Genetic synthesis of fuzzy logic controllers in turning

Pressurization control system plays an important role in the process control at the stages of riser-tube filling, mold-filling, pressure-increasing and pressure maintaining in counter-gravity casting. Casting quality is affected by the precision of pressurization control system to a great extent. Input and output scaling factors of conventional fuzzy controller are static, which is hard to ensure a suitable state for various counter-gravity casting equipments. A new fuzzy controller with self-tuning scaling factors has been developed in our laboratory, which will be described in this paper. Input and output scaling factors of fuzzy controller are tuned by different error ranges. When the differential pressure error is large, output scaling factor is increased to speed up dynamic response. At the same time, input scaling factors are also increased in order to reduce steady state error. Compared to fuzzy controller and conventional PID controller, fuzzy-PID controller with self-tuning scaling factors has more steady-state precision and excellent robustness. Experimental results show that at riser-tube filling stage and mold-filling stage the gas flow is stable, and the tracking property of pressurization control system is very satisfactory.

The aim of this paper is to show how to build a fuzzy controller and its membership functions automatically. In a fuzzy logic controller (FLC), the proposed method allows one easily to construct a set of membership functions, called shrinking-span membership functions (SSMFs). The FLC uses Mamdani-type fuzzy controllers for the defuzzification strategy and inference operators. The FLC hardware implementation is performed on an 8-bit microcontroller. Simulation results and experimental results demonstrate that the converter can be regulated with good performance even when subjected to input disturbance and load variation. The presented approach is generally valid for the design of an FLC, and can be applied to any dc–dc converter topologies.

This paper introduces three hybrid methods for the generation and optimization of rules and membership functions of a fuzzy logic controller for nonlinear systems. The proposed methods overcome the deficiency of a systematic approach for optimal design of fuzzy controllers. An optimally designed fuzzy logic controller should have the least number of fuzzy variables and fuzzy rules and the best possible configuration of fuzzy rules in the rule table. The first strategy of this paper is a two-phase optimization problem: in the first phase, the number of fuzzy variables and their arrangement in the rule table are optimized by a genetic algorithm; in the second phase, the parameters of the membership functions are optimized via extended Kalman filtering. The second strategy tries to achieve the goals of the first method all in one phase by modifying the chromosome structure of the genetic algorithm. Then in the next step, a local search algorithm is utilized to improve the obtained results. The third strategy is similar to the second strategy in structure. However, along with optimizing the number of fuzzy variables and membership parameters, the number of fuzzy rules is also optimized. The first and second strategies are obliged to use every possible combination of fuzzy variables in the rule table; however, the third strategy is capable of distinguishing between useful and useless fuzzy rules in the rule table. The introduced strategies are applied to an automotive cruise control system. The results of the simulations show the effectiveness of the proposed methods and the superiority of the latter approaches over the former ones.

With the availability of a wide range of evolutionary algorithms such as genetic algorithms, evolutionary programming, evolution strategies and differential evolution, every conceivable aspect of the design of a fuzzy logic controller has been optimized and automated. Although there is no doubt that these automated techniques can produce an optimal fuzzy logic controller, the structure of such a controller is often obscure and in many cases these optimizations are simply not needed. We believe that the automatic design of a fuzzy logic controller can be simplified by using a generic rule base such as the Mac Vicar-Whelan rule base and using an evolutionary algorithm to optimize only the membership functions of the fuzzy sets. Furthermore, by restricting the overlapping of fuzzy sets, using triangular membership functions and singletons, and reducing the number of parameters to represent the membership functions, the design can be further simplified. This paper describes this method of simplifying the design and some experiments performed to ascertain its validity.

Design of a fuzzy controller requires specification of both membership functions and decision rules. Specification of membership function for a fuzzy logic controller has been an important issue. The traditional way of selecting membership functions has been, in most cases, an adhoc procedure. In this paper, an optimization algorithm based on simulated annealing for designing fuzzy membership functions for fuzzy controllers is introduced. An optimization algorithm for designing the membership functions and fuzzy rule base for a fuzzy controller is presented. An optimal fuzzy controller using proposed technique for backing up a truck problem is designed and implemented, in which optimal fuzzy membership functions and fuzzy rules are designed. Also, a neural network controller for the same truck problem is designed to compare our results.

