Design Of Fuzzy Logic Controller Research Articles

Disturbance Compensation in Fuzzy Logic Control of Level in Carbonisation Column for Soda Production

The intelligent approaches emerge as leading techniques in providing of stable and high performance control of industrial plants with nonlinearity, model uncertainty, variables coupling and disturbances. In the present research a novel approach for the design of a nonlinear model-free fuzzy logic controller (FLC) with two inputs – the system error and the main measurable disturbance and a rule base for disturbance compensation is suggested. It is based on off-line parameter optimisation via genetic algorithms. The approach is applied for the development of a FLC for the control of the level of ammonia brine solution in a carbonisation column with compensation of the changes in the inflow pressure. The control algorithm is implemented in a general purpose industrial programmable logic controller in ”Solvay Sodi” SA – Devnya, Bulgaria. The FLC system with disturbance compensation outperforms in an increased dynamic accuracy the FLC with the system error as a single input even when linear feedforward disturbance compensation is added. The performance of all systems is assessed from the real time control and the simulations based on a derived TSK plant model.

Artificial neural network model and fuzzy logic control of dissolved oxygen in a bioreactor

In a fermentation process, dissolved oxygen is the one of the key process variables that needs to be controlled because of the effect they have on the product quality. In a penicillin production, dissolved oxygen concentration influenced biomass concentration. In this paper, multilayer perceptron neural network (MLP) and Radial Basis Function (RBF) neural network is used in modeling penicillin fermentation process. Process data from an industrial scale fed-batch bioreactor is used in developing the models with dissolved oxygen and penicillin concentration as the outputs. RBF neural network model gives better accuracy than MLP neural network. The model is further used in fuzzy logic controller design to simulate control of dissolved oxygen by manipulation of aeration rate. Simulation result shows that the fuzzy logic controller can control the dissolved oxygen based on the given profile.

Design of fuzzy logic controller for load frequency control in an isolated hybrid power system

As the world is moving towards green energy generation to reduce the pollution by renewable sources such as wind, solar, geothermal and more. These sources are intermittent in nature, to coordinate and control with traditional power generating units a control technique is necessary. This paper mainly focuses on the design of fuzzy based classical controller using a PSO algorithm for optimal controller gains to control the frequency variations in island hybrid power system. The considered mathematical model comprises of a diesel generating model, wind turbine generator and a battery storage system. Fuzzy is an intelligent controller which is designed with trial and error rules or on the basis of past experience provided by experts or by optimization methods for optimized gains using computational algorithms. To give best solution for these kinds of problems with FLCs traditional controllers are integrated with fuzzy logic. The PSO algorithm is applied to tune the classical controller gains to decrease the frequency deviation of the island power system, during the different load and wind disturbances. The Fuzzy PID classical controller shows the best performance compared with the only fuzzy and Fuzzy-PI controller configurations by illustrating the under shoot, overshoot and settling time and the proposed method is robust for various loading conditions and different wind changes.

Manufacturing System Optimization with Lean Methods, Manufacturing Process Objectives and Fuzzy Logic Controller Design

This paper presents an approach for the integration of lean methods and manufacturing process objectives through the usage of a fuzzy logic controller. The fuzzy logic controller comprises twelve common lean methods, three manipulated variables (setup time, error rate and technical availability) and five manufacturing process objectives, which are operationalized through the target variables Every Part Every Interval (EPEI), Overall Equipment Effectiveness (OEE), Lead Time (LT), Quality Grade (QG) and Delivery Service (DS). The basic structure of the fuzzy logic controller design and the modeling of the optimization effect on the manufacturing process level is shown.

Optimization of fuzzy logic controller parameters using modern meta-heuristic algorithm for gantry crane system (GCS)

Robust control of underactuated nonlinear systems is a challenging task using conventional methods because of uncertainties which may lead to failure especially for mechanical and robotic applications. Fuzzy logic controller (FLC) is one of the methods specifically used for designing a robust controller that is able to deal with nonlinearity and achieve satisfactory performance for control applications. FLC uses heuristic information or expert operator knowledge to create its mechanism. However, nonlinearities exist in the control systems make its rule-based FLC design a non-trivial task. Manual trial and error tuning method is common way used for tuning the parameters of FLC to achieve the desired performance of the system. Hence, an automatic tuning using optimization algorithm is necessary. Meta-heuristic optimization algorithms are applied to tune the parameters of FLC automatically. In this paper, three popular meta-heuristic algorithms are used to optimize the scaling factors of FLC, i.e. Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Differential Evolution (DE) to improve the effectiveness of FLC design. A gantry crane system, which is a nonlinear system, is used as test system for this project. The simulation of the optimization of FLC parameters by using meta-heuristics algorithms was successfully achieved. The simulation results show that the optimization techniques for FLC are effective to improve the performance of conventional FLC for gantry crane control, i.e. position control and anti-swing control. In addition, PSO performs better than CS and DE in optimization of FLC for gantry crane system.

