Professor Ebrahim Mamdani Memorial Issue

A new type of multiple-variable fuzzy logic controller is proposed, called the two-layer multiple-variable fuzzy logic controller. In the first layer, various fuzzy decision-making logics are applied to fuzzy variables. The feedback is the weighted sum of the fuzzy variables obtained from fuzzy inference. In the second layer, the weight of each variable is determined by fuzzy inference and system parameters. The two-layer fuzzy control algorithms are applied to a nonlinear, cart-pole system. Computer simulations show the effectiveness and better performances of the two-layer fuzzy logic controllers. >

In this work, a Fuzzy Logic Control (FLC) based MPPT technique is proposed to improve the performance of a stand-alone solar energy system. The Fuzzy logic controller is used as an intelligent way of tracking the maximum power point (MPP). The Taguchi method is adopted in this study to analyze multiple operating conditions of solar PV array. Solar PV output changes with Atmospheric conditions. The change in PV Current and Power are measured and fed to the Fuzzy logic controller as input. The Fuzzy controller is designed with 25 fuzzy rules and the Mamdani fuzzy inference is performed to obtain the aggregation which will be defuzzified by Center of gravity method. Based on the change in PV Current and Power, the Fuzzy logic controller generate the duty cycle for the boost converter (DC-DC converter). The variation of the duty cycle is from 0 to 1. The signal of change in duty ratio from the Fuzzy logic MPPT algorithm is fed to the PWM for switching the IGBT to dynamically update the duty cycle of the boost converter for extracting the maximum power from the solar PV array. A stand-alone Photovoltaic system with a boost converter is simulated in MATLAB Simulink to demonstrate the results and applicability of the proposed method.

Recently, there has been increased use of fuzzy logic in control applications. The kernel of a fuzzy logic controller (FLC) is a fuzzy inference engine which includes the knowledge base, the decision making logic, and the defuzzification unit, as shown in Figure 1. For most of the reported applications, fuzzy inference algorithms of the FLCs were implemented in software and executed on a standard Von Neumann type processor or mucrocontroller. Although software-based FLCs are in general more economical and flexible, they often have difficulty in dealing with control systems that require very high processing and I/O handling speeds. For this reason, in the recent years, a number of hardware fuzzy inference systems have been reported or proposed [1–5, 7, 9–16] that reflect the diversity in fuzzy inference methods in technoloies related to hardware design. In general, analog hardware is relatively simple but lacks accuracy and reliability; on the other hand, digital hardware is accurate and reliable but is more complex and lacks speed if an iterative algorithm is unavoidable. In recent years, rapid advances in digital technology allow system designers to design custom computing machines based on a variety of technologies: full custom application-specific IC (ASIC), standard-cell, gate array, FPGA, programmable logic device (PLD), standard IC, etc.. Among these, FPGA is especially suitable for fast implementation and quick hardware verification. Besides technology alternatives, design decisions have to be made based on tradeoffs on performance, hardware resource anf flexibility. In general, higher performance can be achieved at the expense of more hardware resource and less flexibility.

Attempts are being made to enhance the drive performance by intelligent control using fuzzy logic (FL) and neural network techniques. One of the frequently discussed applications of artificial intelligence in control is the replacement of a standard proportional plus integral (PI) speed controller with an FL or artificial neural network (ANN) speed controller. Regardless of all the work, it appears that a thorough comparison of the drive behavior under PI, FL, and ANN speed control is necessary. This article attempts to compare PI, fuzzy, and ANN controllers that are implemented in an embedded system for closed-loop speed control of DC drive fed by a buck-type DC–DC power converter. The PI controller is designed based on the small signal modeling of the system. The PI-like fuzzy controller structure is considered for comparison. Two ANN controllers are designed. One controller uses training data obtained from the simulation of a fuzzy controller and the other uses training data from the simulation of a PI controller. The performance of the controllers is studied for a variety of operating conditions, such as step change in speed command and step change in load torque. The parameters selected for the comparison are the steady-state error and the rise time of the response. It is shown that ANN speed controllers provide a superior speed response in terms of rise time and the steady-state error compared to PI and FL controllers. This advantage arises from the fact that the neural network has the property of generalization and the control surface of the neural controller is smooth. The designed neural network controller is simple, with three neurons only, and so it is best suited for embedded system implementation. It is also found that the ANN controller trained with the training data from a PI controller has a better response compared to the ANN controller trained with data from a fuzzy controller.