In power system any unpredictable variations of consumption load will cause fluctuation in frequency and the transferred power between the lines. Load frequency control is one of the main problems in the power system which synchronizes with automatic generation control. Checking the different factors that cause unbalance in power system is necessary and one of the important factors of them is the load that randomly varies. In this paper, a three area power system in deregulated environments with participation of synchronized generation of wind with Doubly Fed Induction Generator (DFIG), hydro and thermal power plants are studied. The load in this power system is considered as a random variable which periodically and randomly has frequency of 0.25 around nominal amount. Fluctuations are caused due to considering load as a random variable and using wind-powered turbine with DFIG in power system. To improve the response of power system, the optimal fuzzy logic, PID controller and FACTS devices will be used. Imperialist Competitive Algorithm (ICA) has been used for optimizing the parameters of a fuzzy logic controller. PID Controller parameters have been optimized with three intelligent algorithms, ICA, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Algorithm and the results will be compared with each other. With comparison the result it is observed that in the case of using optimal fuzzy logic controller (OFLC) we have the fastest response with the minimum over shoot and under shoot, also in this case the transferred power between the areas is settled in the lowest value. The results show a significant reduction in losses in transmission lines.

The aim of this research work is to modulate the pitch angle of both types of wind turbines based on fuzzy logic control (FLC), as changes in the pitch angle have various functions in horizontal and vertical axis wind turbines. For HAWT, pitch angle control is applied to shield the electrical components of the turbine when the wind speed exceeds the rated speed without shutting down the turbine. FLC is used to control the angular velocity using two inputs and one output with three membership functions for both inputs and output. In VAWT, pitch angle control is applied to boost the performance of the turbine and its self-starting torque. FLC utilizes two inputs and one output with five membership functions for both inputs and output. For both turbine types, FLC produces a control signal that drives the actuator to achieve the desired pitch angle. The dynamics of HAWT and VAWT are simulated by the MATLAB/Simulink to demonstrate the influence of pitch controls on their dynamics. For HAWT, the FLC control has successfully maintained the angular speed of the rotor. The values of tip speed ratio and coefficient of performance are reduced in order to maintain the rotor angular velocity at its rated value. On the other hand, the results showed that the torque produced by the VAWT individual blade has improved with the pitch angle control. In addition, using FLC to control the pitch angle gives enhanced output and higher Cp at low tip speed ratios. Gain schedule PI controller is also used in both HAWT and VAWT for comparative study.

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.

In this paper a two-step design methodology of the near optimal Mamdani type fuzzy logic controller (FLC) has been applied to three types of systems: a linear time invariant system (LTI), a LTI system with time delay and an nonlinear system. In the first step, the tuning/learning procedure of a data base/knowledge base of the FLC is based on the model of the system using genetic algorithms (GA). The achieved solution is the optimal one with regard to the model of the system. In the second step experiments are performed on the real system at the vicinity of the optimum (achieved by GA) and using response surface methodology (RSM) control parameters are readjusted in order to achieve the near optimal solution for the real system. The proposed two step methodology gives a systematic way of the near optimal FLC tuning/learning when confronted with the real system and a very efficient combination of off-line and online part of design procedure.

Application of optimal fuzzy and PI hybrid controllers for permanent magnet synchronous generator–based wind energy conversion systems

In this study, interval type2 fuzzy logic (IT2FL) and PID controller is designed for swing-up position control of double inverted pendulum (DIP) system. The double inverted pendulum system consists of two rigid bars connected by a revolute joint. Mass of the revolute joint is included in the dynamic model.Rigid bars in the system are assumed to experience planar motion. The pendulum system is connected to the base by means of a revolute joint. Torque provided through a motor mounted to the base is used for position control of the system. PID (Proportional-Derivative-Integral) and interval type2 fuzzy logic controllers are developed by using the same performance criteria for position control of double inverted pendulum system. IT2FL controller is similar with type1 fuzzy logic controller. IT2FL system provides soft decision boundaries, whereas a type-1 fuzzy logic system provides a hard decision boundary. Membership function in interval type2 fuzzy logic set as an area called Footprint of Uncertainty (FOU) which limited by two type1 membership function those are upper membership function (UMF) and lower membership function (LMF).System behaviour is obtained by computer simulation using developed controllers respectively. Computer simulation results are compared in order to evaluate applicability of developed controllers. MATLAB/Simulink software is used in computer simulations.

