Intelligent Control of the Energy Generation Systems

2010 ◽  
pp. 40-96
Author(s):  
Nicu Bizon
Keyword(s):  
Fuzzy Logic ◽  
Fuel Cell ◽  
Intelligent Control ◽  
Fuzzy Logic Controller ◽  
Energy Generation ◽  
Control Law ◽  
Battery Voltage ◽  
Fuel Cell Stack ◽  
Cell Current ◽  
Current Ripple

In this book chapter are analyzed the Energy Generation System (EGS) topologies, used in automotive systems, and the grid inverter systems, with intelligent control algorithms (fuzzy logic controller, genetic algorithm, etc.). The EGS blocks are modelled using Matlab & Simulink ® program. A necessary block is the EGS power interface between the fuel cell stack and the batteries stack, usually a boost converter that uses a Peak Current Controller (PCC) with a Boundary Control with Current Taper (BCCT). The control law is a function of fuel cell current and battery voltage, which prevents the “boiling” of the batteries. The control objective for this power interface is also the fuel cell current ripple minimization, used in order to improve the fuel cell stack life cycle. Clocked and non-clocked control methods are tested in order to obtain a small fuel cell current ripple, better a dynamic response, and robustness against system uncertainty disturbances. The EGS behaviour is tested by bifurcation diagrams. It is shown that performances increase if the control law is a function that depends by the fuel cell current ripple and battery voltage. The clocked PCC using the BCCT 2-D law is implemented by a fuzzy logic controller. The power load dynamic is compensated using an ultracapacitors stack as a dynamic energy compensator, connected by a bi-directional converter to the batteries stack bus. Small fuel cell current ripple using compact batteries and ultracapacitors stacks will be obtained by the appropriate design of the control surface, using an Integrated Fuzzy Control (IFC) for both power interfaces.

Comparison of a Fuzzy Logic Controller With a P + D Control Law

1989 ◽  
Vol 111 (2) ◽  
pp. 128-137 ◽  
Author(s):  
S. Daley ◽  
K. F. Gill
Keyword(s):  
Fuzzy Logic ◽  
Attitude Control ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Good Control ◽  
Control Law ◽  
Dynamic Coupling ◽  
Process Knowledge ◽  
Logic Control ◽  
Limited Process

A study is described that compares the performance of a self-organizing fuzzy logic control law (SOC) with that of the more traditional P + D algorithm. The multivariate problem used for the investigation is the attitude control of a flexible satellite that has significant dynamic coupling of the axes. It is demonstrated that the SOC can provide good control, requires limited process knowledge and compares favorably with the P + D algorithm.

A Nonlinear Fuzzy Logic Controller Developed for an Autonomous Surface Boat

2005 ◽  
Author(s):  
Thomas A. Bean ◽  
Akira Okamoto ◽  
John R. Canning ◽  
Dean B. Edwards
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Optimization Method ◽  
Operating Point ◽  
Linear Feedback ◽  
Control Law ◽  
Linear Feedback Control ◽  
Linear Controller ◽  
Optimal Linear ◽  
Optimal Set

This paper presents an optimized nonlinear fuzzy logic controller designed for an autonomous surface craft and describes the process by which it was found. The nonlinear fuzzy logic controller described herein was developed to maintain the linear feedback control of an optimal set of controller gains when the state is near the operating point. The simplex optimization method was utilized to find the optimal fuzzy logic parameters that define the shape of the control law away from the normal operating point. The resultant controller showed approximately a 20% improvement over the optimal linear controller.

An investigation of fuzzy logic control of flexible robots

Robotica ◽  
1993 ◽  
Vol 11 (4) ◽  
pp. 363-372 ◽  
Author(s):  
Yueh-Jaw Lin ◽  
Tian-Soon Lee
Keyword(s):  
Fuzzy Logic ◽  
Control Algorithm ◽  
Fuzzy Logic Controller ◽  
Robot Manipulator ◽  
Nonlinear Effects ◽  
Control Law ◽  
Flexible Robot ◽  
Control Rules ◽  
Logic Control ◽  
The Mathematical Model

SUMMARYIn this paper a control law, which consists of a fuzzy logic controller plus a nonlinear effects negotiator for a flexible robot manipulator, is presented. The nonlinear effects negotiator is used to enhence the control system's ability in dealing with the uncertainty of the mathematical model. The control algorithm is simple and easy to tune as opposed to conventional control law which requires time consuming gains selections. To obtain fuzzy control rules, an error response plane method is proposed.

scholarly journals Robust Longitudinal Aircraft- Control Based on an Adaptive Fuzzy-Logic Algorithm

2002 ◽  
Vol 7 (1) ◽  
pp. 187
Author(s):  
Abdel- Latif Elshafei
Keyword(s):  
Fuzzy Logic ◽  
Feedback Linearization ◽  
Fuzzy Logic Controller ◽  
Control Law ◽  
Adaptive Fuzzy ◽  
Highly Nonlinear ◽  
Short Period ◽  
Wide Range ◽  
Modeling Errors ◽  
Adaptive Fuzzy Logic

To study the aircraft response to a fast pull-up manoeuvre, a short period approximation of the longitudinal model is considered. The model is highly nonlinear and includes parametric uncertainties. To cope with a wide range of command signals, a robust adaptive fuzzy logic controller is proposed. The proposed controller adopts a dynamic inversion approach. Since feedback linearization is practically imperfect, robustifying and adaptive components are included in the control law to compensate for modeling errors and achieve acceptable tracking errors. Two fuzzy systems are implemented. The first system models the nominal values of the system’s nonlinearity. The second system is an adaptive one that compensates for modeling errors. The derivation of the control law based on a dynamic game approach is given in detail. Stability of the closed-loop control system is also verified. Simulation results based on an F16-model illustrate a successful tracking performance of the proposed controller.   

