Modified P&O-Fuzzy Type-2 Variable Step Size MPPT for PEM Fuel Cell Power System

Design of a methanol reformer for on-board production of hydrogen as fuel for a 3 kW High-Temperature Proton Exchange Membrane Fuel Cell power system

Conventional and advanced exergy analyses of a vehicular proton exchange membrane fuel cell power system

This paper presents a comparision between two controllers for Proton Exchange Membrane Fuel Cell PEMFC power system. The aim of this work is to keep the PEMFC working at its MPP by acting on the DC/DC converter, and ensure an adequate robustness against load and inputs variations. Conventional controller PI and backstepping technique are applied to the DC/DC boost converter. The stability of the backstepping is demonstrated by means of Lyapunov analysis. The model and the controllers are implemented in the MATLAB and SIMULINK environment. Simulation results indicate that the aim of this work is achieved using the backstepping algorithm.

Experimental campaign and assessment of a complete 240-kW Proton Exchange Membrane Fuel Cell power system for maritime applications

Photovoltaic (PV) is one of the renewable sources of energy which is used widely in industry and distribution companies consumers. Nonlinear behavior of photovoltaic panels and their output power dependency on the atmospheric conditions motivate researchers to focus on extracting maximum available power from PV panels in order to reduce the cost and maximize the efficiency. In other words, output power of PV panel is dependent on I-P and V-P characteristics and every irradiation and surface temperature of PV. Therefore, maximum power point tracking requires to be addressed to reduce transient time, power fluctuations and increase the efficiency. This is realized by controlling switching elements in DC-DC boost convertor transferring energy from PV panel to the load. This paper presents a new method in maximum power point tracking (MPPT) of PV system based on fuzzy type-2 controller, and also compares its performance with MPPT of fuzzy logic controller type one (FLC1). The PV system composed of a PV panel, DC-DC boost convertor, FLC unit and a DC load, is simulated in Matlab /Simulink. Also, experimental tests have been done for validation. Finally, the simulation results reveal that the FLC2 has better performance in providing MPPT.

In this study, we will present the novel application of Type-2 (T2) fuzzy control into the popular video game called flappy bird. To the best of our knowledge, our work is the first deployment of the T2 fuzzy control into the computer games research area. We will propose a novel T2 fuzzified flappy bird control system that transforms the obstacle avoidance problem of the game logic into the reference tracking control problem. The presented T2 fuzzy control structure is composed of two important blocks which are the reference generator and Single Input Interval T2 Fuzzy Logic Controller (SIT2-FLC). The reference generator is the mechanism which uses the bird's position and the pipes' positions to generate an appropriate reference signal to be tracked. Thus, a conventional fuzzy feedback control system can be defined. The generated reference signal is tracked via the presented SIT2-FLC that can be easily tuned while also provides a certain degree of robustness to system. We will investigate the performance of the proposed T2 fuzzified flappy bird control system by providing comparative simulation results and also experimental results performed in the game environment. It will be shown that the proposed T2 fuzzified flappy bird control system results with a satisfactory performance both in the framework of fuzzy control and computer games. We believe that this first attempt of the employment of T2-FLCs in games will be an important step for a wider deployment of T2-FLCs in the research area of computer games.

Higher order nonlinear systems with prevailing delay have been very challenging in significance with stability and process performance. This paper investigates the performance of a type-2 Mamdani intelligent controller implemented a delay dominant system which is modelled using black box approach identified to be a second order nonlinear model for a dual spherical tank liquid level system (DSTLLS) under LabVIEW environment. The adaptive approach of the intelligent Mamdani-based fuzzy controller proves itself to be very competent compared with the earlier experimented methods that already exist. The performance indices like integrated absolute error (IAE) and integrated squared error (ISE) are also calculated for different varying set point changes of DSTLLS. The response and error reduction efficiency of this method of adaptive type-2 intelligent fuzzy (ATIF) controller has been experimented for different flow configurations of this DSTLLS, like multiple input single output (MISO), multiple input multiple output (MIMO) and single input single output (SISO).