Alternative current motors are generally modelled with constant system parameters. However, parameters of the motor show variations during operation due to complex, unidentified and nonlinear system dynamics. Fuzzy logic controllers are widely used as a solution to overcome this problem. Because, fuzzy logic controllers don’t be affected from model uncertainties of the system to be controlled. In this study, permanent magnet synchronous motor that is one of the alternative current motors is controlled with fuzzy logic controller. All stages of the designed controllers are developed by using unique softwares instead of ready toolboxes. Triangular and trapezoidal membership functions with Mamdani and Larsen inference methods are used in the designed fuzzy logic controllers. Speed control performances of the fuzzy logic controllers have been investigated and compared with each other in the simulation studies. According to the obtained results from simulation studies, it is observed that fuzzy logic controllers designed with Larsen fuzzy inference method and trapezoidal input membership function provides better performance.

In the present paper, a Multi-Segment P/f Droop Control Strategy is developed to achieve localized control, coordination and power management of different sources in an islanded microgrid which consists of two PV/Battery storage Hybrid units and a droop unit. Separate control loops are designed, each for PV power production control, Battery charging and discharging level control, and Droop unit operation control based on multi-segment P/f Droop characteristics. The conventional PI controllers can be used in the control loop design as they are very simple for implementation and give a better dynamic response. But their performance deteriorates when the complexity in the system increases due to disturbances like load variations and intermittent sources. Fuzzy Logic Control (FLC) technique is model-independent and a flexible tool. It can deal with complex systems such as microgrids with different types of imprecise inputs and variables particularly if the power is supplied by intermittent sources and is also consumed by varying and unpredictable loads during sudden disturbances. The Membership functions (MF) of a conventional FLC are determined by trial and error method. This method is time-consuming and does not guarantee an optimal controller. In the present paper, a Self-Adaptive Fuzzy Logic Droop Controller (SAFLDC) is proposed, whose input–output MFs are made adaptive by obtaining the cluster centers using Self-Organizing Maps (SOM) Algorithm. The paper aims to follow a systematic approach to propose an effective SAFLDC with optimal MFs whose parameters are tuned using Hybrid GA-PSO. The developed strategy has been validated using detailed switching models in MATLAB/SIMULINK tool and the behavior of key variables in the strategy is illustrated.

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.

The paper proposes a Multi-Objective Particle Swarm Optimization (MOPSO)-based Fuzzy Logic Control Proportional-Integral-Derivative (MOPSO-FLC-PID) controller for stand-alone hybrid Diesel- variable speedWave Energy Conversion System (WECS).The optimal control performance can be achieved by tuning scaling factors of the FLC and gain parameters with Multi-ObjectiveParticle Swarm Optimization (MOPSO) algorithm in order to improve system dynamic performance especially when nonlinearity,uncertainty and complexity of the hybrid Diesel-WECS are involved. Number of objective functions is defined to measurethe performance of the proposed controllers such as minimize the transient DC and AC voltage and current ripples, minimizethe DC and AC voltage and current Total Harmonic Distortion (THD), minimize the absolute total control steady state errordeviations, minimize the RMS absolute voltage deviations at the DC and AC load collection buses, ensure minimal losses aswell as wave energy optimal power utilization. Since some of these objective functions are conflicting, MOPSO is used to finda Pareto front from which a desired optimal operating state can be chosen. For the purpose of comparing the improvementobtained in the system dynamic performance with the application of the MOPSO-FLC-PID procedure to tune the scaling factorsof the FLC and the PID controller gains, these results are compared with those obtained using traditional Ziegler-Nichols PIDapproach, PID type fuzzy logic controller designed with Single-Objective Particle Swarm Optimization (SOPSO-FLC-PID),traditional PID type controller optimized with SOPSO-PID and traditional PID type controller optimized with MOPSO-PID.A complete simulation model is developed for such stand-alone hybrid Diesel-WECS under variable speed operation usingMATLAB Simulink environment. Simulation results show that the proposed design approaches are efficient to find the optimalscaling factors and PID gains of the controllers and therefore improves the transient performance of the hybrid Diesel-WECSover a wide range of operating conditions, sudden load change and three-phase short circuit fault.