Showing papers on "Fuzzy control system published in 1970"

Dynamic Programming and Fuzzy Allocation Processes.

Abstract: : The modeling and computational aspects of certain allocation processes are studied through a new concept in systems theory -- fuzzy decision making. The use of these concepts will generally provide models of better proximity to the systems modelled than the traditional deterministic and stochastic approaches. Some concepts of fuzzy systems theory are first introduced. Fuzzy dynamic programming models with their corresponding flow charts are then provided for an allocation problem arising in R and D systems. The computational problems in fuzzy algorithms are discussed. An extensive bibliography on fuzzy decision theory is included. (Author)

Behaviour based mobile robot navigation technique using ai system: experimental investigation on active media pioneer robot

Abstract: A key issue in the research of an autonomous robot is the design and development of the navigation technique that enables the robot to navigate in a real world environment. In this research, the issues investigated and methodologies established include (a) Designing of the individual behavior and behavior rule selection using Alpha level fuzzy logic system (b) Designing of the controller, which maps the sensors input to the motor output through model based Fuzzy Logic Inference System and (c) Formulation of the decision-making process by using Alpha-level fuzzy logic system. The proposed method is applied to Active Media Pioneer Robot and the results are discussed and compared with most accepted methods. This approach provides a formal methodology for representing and implementing the human expert heuristic knowledge and perception-based action in mobile robot navigation. In this approach, the operational strategies of the human expert driver are transferred via fuzzy logic to the robot navigation in the form of a set of simple conditional statements composed of linguistic variables. Keywards: Mobile robot, behavior based control, fuzzy logic, alpha level fuzzy logic, obstacle avoidance behavior and goal seek behavior

An Incorporated Use of Fuzzy Logic Toolbox and Modelica Library to Design SSSC Damping Controller

Abstract: ObjectStab is a general purpose simulation tool for power system stability studies developed by Modelica which is an object-oriented modeling language. It provides enough modeling flexibility to allow addition or modification of new power system components. This paper describes an incorporated use of fuzzy logic toolbox in Matlab/Simulink and Object-Stab library to enhance the application of this library into fuzzy control design environment. The example provided here is the modeling of the static synchronous series compensator (SSSC) which is the new device developed in the ObjectStab. In addition, the interface of ObjectStab with Matlab/Simulink for an SSSC damping controller design by fuzzy logic toolbox is explained step by step. Simulation studies in a multi-machine power system confirm the effectiveness of the designed fuzzy controller.

Application Of Fuzzy Inferencing Principles InReservoir Operation Analysis

Abstract: Successful application of fuzzy control to an optimum control problem relies on the ability to make appropriate inferences from fuzzy information. In the reservoir operation problem, the operational rule adopted for simulation of the performance of a reservoir under historical or generated inflows, demands, etc. usually relates to the concept of an optimum release for the 'current' period. The main source of uncertainty in this process arises from the prediction of the value of the inflow during the current period. The value of this inflow is generally known in terms of its distribution. Since the storage volume at the end of each period is highly dependent on this inflow, it also is influenced by this uncertainty. Most stochastic simulation techniques for reservoir operation, however, operate on the basis of strict compliance to, or interpolation of, the operating policies and use as input stochastically generated inflows to account for the inflow uncertainty. Little attention, if any, is given to accounting for uncertainty in the decision itself. Since the optimum release decision obtained from a 3-state variable (storage volume at the beginning of the current period, the inflow in the previous time period, and the reservoir release during the current period) stochastic dynamic program is based on evaluation of the expected value of the return to the system, such a release decision should only be considered as a 'guide' such that, in certain circumstances, deviation of the release decision from the operating rule might be necessary. In this paper, a rational approach for selecting a release decision different from that envisaged in the operation rule is derived from application of the principles of fuzzy inference. The approach is demonstrated by application to the Wadaslintang Reservoir in Prembun, Central Java, Indonesia. Transactions on Ecology and the Environment vol 12, © 1996 WIT Press, www.witpress.com, ISSN 1743-3541

Fuzzy Control For Regenerative TractionStation In DC Railway Systems

Abstract: This paper first presents the AC/DC load flow algorithm used for the analysis of DC railway systems. Based on the load flow results, the fuzzy dwell time control of the train, the tap position and control angle control of rectifier/inverter station are then described. These are implemented for improving energy efficiency and operation in a DC railway system.

Adaptive Fuzzy System For PredictionConcentration In Macau

Abstract: Due to the small size and the complexity of the effects from the neighboring metropolises, traditional modeling such as the wind flow and dispersion modeling of the air pollution problem in Macau may not be economical or feasible. Investigation on the applicability of an adaptive neuro-fuzzy inference system (ANFIS) for predicting the daily average sulfur dioxide concentration in Macau is carried out. The 5-day ahead SO: concentration values predicted by the model are within 14% of the measured values which indicates that the ANFIS could be used to develop efficient air-quality prediction models.

Adaptive Fuzzy Logic Based Controller For APosition Control System

Abstract: High performance position control can be obtained with dc servomotors and actuators by using efficient control strategies. For precise control of position, the control strategy employed should result in fast control of the output, with minimum overshoot and least steady state error. The advancement of control theory over the last 30 years resulted in a huge choice of control schemes, given a plant or a system to be controlled. Despite this, traditional PID (proportional integral and derivative) control scheme is the popular choice for implementation in industrial environment, as this scheme is simple and easy to implement and its design does not require an exact knowledge of the controlled plant dynamics. Fuzzy logic is recently finding wide popularity in various applications that include management, economics, medicine and process control systems. This paper presents the simulation and experimental results of the adaptive fuzzy logic control scheme proposed for a position control system, with cost-effective, real time implementation as the main objective. Traditional PID control and more recent fuzzy logic control schemes have been used for the studies. It is demonstrated both by simulation and experimental implementation on a prototype system that fuzzy logic control can provide better control, does not require mathematical modelling of the plant and yields better disturbance rejection properties.