Nonlinear Fuzzy Tracking Real-time-based Control of Drying Parameters

The highly nonlinear characteristics of drying processes have prompted researchers to seek new nonlinear control solutions. However, the relation between the implementation complexity, on-line processing complexity, reliability control structure and controller-s performance is not well established. The present paper proposes high performance nonlinear fuzzy controllers for a real-time operation of a drying machine, being developed under a consistent match between those issues. A PCI-6025E data acquisition device from National Instruments® was used, and the control system was fully designed with MATLAB® / SIMULINK language. Drying parameters, namely relative humidity and temperature, were controlled through MIMOs Hybrid Bang-bang+PI (BPI) and Four-dimensional Fuzzy Logic (FLC) real-time-based controllers to perform drying tests on biological materials. The performance of the drying strategies was compared through several criteria, which are reported without controllers- retuning. Controllers- performance analysis has showed much better performance of FLC than BPI controller. The absolute errors were lower than 8,85 % for Fuzzy Logic Controller, about three times lower than the experimental results with BPI control.

Precision Control of Single-Phase PWM Inverter Using M68HC11E Microcontroller

Induction motors are being used in greater numbers throughout a wide variety of industrial and commercial applications because it provides many benefits and reliable device to convert the electrical energy into mechanical motion. In some application it-s desired to control the speed of the induction motor. Because of the physics of the induction motor the preferred method of controlling its speed is to vary the frequency of the AC voltage driving the motor. In recent years, with the microcontroller incorporated into an appliance it becomes possible to use it to generate the variable frequency AC voltage to control the speed of the induction motor. This study investigates the microcontroller based variable frequency power inverter. the microcontroller is provide the variable frequency pulse width modulation (PWM) signal that control the applied voltage on the gate drive, which is provides the required PWM frequency with less harmonics at the output of the power inverter. The fully controlled bridge voltage source inverter has been implemented with semiconductors power devices isolated gate bipolar transistor (IGBT), and the PWM technique has been employed in this inverter to supply the motor with AC voltage. The proposed drive system for three & single phase power inverter is simulated using Matlab/Simulink. The Matlab Simulation Results for the proposed system were achieved with different SPWM. From the result a stable variable frequency inverter over wide range has been obtained and a good agreement has been found between the simulation and hardware of a microcontroller based single phase inverter.

Fuzzy Logic Control of Static Var Compensator for Power System Damping

Static Var Compensator (SVC) is a shunt type FACTS device which is used in power system primarily for the purpose of voltage and reactive power control. In this paper, a fuzzy logic based supplementary controller for Static Var Compensator (SVC) is developed which is used for damping the rotor angle oscillations and to improve the transient stability of the power system. Generator speed and the electrical power are chosen as input signals for the Fuzzy Logic Controller (FLC). The effectiveness and feasibility of the proposed control is demonstrated with Single Machine Infinite Bus (SMIB) system and multimachine system (WSCC System) which show improvement over the use of a fixed parameter controller.

Automatic Deactivation in Phased Array Probe for Human Prostate Magnetic Resonance Imaging at 1.5T

A four element prototype phased array surface probe has been designed and constructed to improve clinical human prostate spectroscopic data. The probe consists of two pairs of adjacent rectangular coils with an optimum overlap to reduce the mutual inductance. The two pairs are positioned on the anterior and the posterior pelvic region and two couples of varactors at the input of each coil undertake the procedures of tuning and matching. The probe switches off and on automatically during the consecutive phases of the MR experiment with the use of an analog switch that is triggered by a microcontroller. Experimental tests that were carried out resulted in high levels of tuning accuracy. Also, the switching mechanism functions properly for various applied loads and pulse sequence characteristics, producing only 10 μs of latency.

Hybrid Neuro Fuzzy Approach for Automatic Generation Control of Two -Area Interconnected Power System

The main objective of Automatic Generation Control (AGC) is to balance the total system generation against system load losses so that the desired frequency and power interchange with neighboring systems is maintained. Any mismatch between generation and demand causes the system frequency to deviate from its nominal value. Thus high frequency deviation may lead to system collapse. This necessitates a very fast and accurate controller to maintain the nominal system frequency. This paper deals with a novel approach of artificial intelligence (AI) technique called Hybrid Neuro-Fuzzy (HNF) approach for an (AGC). The advantage of this controller is that it can handle the non-linearities at the same time it is faster than other conventional controllers. The effectiveness of the proposed controller in increasing the damping of local and inter area modes of oscillation is demonstrated in a two area interconnected power system. The result shows that intelligent controller is having improved dynamic response and at the same time faster than conventional controller.

Vibration Control of a Cantilever Beam Using a Tunable Vibration Absorber Embedded with ER Fluids

This paper investigates experimental studies on vibration suppression for a cantilever beam using an Electro-Rheological (ER) sandwich shock absorber. ER fluid (ERF) is a class of smart materials that can undergo significant reversible changes immediately in its rheological and mechanical properties under the influence of an applied electric field. Firstly, an ER sandwich beam is fabricated by inserting a starch-based ERF into a hollow composite beam. At the same time, experimental investigations are focused on the frequency response of the ERF sandwich beam. Second, the ERF sandwich beam is attached to a cantilever beam to become as a shock absorber. Finally, a fuzzy semi-active vibration control is designed to suppress the vibration of the cantilever beam via the ERF sandwich shock absorber. To check the consistency of the proposed fuzzy controller, the real-time implementation validated the performance of the controller.