Fuzzy controllers and fuzzy sliding-mode controllers have found extensive use in a variety of applications. Generally, type-1 fuzzy sets are used for the membership functions of these controllers. However, in real-time applications, uncertainty associated with the available information always occurs. A type-2 fuzzy controller and a type-2 fuzzy sliding-mode controller are proposed that are able to solve the problem that the words used in the inference rules can mean different things to different people. Since the membership functions use type-2 fuzzy sets then, the proposed control schemes can handle the rule uncertainties when the operation is extremely uncertain and/or the membership grades cannot be exactly determined. The proposed control systems are applied to control a buck DC–DC converter. A comparison between a PI controller, a type-2 fuzzy controller and a type-2 fuzzy sliding-mode controller is made. The experimental results show that the type-2 fuzzy sliding-mode controller achieves the best control performance.

Proton exchange membrane fuel cell power systems (PEM FCPS) represent a promising clean energy technology with applications ranging from transportation to stationary power generation. Honeywell Aerospace is actively developing PEM FCPS for unmanned vehicles (UVs), focusing on the unique requirements of this application. While the engineering principles of PEM FCPS are similar to those for heavy-duty applications, there are critical differences in requirements that must be addressed for UV integration. The three primary requirements are weight reduction, high power density, and overall system efficiency.Reducing the weight of a PEM FCPS involves integrating newer, lighter materials for both the stack and balance-of-plant (BOP) components, as well as combining functionalities to eliminate excess weight. For example, 3D printing connected parts instead of using connectors can significantly reduce weight. Achieving high power density focuses on three key areas of the Membrane Electrode Assembly (MEA): enhancing catalyst kinetics, improving proton conductivity through the membrane, and optimizing water management within the UV system. The main challenge is to increase power density without adding additional weight to the system. This involves balancing the number of cells in the fuel cell stack and optimizing BOP components such as humidifiers and blowers, which may need to be smaller to reduce weight but still provide adequate gas feeds. Overall system efficiency is achieved by optimizing the functionality of every component, particularly within the fuel cell stack. This includes designing flow fields to achieve low pressure drops and ensuring efficient operation of all system components.This abstract presents Honeywell Aerospace’s efforts in designing PEM FCPS to meet these three key requirements, facilitating the adoption of PEM FCPS in the UV market. Figure 1

The purpose of this research is to apply proton membrane fuel cells to toys, and assess its feasibility. The study uses Taguchi Methods to analyze the "Proton Exchange Membrane Fuel Cell Power Supply and Work Module for Toys". The study assembled the proton exchange membrane fuel cell power supply module in the "Toy of Hydrogen-based Energy Module-Ship of Hydrogen-based Energy" and observes its operation state. It finds that the ship moved forward smoothly, and indicates that hydrogen can be used for this toy. This study uses renewable "Solar Energy" matching "the Proton Exchange Membrane" to design the "Hydrogen and Oxygen Fuel Module", which creates the hydrogen and oxygen fuel supply for the "Proton Exchange Membrane Fuel Cell Power Supply and Work Module" for toys using hydrogen-based energy. "Hydrogen-based Energy" will become applied extensive to the toy field in the future by way of this study.