Processor-in-the-loop co-simulations and control system design for a scaled autonomous multi-wheeled combat vehicle

This paper describes the design and implementation of PID and fuzzy logic controllers for tracking the desired heading angle for a scaled autonomous multi-wheeled combat vehicle (AMWCV). The challenge of designing these control systems is to control the steering of the front four wheels individually in order to obtain the correct heading angle of the vehicle. The performance of the developed controllers is validated in the presence of noise and disturbance in order to evaluate the irrobustness. A processor-in-the-loop (PIL) co-simulation is conducted to permit and achieve amore realistic situation where the developed control algorithms are evaluated while running on a dedicated processor. The obtained results from both simulation and PIL are compared. This comparison will demonstrate the controller effectiveness in tracking the desired heading angles.

Design of Interval Type-2 Fuzzy Logic Controllers Based on Several Evolutionary Optimization Algorithms for Inverted Pendulum System

Design of Interval Type-2 Fuzzy Logic Controllers Based on Several Evolutionary Optimization Algorithms for Inverted Pendulum System

Processor-in-the-loop co-simulations and control system design for a scaled autonomous multi-wheeled combat vehicle

This paper describes the design and implementation of PID and fuzzy logic controllers for tracking the desired heading angle for a scaled autonomous multi-wheeled combat vehicle (AMWCV). The challenge of designing these control systems is to control the steering of the front four wheels individually in order to obtain the correct heading angle of the vehicle. The performance of the developed controllers is validated in the presence of noise and disturbance in order to evaluate the irrobustness. A processor-in-the-loop (PIL) co-simulation is conducted to permit and achieve amore realistic situation where the developed control algorithms are evaluated while running on a dedicated processor. The obtained results from both simulation and PIL are compared. This comparison will demonstrate the controller effectiveness in tracking the desired heading angles.

A survey of Type-2 fuzzy logic controller design using nature inspired optimization

In this paper, we are presenting a survey of research works dealing with Type-2 fuzzy logic controllers designed using optimization algorithms inspired on natural phenomena. Also, in this review, we analyze the most popular optimization methods used to find the important parameters on Type-1 and Type-2 fuzzy logic controllers to improve on previously obtained results. To this end have included a summary of the results obtained from the web of science database to observe the recent trend of using optimization methods in the area of optimal type-2 fuzzy logic control design. Also, we have made a comparison among countries of the network of researchers using optimization methods to analyze the distribution and impact of the papers.

Design and Simulation of Fuzzy Logic Controller for Variable Frequency Drive of Three Phase AC Induction Motor

Design and Simulation of Fuzzy Logic Controller for Variable Frequency Drive of Three Phase AC Induction Motor

Maximum Power Point Tracking of Photovoltaic System Using Perturb and Observe and Fuzzy Logic Controller Techniques

Nowadays, there is trending toward utilizing renewable resources of energy, due to the massive shortage in fossil fuels, in addition to the environmental pollution caused by burning such fuels which in turn releases carbon dioxide. Photovoltaic systems are regarded as one of the most significant renewable energy resources, so it is required to enhance the performance of such systems. The performance of photovoltaic system can be enhanced by utilizing Maximum Power Point Tracking (MPPT) techniques by continuously tracking the maximum power point under changeable atmospheric conditions, as the characteristics of the photovoltaic systems depend nonlinearly on both solar radiance and ambient temperature. In this paper two MPPT techniques, Perturb and Observe algorithm (P&O) and fuzzy logic controller (FLC), are used to control the photovoltaic systems to reach to the maximum power point. Using the MATLAB program A simulation of a 250-watt photovoltaic module is done to justify that the proposed FLC design accomplishes better performance than P&O technique under various atmospheric conditions.

Experimental Design and Implementation of IT2FL-controlled BLDCM Based on LabVIEW™

This article presents design and implementation of Interval Type-2 Fuzzy Logic Controller (IT2FLC) for speed control of brushless DC motor (BLDCM) based on LabVIEW package. For the purpose of comparison, the performance of IT2FLC is compared to Type-1 FL controller (T1FLC). For fair comparison, both schemes of FL controllers are set with the same number of membership functions and input-output gain values. The robustness characteristics of both FL controllers are assessed in terms of dynamic performance and load rejection capaility. The design of IT2FLC and T1FLC are made and implemented within LabVIEW environment and the effectiveness of controllers are verified experimentally. The experimental results showed that IT2FL controller gives better robustness under load variation than conventional T1FL controller.

