Fuzzy Controller

The aim of this paper is to present a new fuzzy control scheme for real time drive efficiency improvement by the adaptive flux reduction at light loads during steady states and by the optimum flux recovery during transient states. The optimum flux recovery instead of the rated flux does not sacrifice the drive dynamic response since the torque capacity remains the same and accelerates the search convergence in steady states. The control scheme is cooperated with an Indirect-Field-Oriented-Control (IFOC) motor drive. The design of the new fuzzy control scheme is focused to recognize the motor state operation in order to control under the corresponding circumstances. The proposed fuzzy control scheme includes three main units: one Fuzzy-Logic-Search Controller FLSC1, one Fuzzy-Logic-Controller FLC2 and a State detector unit. The State detector unit decides which of the FLSC1 or FLC2 controllers is used, in accordance to the operation circumstances. The fuzzy controllers utilize the stator flux-producing current component into d-q reference frame (stator current direct axis i sd) as control variable, in order to estimate the next optimal change of Δi sd. The drive efficiency improvement and the drive dynamic response is currently validated in laboratory using an experimental array of a 3phase 1 Hp induction motor drive. The control scheme is implemented using the platform eZdsp from Digital Spectrum based on the fixed point 32 bit DSP TMS320F2812 from TI.

This paper focuses on hardware implementations of fuzzy inference systems which provide low silicon cost, high operational speed and adaptability to different application domains. The architecture and basic building blocks of two fuzzy logic controllers are described and their functionality is illustrated with experimental results showing the capability of these systems to be applied as function approximators.

This study involves the fuzzy logic controlling of brushless DC (BLDC) motor with rapid control prototyping method. Initially a MATLAB/Simulink model of the six pulse BLDC motor and its power electronic driver is established. A fuzzy controller is developed by using MATLAB Fuzzy-Logic Toolbox and then it is inserted into the Simulink model. This model is directly communicated with a DSP through DS2201 dSPACE digital signal kit. PWM pulses are produced on the simulation model and DSP C program is directly developed through dSPACE system. It is shown that, fuzzy controller is successfully controlling the motor and, the model based programming of DSPs is very simple and versatile comparing with that of conventional method.

The paper describes a variable speed wind generation system where fuzzy logic principles are used for efficiency optimization and performance enhancement control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which pumps power to a utility grid or can supply to an autonomous system. The generation system has fuzzy logic control with vector control in the inner loops. A fuzzy controller tracks the generator speed with the wind velocity to extract the maximum power. A second fuzzy controller programs the machine flux for light load efficiency improvement, and a third fuzzy controller gives robust speed control against wind gust and turbine oscillatory torque. The complete control system has been developed, analyzed, and validated by simulation study. Performances have then been evaluated in detail.

In this study, a new method is proposed for tuning the coefficients of PID-type fuzzy logic controllers (FLCs). The new method adjusts the input scaling factor corresponding to the derivative coefficient and the output scaling factor corresponding to the integral coefficient of the PID-type FLC using a fuzzy inference mechanism in an on-line manner. The fuzzy inference mechanism that adjusts the related coefficients has two inputs, one of which is called ''normalized acceleration'' and the other one is the classical ''error''. The ''normalized acceleration'' gives the ''relative rate'' information about the fastness or slowness of the system response. An appropriate rule-base is generated for the adaptation of the derivative coefficient of the PID-type FLC using these two input variables. The integral coefficient is then updated as the reciprocal of the derivative coefficient. The robustness and effectiveness of the new self-tuning algorithm have been compared with the other related tuning methods proposed in the literature through simulations. The simulations are done on a second-order system with varying parameters and time delay. r

In this paper we describe the application of Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) on the optimization of the membership functions' parameters of a fuzzy logic controller (FLC) in order to find the optimal intelligent controller for an autonomous wheeled mobile robot. The results obtained by the simulations performed are statistically compared among them and the previous work results obtained with GAs in order to find which is the best optimization technique for this particular robotics problem.