<div class="htmlview paragraph">A Hydrostatic Energy Storage Transmission System(HESTS) designed for vehicle is presented in this paper<span class="xref">*</span>. A bench-scale HESTS was built in the Fluid Power and Control Laboratory of Harbin Institute of Technology. The laboratory testing HESTS was powered by a DC. motor which was controlled by a Silicon-Controlled Rectifier(SCR). Loads adding to the HESTS were regulated by a proportional valve control system.</div> <div class="htmlview paragraph">The HESTS is more complex because there are not only continuous variables-the shaft speed of the hydraulic axial piston variable displacement pump/motor unit and the position of the displacement-varying cylinder, but also discrete variables-the on/off states of clutch, 2/2 solenoid valve and 2/4 solenoid valve in the HESTS to be controlled. So a fuzzy logic controller with a mode selector is designed to perform the role of control to the HESTS. A personal computer stored control rules executes the tasks of data acquisition, fuzzy logic inference and control. Experimental results show that the HESTS had high efficiency and the control strategy was effective.</div>

High efficiency due to reduced losses, low maintenance and low rotor inertia of the brushless dc (BLDC) motor have increased the demand of BLDC motors in high power servo and robotic applications. Since fuzzy logic with human like but systematic property can convert the linguistic control rules based on expert knowledge into automatic control strategy, it can be well applied to control the systems with uncertain or unmodelled dynamics. In view of fuzzy logic merits, this paper proposes three types of fuzzy logic controllers for BLDC motor drive using advanced simulation model and presents a comparative study of performance specifications of classical PI and PID controllers and three fuzzy logic controllers. The three fuzzy logic controllers considered are PI-like fuzzy logic controller (PI-Like FLC), Hybrid fuzzy logic controller (HFLC) and integrated fuzzy logic controller (IFLC). The steady state and dynamic characteristics of speed and torque are effectively monitored and analyzed using the proposed model. The aim of fuzzy logic controllers is to obtain improved performance in terms of disturbance rejection or parameter variation than obtained using classical controllers. In the HFLC, the proportional term of the traditional PID controller is replaced with an incremental fuzzy logic controller. For the PI-Like FLC, the output of the controller is modified by a rule base with the error and change of error of the controlled variable as the inputs. The IFLC is constructed by using Fuzzy logic controller and PID controller. A performance comparison of classical and fuzzy logic controllers has been carried out by several simulations at different speeds and different load conditions.

DVRs commonly use PI controllers. In spite of the fact that the required Proportional Integral controller does not alone provide the output, by using gains which are fixed when there is a variations in the operating conditions or system parameters. This problem is solved by an adaptive Proportional integral with fuzzy logic. The fuzzy controller will adjust the error and rate of error of the Proportional Integral controller according to respective readings and fuzzy control rules, in order to get adjusted to any operating conditions variations. The corresponding results of simulation proved that the method of adaptive Proportional Integral with fuzzy logic control considerably improves the DVR performance as compared to the normal Proportional Integral controller.

Fuzzy logic control based on the Takagi–Sugeno inference method has been applied for the yeast alcoholic fermentation running in a continuous-time biochemical reactor in this paper. A type-1 fuzzy PID controller was designed to temperature control in a biochemical reactor. The fuzzy PID controller was also designed using type-2 fuzzy sets. The advantage of the fuzzy control is that it can be used very successfully for control of strongly non-linear processes and processes that are difficult to model because of complicated reaction kinetics. Obtained simulation results confirm this fact. The disadvantage of the fuzzy control design lies in the time-consuming tuning of controllers. The subtractive clustering method was used to identify the rule base. This approach was chosen to minimize the number of rules of the designed fuzzy logic controllers and to simplify the fuzzy controller design. Simulation results confirm that fuzzy PID controllers can assure better performance than conventional PID controllers. Yeast alcoholic fermentation is an exothermic process and the cooling is necessary to maintain the optimal temperature in the reactor. Fuzzy PID control reduces coolant consumption in comparison with conventional PID control and so it assures energy-efficient control of continuous-time alcoholic fermentation. Using type-2 fuzzy sets in the fuzzy PID controller design is very promising as the type-2 fuzzy controller offers even better results than the type-1 fuzzy controller.

This paper presents the design of a novel fuzzy logic based automatic caution order system for Indian Railways. The Artificial Intelligent Fuzzy Logic Controller (FLC) is used. Simulation is done in Matlab-Simulink to control speed of the train as per the caution order. A `caution data processor' subsystem is introduced to switch from manual to fuzzy control of the traction motor at caution regions of the track & sets the speed limitation as per caution order. FLC gets activated at Caution point by the Caution data Processor and gets deactivated at the end of caution thereby passing the control of the train to its loco pilot. This system of imposing speed limitations on route sections is made automatic by the introduction of Caution Data Processor and FLC; improving on conventional speed control aspects of the train manually by the loco pilot. FLC Inputs are Speed error and acceleration. An emergency switch is also included to override the fuzzy control in the caution region, if any emergency occurs. Simulation studies were done to check the working of FLC design & the speed control of traction motor is achieved as per Caution Order. Thus FLC & Caution Data Processor provides intelligent & effective speed control of traction motor.

This paper presents a fuzzy logic controller for the closed loop control of BLDC motor. The fuzzy logic controller is developed using a fuzzy logic toolbox in MATLAB. Also here a single current sensor technique is proposed for closed loop current control. For closed loop current control of BLDC motor, the motor phase currents are measured using current sensors. These sensors are expensive and the use of different current sensors can cause undesirable imbalance in phase currents due to differences in current sensor sensitivities. These drawbacks can be avoided by using a single current sensor placed on the DC link. Here an algorithm is presented to obtain the phase current values from the DC link current. The proposed system was simulated using MATLAB/Simulink, with PI and fuzzy logic speed controllers. The simulation result shows that, the fuzzy logic controller gives better performance than PI controller. The hardware implementation of the fuzzy logic controller is done using PIC16F877A. The hardware results show that the fuzzy logic controller gives a smooth speed control.