An interval type-2 fuzzy logic controller design method for hydraulic actuators of a human-like robot by using improved drone squadron optimization

Hydraulic actuator becomes an increasingly concerned driver for human-like robots. However, its dynamic performance under the control should be still further improved because hydraulic system is a typical nonlinearity system. Interval type-2 fuzzy logic controller is an advanced control method featured with high performance to deal with uncertain and nonlinear dynamics, so designing an interval type-2 fuzzy logic controller for the control of hydraulic is a feasible method. In this article, an improved drone squadron optimization-based approach is proposed to optimize interval type-2 fuzzy logic controller parameters. To verify the feasibility and priority of improved drone squadron optimization, a comparison on three different typical plants including proportional-derivative (PD) system, proportional-integral (PI) system, and PI nonlinear system between improved drone squadron optimization and other meta-heuristic algorithms is carried out. Simulation results demonstrate that improved drone squadron optimization not only gets an appropriate interval type-2 fuzzy logic controller for system control but also outperforms other popular algorithms in accuracy of performance.

FLC and PWM SMC for KY Boost Converter

This paper presents a design and implementation of fuzzy logic controller (FLC) plus PWM sliding mode controller (SMC) for 2nd order-d KY positive output voltage boost converter (KYPOVBC) operated in continuous inductor current mode (CICM). It is more suitable for steady power source in liquid crystal display (LCD), I-pad, CCTV camera, computer parts, light emitting diode (LED), renewable energy and industrial application. The 2nd order-d KYPOVBC always works in CICM and replica of synchronous rectification characteristics. The ON/OFF nature of the 2nd order-d KYPOVBC is nonlinear and their dynamic performance becomes poor. The classical linear controllers are noncapable of output voltage regulation of this converter during the high input supply voltage and load disparities. With the purpose of improving dynamic performance, an output voltage and inductor current regulation of the 2nd order-d KYPOVBC, a SMC plus FLC is designed. The state-space equations of the 2nd order-d KYPOVBC are arrived and then, SMC parameters are calculated. The FLC rules are framed according to the working nature of the 2nd order-d KYPOVBC without mathematical modeling, which is one of the major advantages of the FLC. The SMC acts as an inside loop of this converter to regulate the inductor current, whereas the FLC and proportional integral (PI) controllers act as an outside loop of the same converter for controlling the output voltage. The performance of the designed model is investigated at various operating regions by developing both the experimental and matrix laboratory (MATLAB)/simulation link (Simulink) models in comparison with the SMC plus PI controller. The results are presented to show the best performance of the designed model.

Design and industrial implementation of fuzzy logic control of level in soda production

The linear control techniques often fail to satisfy the high standards in the control of the contemporary complex plants accounting for the emerging importance of the plant nonlinearity, model uncertainty, variables coupling and disturbances from the industrial environment. The fuzzy logic controller (FLC) successfully ensures control system stability, robustness and desired performance in the whole range of plant operation. Despite the rapid theoretical development the reported FLC industrial applications are still only few. The aim of the present investigation is to design and implement a model-free FLC for the liquid level in a carbonisation column in soda ash production using a low-cost general purpose industrial programmable logic controller (PLC). The novelty is the design of a single-input-single-output FLC, the FLC transformation to enable its programming in a PLC and implementation of the PLC-FLC for the real time control of the level in a carbonisation column in “Solvay Sodi” SA – Devnya, Bulgaria. The conducted real time experiments on the industrial plant show that the FLC system outperforms the existing model-based fuzzy parallel distributed control system in an increased dynamic accuracy and a reduced settling time.