This paper investigates the system stability of a sampled-data fuzzy-model-based control system, formed by a nonlinear plant and a sampled-data fuzzy controller connected in a closed loop. The sampled-data fuzzy controller has an advantage that it can be implemented using a microcontroller or a digital computer to lower the implementation cost and time. However, discontinuity introduced by the sampling activity complicates the system dynamics and makes the stability analysis difficult compared with the pure continuous-time fuzzy control systems. Moreover, the favourable property of the continuous-time fuzzy control systems which is able to relax the stability analysis result vanishes in the sampled-data fuzzy control systems. A Lyapunov-based approach is employed to derive the LMI-based stability conditions to guarantee the system stability. To facilitate the stability analysis, a switching fuzzy model consisting of some local fuzzy models is employed to represent the nonlinear plant to be controlled. The comparatively less strong nonlinearity of each local fuzzy model eases the satisfaction of the stability conditions. Furthermore, membership functions of both fuzzy model and sampled-data fuzzy controller are considered to alleviate the conservativeness of the stability analysis result. A simulation example is given to illustrate the merits of the proposed approach.

In this study a fuzzy logic controller for mobile robot navigation has been designed. The designed controller deals with the uncertainty and ambiguity of the information the system receives. The technique has been used on an experimental mobile robot which uses a set of seven ultrasonic sensors to perceive the environment. The designed fuzzy controller maps the input space (information coming from ultrasonic sensors) to a safe collision-avoidance trajectory (output space) in real time. This is accomplished by an inference process based on rules (a list of IF-THEN statements) taken from a knowledge base. The technique generates satisfactory direction and velocity maneuvers of the autonomous vehicle which are used by the robot to reach its goal safely. Simulation and experimental results show the method can be used satisfactorily by wheeled mobile robots moving on unknown static terrains.

This paper presents a dynamic model of a fuel cell system for residential power generation. The models proposed include a fuel cell stack model, reformer model and DC/AC inverter model. Furthermore a fuzzy logic (FLC) controller is used to control the active power of PEM fuel cell system. The controller modifies the hydrogen flow feedback from the terminal load. Simulation results confirmed the high performance capability of the fuzzy logic controller to control power generation. K e y w o r d s: polymer-electrolyte fuel cell, dynamic model, residential power, fuzzy controller

This article starts with an overview of the principles of tertiary building control, and some basic points on the application of fuzzy logic to this problem. After a presentation of the TRNSYS simulation program, two new modules are described. The first of these, the TYPE 70, uses a simplified model of a building, based on an electrical analogy. It sets out the heat balance of a thermally homogenous zone, and also studies a multi-zone building made up of a number of adjacent rooms, namely several TYPE 70s connected together. The second module, the TYPE 71, is a fuzzy controller which determines the supply temperature of the water in the heating system. Its originality lies in the fact that it takes into account the 'thermal state' of the building, in addition to the external temperature and the solar gains. A comparison is made between the TYPE 71 and an equivalent, but classical, controller. Simulations carried out with a mono-zone storey of a building showed that the fuzzy controller produced a temperature closer to the set-point temperature, and also a more accurate optimization of the restarting procedure. © 1997 Elsevier Science S.A.

A concept of control for municipal waste water treatment plants is developed. The used fuzzy approach to real-time control aims at an operation of the plant that guarantees satisfactory performance of cleaning in different situations of load. The problem of nitrogen removal using flexible nitrification and denitrification volumes is tackled by means of fuzzy control. Important aspects of a simulation based analysis of the process including the structure of control are reported. Special emphasis is laid on the results obtained for conventional and fuzzy control in respect to a reduction of total inorganic nitrogen and lowering of running costs.