The interval type-2 fuzzy Proportional-Integral (PI) controller (IT2-FPI) might be able to handle high levels of uncertainties to produce a satisfactory control performance, which could be potentially due to the robust performance as a result of the smoother control surface around the steady state. However, the transient state and disturbance rejection performance of the IT2-FPI may degrade in comparison with the type-1 fuzzy PI (T1-FPI) counterpart. This drawback can be resolved via general type-2 fuzzy PI controllers which can provide a tradeoff between the robust control performance of the IT2-FPI and the acceptable transient and disturbance rejection performance of the type-1 PI controllers. In this paper, we will present a zSlices-based general type-2 fuzzy PI controller (zT2-FPI), where the secondary membership functions (SMFs) of the antecedent general type-2 fuzzy sets are adjusted in an online manner. We will examine the effect of the SMF on the closed-system control performance to investigate their induced performance improvements. This paper will focus on the case followed in conventional or self-tuning fuzzy controller design strategies, where the aim is to decrease the integral action sufficiently around the steady state to have robust system performance against noises and parameter variations. The zSlices approach will give the opportunity to construct the zT2-FPI controller as a collection of IT2-FPI and T1-FPI controllers. We will present a new way to design a zT2-FPI controller based on a single tuning parameter where the features of T1-FPI (speed) and IT2-FPI (robustness) are combined without increasing the computational complexity much when compared with the IT2-FPI structure. This will allow the proposed zT2-FPI controller to achieve the desired transient state response and provide an efficient disturbance rejection and robust control performance. We will present several simulation studies on benchmark systems, in addition to real-world experiments that were performed using the PIONEER 3-DX mobile robot that will act as a platform to evaluate the proposed systems. The results will show that the control performance of the self-tuning zT2-FPI control structure enhances both the transient state and disturbance rejection performances when compared with the type-1 and IT2-FPI counterparts. In addition, the self-tuning zT2-FPI is more robust to disturbances, noise, and uncertainties when compared with the type-1 and interval type-2 fuzzy counterparts.

In this study, liquid level control in a coupled tank system is realized with PI controllers whose parameters are adapted using fuzzy logic approximation methods. Type-1 fuzzy PI control, interval type-2 fuzzy PI control and classical PI+Feedforward control methods are applied to the tank system in real time for different reference liquid levels. The performances of the controllers are compared in terms of system response parameters such as rise time, settling time and overshoot percentage. In addition, the performance evaluation of the controllers over a certain period of time is also compared by calculating integral-based concepts that express error performance metrics such as integral square error (ISE), integral time square error (ITSE), integral absolute error (IAE) and integral time absolute error (ITAE). The obtained results show that the type-2 fuzzy PI controller shows the best performance, followed by type-1 fuzzy PI control and classical PI+FF controller, respectively.

This paper focuses on the development of a stable Mamdani type-2 fuzzy logic based controller for automatic control of servo systems. The stability analysis of the fuzzy controller has been done by employing the concept of Lyapunov. The Lyapunov approach results in the derivation of an original stability analysis that can be used for designing the rule base of our proposed online gain adaptive Interval Type-2 Fuzzy Proportional Derivative controller (IT2-GFPD) for servo systems with assured stability. In this approach a quadratic positive definite Lyapunov function is used and sufficient stability conditions are satisfied by the adaptive type-2 fuzzy logic control system. Illustrative simulation studies with linear and nonlinear models as well as experimental results on a real-time servo system validate the stability and robustness of the developed intelligent IT2-GFPD. A comparative performance study of IT2-GFPD with other controllers in presence of noise and disturbance also proves the superiority of the proposed controller.

This paper presents a comparative study between the firefly algorithm (FA) and the galactic swarm optimization (GSO) method, where the performance of both methods is observed and tested in the optimization of a fuzzy controller for path tracking of an autonomous mobile robot. The main contribution of this work is finding the best method that generates an optimal vector of values for the membership function optimization of the fuzzy controller. This with the goal of improving the performance of the controller and thus the trajectory generated by the autonomous robot is closer to the desired trajectory. It should be noted that the fuzzy controller that is optimized is an interval type-2 fuzzy controller, which has a greater capability for managing uncertainty than a type-1 fuzzy controller. In this case, the limiting membership functions in the interval type-2 fuzzy sets are themselves type-1 fuzzy sets that define the footprint of uncertainty. Type-2 fuzzy controllers have been shown in previous works to handle better the control of robotic systems under noisy and dynamic conditions and this is why their optimal design is very important. Simulation results show that GSO outperforms FA in the optimal design of interval type-2 fuzzy controllers.

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.