The emergence of computational intelligence technology inspired by biological and human intelligence is one of the most exciting and important fields in engineering . It is expected that these technologies like fuzzy logic , will play a significant role in the development of intelligent robotic systems , machine systems , and mechatronics systems . In this work ; a Fuzzy Logic Controller is presented to control the motion of a robotic arm and avoid the obstacles existed in its mission road ,using a real platform robotic arm in combination with a vision system . This work involves constructing an integrated and autonomic MATLAB program. It could be applicable for any robotic arm . It depends on a new approach in analyzing the robotic environment videos acquired by a fixed webcam. The approach uses colors to detect and recognize the changeable locations and objects’ dimensions for each of the robot’s end-effector , the goal , and the obstacles .
Design and implementation of fuzzy logic controller for mobile robot navigation in unknown environments is presented. The task of navigation is divided into three behaviors namely hurdle avoidance, wall following and goal seeking. The outputs from these behaviors are combined to generate collision free motion of robot amongst obstacles in reaching the target. The controllers for these behaviors are designed using Fuzzy Logic toolbox of MATLAB® and their implementation is realized with readily available and inexpensive AT89C52 microcontrollers. Finally, the robot with these controllers is tested in indoor environments containing obstacles with changing destination places and is found to reach the set targets successfully which shows the validity of the designed controllers in achieving the required task.
The learning capabilities of artificial neural networks (ANNs) to identify and emulate the behavior of complicated nonlinear systems have made them effective tools that can be utilized in intelligent adaptive control strategies. The use of ANNs in the design of trajectory tracking controllers for robotic manipulators is dated back to the 1980s. Due to the flexibility of their structure as well as the continuous development and enhancement of their self-training algorithms, the use of ANNs in the field of robotic manipulator trajectory tracking control is being considered an important research area. This textbook explains in great detail the process of designing an effective controller to enhance the trajectory tracking performance of a two degree of freedom (2-DOF) robotic arm using neural networks. Feed-forward ANNs were used in both model-based and non-model-based control strategies. Since it also includes a deep explanation of the modeling of the 2-DOF robotic arm system including its actuating DC-motors and their control using a PD controller, this textbook can also serve as an effective educational tool for both undergraduate and graduate electrical engineering students.
Petri nets are considered to be the alternative approach for analysis, design and implementation of DES. However, the prospect of uncertain and vague information still creates a problem. Hence, to handle the uncertainty, fuzzy logic is integrated into Petri nets to form Fuzzy Automation Petri nets as a modeling tool to handle these uncertainties. The use of Fuzzy Automation Petri nets (FAPN) demands a valid translation of the formalism to Ladder Logic Diagrams for implementation. This is achieved using Token Passing Methodology. This work investigates the use of Fuzzy Petri nets in supervisory control and suggests a modified and improved version called Fuzzy Automation Petri net (FAPN) as a modeling tool. It presents a systematic approach to the synthesis of Fuzzy Petri net based supervisor for the forbidden state problem using supervisory design procedure. The controlled model of the system can be constructed from this FAPN net structure. The implementation is using Flexible Manufacturing System as an example of DES.
This study is aimed at characterization of Fuzzy logic control based system with varying span of Member Function by establishing region of stable operation for different cases using range provided by 'k' for setting the span for desired performance.The performed work may be used as reference, by designers for designing a Fuzzy logic based control system depending upon the requirement of desired application.
Fuzzy Logic Control (FLC) is an important alternative method to the conventional Proportional, Integral, and Derivative (PID) control method for use in nonlinear systems. This book, therefore, highlight the feasibility and effectiveness of fuzzy logic control in application to mathematical models of two basic types of steam turbines; straight expansion and single-automatic extraction turbines. The derived performance of the developed mathematical models, in terms of input/output duty variables without mean of control, is found to be in a good agreement with the actual performance of typical steam turbines with practical technical data and operating conditions. Model components exhibit nonlinear behavior. A comparison is made between the efficiency of Fuzzy Logic Control and the conventional PID control for the dynamic responses of the closed loop drive system. In case of straight expansion steam turbines, the control task is either speed or backpressure control. In case of single extraction steam turbines, the control task is to maintain both speed and extraction pressure of the turbine constants. This is done in presence of severe changes in load and/or steam demand conditions.
Continuous stirred tank reactor is a typical chemical reactor with complex non linear dynamic characteristics. There has been considerable interest in its state estimation and real time control based on mathematical modeling. However, the lack of understanding of the dynamics of the process, the highly sensitive and non linear behavior of the reactor, has made it difficult to develop the precise mathematical modeling of the system. Efficient control of the product concentration in CSTR can be achieved only through accurate model. Here attempts are made to ease the modeling difficulties using AI techniques such as Fuzzy logic. Simulation results demonstrate the effectiveness of Fuzzy logic control technique. The system is a stirred tank reactor with two flows of liquid enter the system and another flow exits the tank with the formation of product. Simulink was employed to design a model for this system in the simulation environment. This work is aimed for utilization by researchers, industrialists’ and students. Furthermore it will be especially relevant and useful for the students of Electrical Engg. and Chemical Engg. for carrying out research in the area of simulation of CSTR.
Main aim of this book is to identify, model and control robotic manipulator with three degrees of freedom. The book is a part of major project , the aim of which is to create an educational platform. In the book the simple PID control and the PID with feedforward compensation control is tested on the model of simple pendulum. In the next part models of DC motors, which are used for construction of the manipulator, are developed and the inverse dynamics model of manipulator is developed. This model is used for feedforward control of the manipulator. In the final part the application was developed, which allows the manipulator to be taught some movements, which can be later on, executed. For the simple control of the application the graphical user interface was programmed.