The fundamental equations governing the operation of a dc motor are straightforward and are well presented in most undergraduate curricula, usually in a required energy-conversion or electrical-machinery class at the junior level. The physical implications of the equations regarding device behavior, and the need for speed control, are easily understood. Furthermore, students can readily develop a mental picture of the physical motor and its response.

ABSTRACT Application of a fuzzy logic controller to a class of hydraulically actuated industrial robots is investigated. A simple set of membership functions and rules are described which meets certain control requirements. An off-line routine based on the simplex method is outlined to tune the controller gains for an optimum response. The fuzzy control gains are tuned by minimizing the summation of absolute position errors over step input responses. The fuzzy logic controller is first examined through simulation of a two-link hydraulic robot of the same hydraulic configuration as many industrial manipulators. The controller has positive aspects which cannot be easily achieved by conventional control techniques. These include a fast rise-time and a well maintained damped response. The fuzzy controller is applied to an instrumented Unimate MK-II industrial hydraulic robot. The experimental results are encouraging in that the best performing control gains are found for different links with a reasonable number of trials and produce step responses with fast response and few oscillations at the set point. The controller demonstrates robustness

This paper addresses robust observer based H∞ control problem for Takagi-Sugeno fuzzy systems with timevarying norm bounded uncertainties. Sufficient relaxed conditions for synthesis of a fuzzy observer and a fuzzy controller for T-S fuzzy systems are derived in terms of a set of linear matrix inequalities (LMIs). In comparison with the existing techniques in literature, the proposed approach considerably simplifies the design procedure and gives in only one step the controller and the observer gains. The observer and controller designed are capable to reject the disturbance assumed known but norm bounded. In order to highlight the performance of the proposed control algorithm, numerical simulations are performed.

Theoretical analysis and implementation of autonomous staircase detection and stair climbing algorithms on a novel rescue mobile robot are presented in this paper. The main goals are to find the staircase during navigation and to implement a fast, safe and smooth autonomous stair climbing algorithm. Silver is used here as the experimental platform. This tracked mobile robot is a tele-operative rescue mobile robot with great capabilities in climbing obstacles in destructed areas. Its performance has been demonstrated in rescue robot league of international RoboCup competitions. A fuzzy controller is applied to direct the robot during stair climbing. Controller inputs are generated by processing the range data from two laser range finders which scan the environment one horizontally and the other vertically. The experimental results of stair detection algorithm and stair climbing controller are demonstrated at the end.

This paper presents the design and evaluation of a fuzzy logic trac signal controller for an isolated intersection. The controller is designed to be responsive to real-time trac demands. The fuzzy controller uses vehicle loop detectors, placed upstream of the intersection on each approach, to measure approach ows and estimate queues. These data are used to decide, at regular time intervals, whether to extend or terminate the current signal phase. These decisions are made using a two-stage fuzzy logic procedure. In the ®rst stage, observed approach trac¯ows are used to estimate relative trac intensities in the competing approaches. These trac intensities are then used in the second stage to determine whether the current signal phase should be extended or terminated. The performance of this controller is compared to that of a trac-actuated controller for dierent trac conditions on a simulated four-approach intersection. Ó (M.B. Trabia).

This paper is devoted to the development of a fast industrial design of an embedded fuzzy controller for an OL unstable plant starting from an initial fuzzy system developed in conjunction with an expert. Through prototyping and implementation on a general-purpose microprocessor the CL plant can then be tested and used to iteratively identify and control the plant until the desired control system criteria is achieved. This paper describes the design, implementation and testing of an embedded fuzzy controller as a simple, practical solution to a regulation problem in reefer refrigeration systems. Fuzzy logic was utilized for the ease with which it enables expert opinion expressed within the framework of a natural language to be converted into a PID-like control algorithm. By using the Motorola HC12 MCU with its inbuilt fuzzy logic instruction set a rapid prototype design was implemented and the application of fuzzy logic and iterative identification and control to general industrial refrigeration systems was found to be a possible economically feasible solution.

