is transfted into a cell-to-cell mapping. Then, an algorithin is used to locate the periodic solutions of the fuzzy dynamic system. The Domain of Attraction of a periodic motion is also shown to describe the behavior of the fuzzy dynamic system.

Expert systems, fuzzy logic and rule-based control explained at last

Abstract: New techniques are becoming available to control and manage manufacturing plant, including rule-based systems, knowledge-based systems, fuzzy logic, etc. This paper will describe briefly what the terms mean and how they interrelate. It is argued that expert, rule-based systems for control differ fundamentally from traditional control systems because they attempt to model the skills of the human operator, rather than modelling the process itself Expert systems and intelligent knowledge-based systems will be introduced, explaining how they may be used in industrial control, for other complex tasks. The problem of knowledge acquisition is discussed, again in the context of control, and the role of fuzzy logic is explained, as a means of expressing necessarily imprecise knowledge and coping with the problems of an incomplete knowledge-base. The self-organising controller is briefly introduced as an enhancement of rule-based control The scope of rule-based control and artificial intelligence in general is disc...