One of the main and recent problems in Malaysian hospitals is the lack of surgeonsand specialists, especially in rural areas. Insufficient specialised surgeons in such regions particularly in the niche of orthopaedic causes more fatalities and amputees due to time constrain in attending the patients. Broken limbs due to accidents can be treated and recovered. But severed blood vessels results in blood loss and leads to amputation or even worst fatalities. A mobile robotic system known as OTOROB is designed and developed to aid orthopaedic surgeons to be virtually present at such areas for attending patients. The developed mobile robotic platform requires a flexible robotic arm vision system to be controlled remotely by the surgeon. To be present virtually is still insufficient if clearer view is not obtained. Thus, a flexible robotic arm with vision system as end effector is designed, developed and tested in real time. Fuzzy logic is implemented in the control system to provide safety for the robotic arm articulation. The safety systems of the robotic arm consist of Danger Monitoring System (DMS), Obstacle Avoidance System (OAS) and Fail Safe and Auto Recovery System (FSARS).
Fuzzy controllers are used to control consumer products, such as washing machines, video cameras, and rice cookers, as well as industrial processes, such as cement kilns, underground trains, and robots. Fuzzy control is a control method based on fuzzy logic. Just as Fuzzy logic can be described simply as “computing with words rather than numbers’’ and Fuzzy control can be described simply as “control with sentences rather than equations’’. A Fuzzy controller can include empirical rules, and that is especially useful in operator controlled plants.This is the world of automation. The majority of the products allow actions to be automatically triggered by events. The Performance of a SCADA system can be much improved using a fuzzy logic controller based SCADA in industries. This book describes design of fuzzy logic based SCADA Systems using MATLAB fuzzy logic toolbox.This Book is useful for Engineers and Research Scholars in the field of Electrical & Power Engineering.
With every year air conditioning units are increasing rapidly all over the world and air conditioning system has not only become integral part of every institution but also of our lives. The survey of this system shows that air conditioning system consumes nearly 50% of the total energy intake of a building. In air conditioning system, compressor unit consumes most of the energy. Different control techniques can be used to control the compressor speed. This book therefore provides the use of two intelligent control techniques to control the compressor speed and make air conditioning system energy efficient. The techniques used are fuzzy logic and neuro-fuzzy. This book also provides the comparison of both the techniques. This book should be helpful for professionals in artificial intelligence, or anyone else who may seek to work in Fuzzy , Neuro-fuzzy or related techniques. This book would also be helpful to students for study of these techniques.
In this Book, a modern FPGA card (Spartan-3A, Xilinx Company) is used to realized a novel Fuzzy- PID control strategy on a complex system; a three- phase induction motor (squirrel cage type) is selected as a case study. The Fuzzy logic control demonstrates a good performance. Furthermore, Fuzzy logic offers the advantage of a faster design, and emulation of human control strategies. Also, Fuzzy control works well for high-order and nonlinear and shows the efficiency over the PID controller. The proposed controller and the pulse width modulator (PWM) inverter algorithm which have been built in FPGA have resulted in a fast speed response and a good stability in controlling the three-phase induction motor. For comparison purpose, two widely used controllers are realized using the same FPGA kit: PID and Fuzzy. Simulations are performed using MATLAB/SIMULINK (R2009a) with varying load and speed conditions. The Fuzzy-PID control strategy outperformed both PID and Fuzzy controllers. This book will be especially useful to academics, researchers, and practitioners.
This book presents the details theory and applications of Fuzzy sets,fuzzy systems,membership functions & controller designed. A logic based on the two truth values True and False is sometimes inadequate when describing human reasoning. Fuzzy logic uses the whole interval between 0 (False) and 1 (True ) to describe human reasoning. As a result, fuzzy logic is being applied in rule based automatic controllers. A fuzzy control method for automatic steering and a method for line tracking are conveyed in this article. The principal for fuzzy control steering and the construction of fuzzy controller are described in detail.For Example,A vehicle is refitted with a storage battery car. Navigation control system is developed based on digital compass and other sensors. The vehicle acquires the location information using the fuzzy control steering and line tracking method. Test results indicated that the navigation lateral error was less than 0.3m when the robot ran following the predefined line route. Finally, a power system network with UPFC has been considered & A POD controller has designed by using Fuzzy Logic to improve the damping oscillation of power system network.
The authors guide readers quickly and concisely through the complex topics of neural networks, fuzzy logic, mathematical modelling of electrical machines, power systems control and VHDL design. Unlike the academic monographs that have previously been published on each of these subjects, this book combines them and is based round case studies of systems analysis, control strategies, design, simulation and implementation. The result is a guide to applied control systems design that will appeal equally to students and professional design engineers. The book can also be used as a unique VHDL design aid, based on real-world power engineering applications.*Introduces cutting-edge control systems to a wide readership of engineers and students*The first book on neuro-fuzzy control systems to take a practical, applications-based approach, backed up with worked examples and case studies*Learn to use VHDL in real-world applications
Process control is the regulation of designated process parameters to within a specified target range or to a set target value called the set point. Process control is most often used in refineries, because many materials, such as fuel, gasoline and kerosene, must be accurate for a product to be well made. Therefore, to implement a quality product, process control is used to monitor and correct process parameters by analyzing the state of dynamic variables. Dynamic variables are process characteristics, such as temperature, flow, and pressure that vary with time. Through its I/O interfaces, a controller can regulate these dynamic variables to a desired set point, thus implementing process control.