The stability analysis and the design technique of fuzzy control systems using fuzzy block diagrams are discussed. First, we show the concept of fuzzy blocks and consider the connection problems of fuzzy blocks diagrams. We derive some theorems and corollaries with respect to two basic types of connections of fuzzy blocks. In order to preserve some properties in a connection of fuzzy blocks, continuous piecewise-polynomial membership functions are defined. Secondly, a sufficient condition which guarantees the stability of fuzzy systems is obtained in terms of Lyapunov's direct method. We give an important fact based on this condition. Thirdly, we propose a new design technique of a fuzzy controller. The fuzzy block diagrams and the stability analysis are applied to the design problems of a model-based fuzzy controller.

This paper describes the effective utilization of particle swarm optimization (PSO) to train a Fuzzy- Neural controller (FNC) for two samples of high nonlinear systems. First sample system is continuous stirred tank reactor (CSTR) and the second is Van- Der-Pole (VDP) oscillator. FNCs are learned by PSO to hold the selected state variable at fixed set point. While the controller's

Fifty years of cruise technology development were studied. The present driving environment is too noisy which imposes great demands on the present cruise technology. Today's cruise technology puts emphasis on driving safety by introducing a minimum safe distance for safe control actions (breaking, accelerating, etc.). Two control methods were used in the past which are compared and investigated: classical control and fuzzy logic control. This review clearly indicates that the later method is taking over due to its ability to handle cruise problems which are too noisy and difficult to model. 0 1997 Elsevier Science Ltd.

In this work, an efficient fuzzy logic controller is used to improve the controllability of a SVC (Static Var Compensator). The SVC device is a member of the FACTS (Flexible Alternating Current Transmission System) family employed in power transmission systems. The new fuzzy logic controller replaces the existing PI regulator and permits better disturbance compensation and voltage regulation. The SVC is used to maintain the voltage bus constant by absorbing or injecting reactive power. The proposed fuzzy controller uses the error between the reference voltage and the measured voltage to determine the required susceptance B. The fuzzy controller has been implemented in MATLAB/SIMULINK and the effectiveness of the reactive power compensation and the voltage regulation are demonstrated by simulations. Fuzzy logic control of a SVC to improve the transient stability of ac power systems, I. Mansour, D.O.

En este trabajo se propone un nuevo método de defuzzificación. Éste es una modificación del método del centro de área utilizando singletons. La característica principal de este nuevo método es la inclusión de elementos de retraso. De ésta manera, los efectos de los retrasos inherentes a cualquier proceso industrial son enormemente reducidos. También se presenta una versión simplificada de controlador difuso como PI que hace uso del nuevo método de defuzzificación. Los resultados obtenidos de la aplicación de este controlador en un caso de estudio son presentados y comparados con los obtenidos con un control PI tradicional.

The primary focus of the paper is on the development of an intelligent control scheme, which is insensitive to parametric uncertainty, load and parameter fluctuations and most importantly amenable for real time implementation. In this paper, we present a stable Lateral Fuzzy Controller (LAFC) for an outdoor Autonomously Guided Vehicle (AGV), which is a converted electrically powered golf-car. The controller

Most fuzzy controllers and fuzzy expert systems must predefine membership functions and fuzzy inference rules to map numeric data into linguistic variable terms and to make fuzzy reasoning work. In this paper, we propose a general learning method as a framework for automatically deriving membership functions and fuzzy if-then rules from a set of given training examples to rapidly build a prototype fuzzy expert system. Based on the membership functions and the fuzzy rules derived, a corresponding fuzzy inference procedure to process inputs is also developed.