An intelligent approach for dynamic load frequency control with hybrid energy storage system

ABSTRACTOne of the important factors to be considered in an electric power system is to maintain the frequency constant, so that power stations run satisfactorily in synchronise among the connected pool of power generation systems. The main purpose of load frequency control (LFC) is to reduce the frequency deviation and net interchange of power flow between electrical power generating units and for achieving steady-state error to be zero. This research paper proposes the way to improve the LFC by employing energy storage systems (battery energy storage systems and superconducting magnetic energy storage (SMES)) during change in load demand. The energy storage devices are acting quickly and they are effectively damp electro mechanical oscillations in a power system. Moreover, they provide storage capacity and the kinetic energy of the generator shares the sudden changes in power requirement. An integrated hybrid BES and SMES energy storage systems along with LFC minimise the deviation of frequency by contribution of additional power requirement. Investigating the performance of the LFC with storage system under step change of load (∆Pd) has been analysed by employing proportional-integral (PI) and fuzzy logic controller (FLC) for frequency regulation and load tracking. Even, the PI controller attains zero steady-state error in system frequency, it has unsatisfied dynamic (overshoots and settling time) performance followed by disturbances. The mismatch power in the power system makes frequency deviation followed by the change in load demand and electrical power generation and it is balanced by employing of an energy storage system with proper controller. The simulation results are illustrated that the performance of LFC system is enhanced by BES storage system which effectively reduces frequency deviation than SMES system. The comparative analysis has been made for LFC with BES, SMES and hybrid storage system employing FLC and PI controller design also. A significant improvement in the frequency profile has been obtained with FLC controller than PI controller followed by the step load changes.

Design of fuzzy logic controller for temperature control of small-scale food storage

A Fuzzy Logic Controller (FLC) is developed to control the temperature of small-scale food storage. The FLC has two components such as the algorithm part and the hardware part. For the algorithm part, The FLC takes two input parameter such as the temperature and the humidity of the storage and takes one output parameter such as the speed of the fan. The membership function is designed as following hot, fair, cold, and too cold as for the membership functions of the temperature parameter. For the humidity membership functions are dry, dry enough, normal, less moist, and moist. As for the fan speed, the membership functions are fast, less fast, fair, slow, and too slow. On the other hand, for the hardware, the DHT11 senses the temperature and the humidity. For the cooling process, thermoelectric is used to decrease the temperature. The results show that the FLC can be used to control the temperature of the storage to achieve as the minimum temperature as 12.5°C.

Dynamic parameter adaptation in the harmony search algorithm for the optimization of interval type-2 fuzzy logic controllers

At the present time there are several types of metaheuristics which have been used to solve various types of problems in the real world. These metaheuristics contain parameters that are usually fixed throughout the iterations. However, various techniques exist to adjust the parameters of an algorithm such as probabilistic, fuzzy logic, among others. This work describes the methodology and equations for building Triangular and Gaussian interval type-2 membership functions, and this methodology was applied to the optimization of a benchmark control problem with an interval type-2 fuzzy logic controller. To validate in the best way the effect of uncertainty we perform experiments using noise (Pulse generator) and without noise. Also, a statistical z-test is presented to verify the effectiveness of the proposed method. The main contribution of this article is the proposed use of the theory of interval type-2 fuzzy logic to the dynamic adjustment of parameters for the harmony search algorithm and then its application to the optimal design of interval type-2 fuzzy logic controller.

Intuit before tuning: Type-1 and type-2 fuzzy logic controllers

Although a considerable amount of effort has been put in to show that fuzzy logic controllers have exceptional capabilities of dealing with uncertainty, there are still noteworthy concerns, e.g., the design of fuzzy logic controllers is an arduous task due to the lack of closed-form input–output relationships which is a limitation to interpretability of these controllers. The role of design parameters in fuzzy logic controllers, such as position, shape, and height of membership functions, is not straightforward. Motivated by the fact that the availability of an interpretable relationship from input to output will simplify the design procedure of fuzzy logic controllers, the main aims in this work are derive fuzzy mappings for both type-1 and interval type-2 fuzzy logic controllers, analyse them, and eventually benefit from such a nonlinear mapping to design fuzzy logic controllers. Thereafter, simulation and real-time experimental results support the presented theoretical findings.

A simplify fuzzy logic controller design based safe experimentation dynamics for pantograph-catenary system

Contact force between catenary and pantograph of high speed train is a crucial system to deliver power to the train. The inconsistence force between them can cause the contact wire oscillate a lot and it can damage the mechanical structure of system and produce electric arc that can reduce the performance of system. This project proposes a single-input fuzzy logic controller (SIFLC) to control the contact force between the pantograph-catenary by implement Safe Experimentation Dynamics (SED) method to tune the SIFLC parameters. The essential feature of SIFLC is that it is model-free type controller design with less pre-defined variables as compared to other existing model-based controllers. The performance of the SIFLC is analyzed in terms of input tracking of contact force of pantograph-catenary and time response specifications. A simplified model of three degree of freedom (3-DOF) pantograph-catenary system is considered. In this study, the simulation result shows that the SIFLC successfully track the given contact force with less overshoot with percentage different of peak to peak response from actual force 2% and fast response within 5.27s