This paper presents fuzzy logic based velocity feedback control for active vibration control of beams. The controller is first developed for a single degree of freedom spring mass system. Rule base consisting of three simple rules based on velocity is used. It is found theoretically as well as experimentally, that for the same settling time maximum applied force required by fuzzy logic controller is much less than that required by direct negative velocity feedback control. The fuzzy controller so developed is then applied for active vibration control of beams. The controller is implemented experimentally on a test beam and the results are found satisfactory. The test system consists of a cantilevered beam with piezoelectric sensor and actuator patches mounted in collocated fashion. The fuzzy logic controller is based on modal velocity of the beam. Modal velocity of the beam acts as an input to the fuzzy controller and actuation force is output from the inference engine. The issues related to design of fuzzy logic controller based on velocity are discussed.

The Mamdani fuzzy inference method is one of the most important fuzzy logic control (FLC) techniques and has several applications in different fields. Despite its applications, the Mamdani fuzzy inference method has some core issues which still require solutions. The most critical issue is the selection of accurate shape and boundaries of membership functions (MFs) in the universe of discourse. In this work, we introduced a methodology called learning to control (LtC) to resolve the problem. The proposed methodology consisted of two main modules, namely, a control algorithm (CA) module and a learning algorithm (LA) module. In the CA module, the Mamdani FLC method has been used, whereas, in the LA module, we have used the artificial neural network (ANN) algorithm. Inputs into the ANN were the error difference between environmental temperature and the required temperature. The output of the ANN was the MF set to the FLC. Inputs into the fuzzy logic controller (FLC) were the error difference between environmental temperature and required temperature (D), and the output was the required power for the fan actuator. The purpose of the ANN was to tune the MFs of the FLC to improve its efficiency. The proposed learning-to-control method along with the conventional fuzzy logic controller method was applied to the data to evaluate the model’s performance. The results indicate that the proposed model’s performance is far better than that of conventional fuzzy logic techniques.

This book presents a systematic framework targeting at fuzzy modeling and fuzzy control of nonlinear systems with uncertainties. The book is organized into three major parts incorporating 13 chapters. The first part contains four chapters focusing on the modeling of nonlinear dynamical systems by using fuzzy logic. The second part includes five chapters in which fuzzy inference and control techniques are addressed. The final part consists of four chapters, which covers several advanced topics in fuzzy control ranging from controller and filter design to chaotification of fuzzy systems and feedforward fuzzy control of nonlinear systems via Fourier integrals. One of the major contributions of this book is that it presents the latest original research developments in the field. It provides rich examples and references, too. It is a valuable resource for those researchers and practitioners interested in expanding their knowledge from fuzzy logic and applications to nonlinear dynamical systems.

The asynchronous machine has qualities that make it a preferred candidate for variable speed drives, but this simplicity is accompanied by great physical complexity due to the electromagnetic interactions between the stator and the rotor. This paper presented a comparison between fuzzy PI logic and PI traditional controllers based for the speed control of the asynchronous machine (ASM); the analysis was achieved in MATLAB/SIMULINK environment. At first, it described the ASM indirect vector control, which consists in eliminating the coupling between the armature and the inductor to reduce its operation comparable to that of a DC machine. In addition, it described the PI controller; the fuzzy logic controller that does not deal with a well-defined mathematical relationship, but uses inferences with multiple rules control, then, compared their performance. The results of all the controllers have been compared. The machine started at t = 0.1 s from zero speed to t = 0.4 s at no load, and at t = 5 s, a load torque of T = 10 N m was applied to observe the controllers’ performance. As for the comparison of the controllers’ speed response, it can be observed that the PI controller has the highest and that the fuzzy PI has the lowest overshoot of (rad/s). Besides, the settling time for the fuzzy PI is lower than that of PI. The results showed the effectiveness and robustness of the fuzzy control system compared to PI controller for indirect vector control of asynchronous machine.KeywordsAsynchronous machine (ASM)Indirect field-oriented control (IFOC)PI controllerFuzzy logic controllerVector control

Fuzzy logic controllers are designed for spacecraft hovering flight in a binary asteroid system. The binary asteroid system is modeled as an ellipsoid-sphere system where the spherical harmonics method is adopted to represent the gravitational field of the asteroids. The method to design the fuzzy logic controllers is introduced in detail. Hovering flight about the inner collinear equilibrium point L 1 of binary asteroid Didymos is numerically presented. Detailed steps on roughly estimating the scaling gains of fuzzy logic controllers are also summarized. Numerical simulations are performed to show the effectiveness of the proposed method. Moreover, the aforementioned fuzzy logic controllers can be used for both continuous and impulsive thrusters. The fuzzy controllers with continuous thrust, the fuzzy controllers with fixed thrust, and the other two kinds of sliding-mode controllers are optimized in the same way and compared in the same simulation environment together, to show tremendous advantages of fuzzy logic controllers and continuous thrusters on both control performance and propellant consumption.