This paper presents an implementation of an aircraft pose and motion estimator using visual systems as the principal sensor for controlling an Unmanned Aerial Vehicle (UAV) or as a redundant system for an Inertial Measure Unit (IMU) and gyros sensors. First, we explore the applications of the unified theory for central catadioptric cameras for attitude and heading estimation, explaining how the skyline is projected on the catadioptric image and how it is segmented and used to calculate the UAV’s attitude. Then we use appearance images to obtain a visual compass, and we calculate the relative rotation and heading of the aerial vehicle. Additionally, we show the use of a stereo system to calculate the aircraft height and to measure the UAV’s motion. Finally, we present a visual tracking system based on Fuzzy controllers working in both a UAV and a camera pan and tilt platform. Every part is tested using the UAV COLIBRI platform to validate the different approaches, which include comparison of the estimated data with the inertial values measured onboard the helicopter platform and the validation of the tracking schemes on real flights.

Anti-lock and also Anti-slip braking system controller designed for stability enhancement of vehicle during braking and turning circumstances is presented. Using available signals, a novel structure is proposed for vehicle stability improvement encountering critical braking conditions such as braking on slippery or μ-split road surfaces. In conventional vehicles, undesired lane changes may occur due to equal dispatch of braking torques to all wheels simultaneously. Also, intensive pressure on brake pedal can bring about wheels lockup which results in vehicle instability and undesired lane changes. The Anti-Lock Braking System (ALBS) and also Anti-Slip Braking System (ASBS) can serve as a driver assist system in vehicle path correction facing critical driving conditions during braking and turning round on different road surfaces. For these purposes, at first, a reference yaw angle is used for predicting of stable vehicle path. Then, slip of each wheel will be controlled by an Anti-Slip fuzzy controller which has been designed for each wheel. Also, a sliding mode controller has been designed so as to control the yaw angle concerning the yaw error where the yaw error is resulted from the difference between the reference value and actual yaw angle. Then, the difference between the left and the right wheels braking torques is used by the sliding mode controller in order to decrease the yaw error. Considering a model of Three-Degree-of-Freedom for chassis modeling and One-Degree-of-Freedom Dugoff's tire model for each wheel, a series of Matlab/Simulink simulation results will be presented in order to validate the effectiveness of the proposed controller.

This paper investigates the potential of supercapacitor based energy storage system for enhancing fault ride through capability of wind driven induction generator. A dynamic model of wind generation system including Static Compensator (STATCOM) is developed. The power control capability of the STATCOM is extended by incorporating supercapacitor based energy storage system. A vector control technique based on the decoupling of real and reactive power is implemented. Simulation results show that fixed speed induction generator achieves significant grid fault withstand capability when aided by supercapacitor based energy storage system. Induction generator regains prefault status immediately after the fault is cleared.

This paper investigates the potential of supercapacitor based energy storage system for enhancing fault ride through capability of wind driven induction generator. A dynamic model of wind generation system including Static Compensator (STATCOM) is developed. The power control capability of the STATCOM is extended by incorporating supercapacitor based energy storage system. A vector control technique based on the decoupling of real and reactive power is implemented. Simulation results show that fixed speed induction generator achieves significant grid fault withstand capability when aided by supercapacitor based energy storage system. Induction generator regains prefault status immediately after the fault is cleared.

The paper describes the design and development of an intelligent autonomous vehicle that can navigate without human operator in unknown environments. We have interfaced to AT 89C52 microcontroller with the ultrasonic sensors and GPS receiver for easy access to the destination by avoiding the obstacles in its way at low level. A camera is interfaced to pc for lanes identification and road following task at higher level. The designed vehicle has modular structure and more modules can be added without much complexity. The experimental results demonstrate the effectiveness of the proposed design.