scholarly journals Interval type-2 fuzzy logic controller design for distillation column process

2019 ◽  
Vol 8 (3) ◽  
pp. 155
Author(s):  
Mohammed Y. Hassan ◽  
Sebal S. Ezzaten
Keyword(s):  
Fuzzy Logic ◽  
Intelligent Control ◽  
Distillation Column ◽  
Fuzzy Logic Controller ◽  
Molar Fraction ◽  
Absolute Error ◽  
Set Point ◽  
Oil Refineries ◽  
Interval Type

Distillation columns are the most units used in oil refineries, and chemical factories. This is a very difficult process and non-linear. Therefore, <br /> the development of intelligent control systems for the columns of <br /> the distillation is very difficult. In this paper, an intelligent control strategy using Mamdani type Interval Type-2 PI Like Fuzzy Logic Controller (IT2FLC) is used. The controller consists of PD-Like FLC with integrated output. Kernek Mendel (KM) algorithm is used as the type reduction method for the IT2FLC. This controller is applied to control a continuous binary trays distillation column. The controller has three tunable gains to reach minimum overshoot, minimum error and minimum settling time at least variables can be controlled. The controller is a variable of the molar fraction of distillate and the reflex ratio is the manipulated variable. Integral Time Absolute Error (ITAE) is employed as an objective function to measure the improvement in time response where the error is between desired and output product composition. The performance of IT2FLC is compared with Type-1 PI Like FLC (T1FLC). The results of the simulations have shown that the project of IT2FLC works efficiently to no- disturbance and the effects of disturbance. Improve average is of 85% for a constant set-point without a disturbance and 80% with a disturbance. Furthermore, the average improvement for a step set-point is 53% without disturbance and 74% with disturbance. All results of the simulation confirmed the hardiness and control any consistent inaccurate with obvious advantages for the IT2FLC.

scholarly journals An Energy Management System for Residential Autonomous DC Microgrid Using Optimized Fuzzy Logic Controller Considering Economic Dispatch

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1457 ◽  
Author(s):  
Shehab Al-Sakkaf ◽  
Mahmoud Kassas ◽  
Muhammad Khalid ◽  
Mohammad A. Abido
Keyword(s):  
Fuzzy Logic ◽  
Energy Storage ◽  
Fuel Cell ◽  
Wind Turbine ◽  
Energy Management ◽  
Fuzzy Logic Controller ◽  
Economic Dispatch ◽  
Energy Management System ◽  
Battery Energy Storage ◽  
Battery Energy

This work presents the operation of an autonomous direct current (DC) DC microgrid for residential house controlled by an energy management system based on low complexity fuzzy logic controller of only 25-rules to manage the power flow that supply house load demand. The microgrid consists of photovoltaic (PV), wind turbine, fuel cell, battery energy storage and diesel generator. The size of the battery energy storage is determined based on the battery sizing algorithm depending on the generation of renewables during all seasons of the year in the eastern region of Saudi Arabia. Two scenarios are considered in this work. In the first scenario: the microgrid consists of solar PV, wind turbine, battery energy storage and fuel cell. The fuzzy logic controller is optimized using an artificial bee colony technique in order to increase the system energy saving efficiency and to reduce the cost. In the second scenario: wind turbine is replaced by a diesel generator, also the rated power of the fuel cell is reduced. In this scenario, a new method is proposed to reduce the generation cost of the dispatchable sources in the microgrid by considering economic dispatch within the optimized fuzzy logic energy management system. To obtain the most suitable technique for solving the economic dispatch problem, three optimization techniques were used which are particle swarm optimization, genetic algorithm and artificial bee colony based on real environmental data and real house load demand. A comparison in terms of energy saving between the two scenarios and a comparison in terms of cost reduction between conventional economic dispatch method and the proposed method are presented.

scholarly journals Effect of Fuzzy Logic Controller Implementation on a Digitally Controlled Robot Movement

1970 ◽  
Vol 4 (1) ◽  
pp. 28-39
Author(s):  
Azura Che Soh ◽  
Erny Aznida Alwi ◽  
Ribhan Zafira Abdul Rahman ◽  
Li Hong Fey
Keyword(s):  
Fuzzy Logic ◽  
Intelligent Control ◽  
Fuzzy Logic Controller ◽  
Controller Design ◽  
Movement Control ◽  
Digital Controller ◽  
Movement Speed ◽  
Engineering And Technology ◽  
Digitally Controlled ◽  
Neural Network System

Advanced intelligent control design cannot entirely replace the application of conventional controller in robotic system. On the other hand, intelligent controllers can be implemented into conventional controller design to increase its performance and ability. Most of intelligent control in movement control involves fuzzy logic and neural network system. This study features the influence of fuzzy logic controller upon the performance of robot movement simulation, which is controlled by a digital controller. Model design and simulation are done in SIMULINK, using Fuzzy Logic Toolbox provided by MATLAB software to control the movement speed of a robotic designed system. Finally, the movement speed is found better off controlled with the additional of fuzzy logic controller instead of the digital controller solely. Keywords: Intelligent Control; Fuzzy Logic; Digital Controller; Robot Movement; Simulink. DOI: 10.3126/kuset.v4i1.2881Kathmandu University Journal of Science, Engineering and Technology Vol.4, No.1, September 2008, pp 28-39

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