Servomotor uses feedback controller to control either the speed or the position or both. This paper discusses the performance comparisons of a modified genetic algorithm, named as the semi-parallel operation genetic algorithm (SPOGA) and the conventional genetic algorithm (GA), in optimizing the I/O scale factors, membership functions, and rules of a hybrid-fuzzy controller. Singleton fuzzification is used as a fuzzifier with seven membership functions for both input and output of the controller, whilst center of average is used as a defuzzifier. A 21-bit-30-population is used in SPOGA for both I/O scales and for membership functions. Two control modes are applied in cascaded: position and speed. Both the simulation and practical experiment results show that fuzzy-logic parallel integral controller (FLIC) with SPOGA-optimized is better as compared to FLIC with GA-optimized and also the non-optimized FLIC, FLC, and PI in terms of performance and the reduction of the number of test runs for the optimization.

A novel driver-assist stability system for all-wheel-drive Electric Vehicles is introduced. The system helps drivers maintain control in the event of a driving emergency, including heavy braking or obstacle avoidance. The system comprises a Fuzzy logic system that independently controls wheel torques to prevent vehicle spin. Another Fuzzy wheel slip controller is used to enhance the vehicle stability and safety. A neural network is trained to generate the required reference for yaw rate. Vehicle true speed is estimated by a sensor data fusion method. The intrinsic robustness of Fuzzy controllers allows the system to operate in different road conditions successfully. Moreover, the ease to implement Fuzzy controllers gives a potential for vehicle stability enhancement.

Magnetic drive pump has gotten great achievement and has been widely used in some special fields. Currently, the researches on magnetic drive pump have focused on hydraulic design, bearing, axial force in China, and a new magnetic drive pump with low flow and high head have been developed overseas. However, low efficiency and large size are the common disadvantages for the magnetic drive pump. In order to study the performance of high-speed magnetic drive pump, FLUENT is used to simulate the inner flow field of magnetic drive pumps with different rotate speeds, and get velocity and pressure distributions of inner flow field. According to analysis the changes of velocity and pressure to ensure the stable operation of pump and avoid cavitation. Based on the analysis of velocity and pressure, this paper presents the pump efficiency of magnetic drive pumps with different rotated speeds by calculating the power loss in impeller and volute, hydraulic loss, volumetric loss, mechanical loss and discussing the different reasons of power loss between the magnetic drive pumps with different rotated speeds. In addition, the magnetic drive pumps are tested in a closed testing system. Pressure sensors are set in inlet and outlet of magnetic drive pumps to get the pressure and the head, while the pump efficiency can be got by calculating the power loss between the input power and the outlet power. The results of simulation and test are similar, which shows that the method of simulation is feasible. The proposed research provides the instruction to design high-speed magnetic drive pump.

In this paper, the impact of different gate-controlled series capacitor (GCSC) control methodologies on sub-synchronous resonance (SSR) problems of series capacitive compensated transmission lines is analyzed. The low-frequency power oscillation (LFPO) damping using GCSC also is studied. In these studies, the effect of the rating of the GCSC is also considered. Three different control methodologies are proposed: open loop, constant power, and Takagi-Sugeno (TS) fuzzy control. It is shown that the GCSC can damp both the SSR and LFPO using the constant power control (CPC) methodology. It is also shown that when the parameters of the CPC is optimized by the TS fuzzy controller, the third methodology can present a cost-effective solution for both the SSR and LFPO damping. In this work, the IEEE First Benchmark Model is employed, including a GCSC device, and the study is performed using MATLAB program.

One of the most important rules in electric power system operation and control is Load Frequency Controller (LFC). Many problems are subject to LFC such as a generating unit is suddenly disconnected by the protection equipment and suddenly large load is connected or disconnected. The frequency gets deviated from nominal value when the real power balance is harmed due to disturbances.LFC is responsible for load balancing and restoring the natural frequency to its natural position. In this paper, PSO-fuzzy logic technique for Load Frequency Control system was proposed. PSO optimization method is used to tuning the input and output gains for the fuzzy controller. The proposed method has been tested on two symmetrical thermal areas of an interconnected electrical power system. The simulation results are carried out in term of effectiveness of the frequency time response on its damping and compared it to common PID controller. The results show the performances of the proposed controller have quite promising compared to PID controller.