One-DOF Precision Position Control using the Combined Piezo-VCM Actuator

This paper presents the control performance of a high-precision positioning device using the hybrid actuator composed of a piezoelectric (PZT) actuator and a voice-coil motor (VCM). The combined piezo-VCM actuator features two main characteristics: a large operation range due to long stroke of the VCM, and high precision and heavy load positioning ability due to PZT impact force. A one-degree-of-freedom (DOF) experimental setup was configured to examine the fundamental characteristics, and the control performance was effectively demonstrated by using a switching controller. In rough positioning state, an integral variable structure controller (IVSC) was used for the VCM to conduct long range of operation; in precision positioning state, an impact force controller (IFC) for the PZT actuator coupled with presliding states of the sliding table was used to obtain high-precision position control and achieve both forward and backward actuations. The experimental results showed that the sliding table having a mass of 881g and with a preload of 10 N was successfully positioned within the positioning accuracy of 10 nm in both forward and backward position controls.

Adaptive Neural Network Control of Autonomous Underwater Vehicles

An adaptive neural network controller for autonomous underwater vehicles (AUVs) is presented in this paper. The AUV model is highly nonlinear because of many factors, such as hydrodynamic drag, damping, and lift forces, Coriolis and centripetal forces, gravity and buoyancy forces, as well as forces from thruster. In this regards, a nonlinear neural network is used to approximate the nonlinear uncertainties of AUV dynamics, thus overcoming some limitations of conventional controllers and ensure good performance. The uniform ultimate boundedness of AUV tracking errors and the stability of the proposed control system are guaranteed based on Lyapunov theory. Numerical simulation studies for motion control of an AUV are performed to demonstrate the effectiveness of the proposed controller.

LQR Control for a Multi-MW Wind Turbine

This paper addresses linear quadratic regulation (LQR) for variable speed variable pitch wind turbines. Because of the inherent nonlinearity of wind turbine, a set of operating conditions is identified and then a LQR controller is designed for each operating point. The feedback controller gains are then interpolated linearly to get control law for the entire operating region. Besides, the aerodynamic torque and effective wind speed are estimated online to get the gain-scheduling variable for implementing the controller. The potential of the method is verified through simulation with the help of MATLAB/Simulink and GH Bladed. The performance and mechanical load when using LQR are also compared with that when using PI controller.

Identification of Nonlinear Predictor and Simulator Models of a Cement Rotary Kiln by Locally Linear Neuro-Fuzzy Technique

One of the most important parts of a cement factory is the cement rotary kiln which plays a key role in quality and quantity of produced cement. In this part, the physical exertion and bilateral movement of air and materials, together with chemical reactions take place. Thus, this system has immensely complex and nonlinear dynamic equations. These equations have not worked out yet. Only in exceptional case; however, a large number of the involved parameter were crossed out and an approximation model was presented instead. This issue caused many problems for designing a cement rotary kiln controller. In this paper, we presented nonlinear predictor and simulator models for a real cement rotary kiln by using nonlinear identification technique on the Locally Linear Neuro- Fuzzy (LLNF) model. For the first time, a simulator model as well as a predictor one with a precise fifteen minute prediction horizon for a cement rotary kiln is presented. These models are trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. At the end, the characteristics of these models are expressed. Furthermore, we presented the pros and cons of these models. The data collected from White Saveh Cement Company is used for modeling.

Accurate Control of a Pneumatic System using an Innovative Fuzzy Gain-Scheduling Pattern

Due to their high power-to-weight ratio and low cost, pneumatic actuators are attractive for robotics and automation applications; however, achieving fast and accurate control of their position have been known as a complex control problem. A methodology for obtaining high position accuracy with a linear pneumatic actuator is presented. During experimentation with a number of PID classical control approaches over many operations of the pneumatic system, the need for frequent manual re-tuning of the controller could not be eliminated. The reason for this problem is thermal and energy losses inside the cylinder body due to the complex friction forces developed by the piston displacements. Although PD controllers performed very well over short periods, it was necessary in our research project to introduce some form of automatic gain-scheduling to achieve good long-term performance. We chose a fuzzy logic system to do this, which proved to be an easily designed and robust approach. Since the PD approach showed very good behaviour in terms of position accuracy and settling time, it was incorporated into a modified form of the 1st order Tagaki- Sugeno fuzzy method to build an overall controller. This fuzzy gainscheduler uses an input variable which automatically changes the PD gain values of the controller according to the frequency of repeated system operations. Performance of the new controller was significantly improved and the need for manual re-tuning was eliminated without a decrease in performance. The performance of the controller operating with the above method is going to be tested through a high-speed web network (GRID) for research purposes.

Tele-Operated Anthropomorphic Arm and Hand Design

In this project, a tele-operated anthropomorphic robotic arm and hand is designed and built as a versatile robotic arm system. The robot has the ability to manipulate objects such as pick and place operations. It is also able to function by itself, in standalone mode. Firstly, the robotic arm is built in order to interface with a personal computer via a serial servo controller circuit board. The circuit board enables user to completely control the robotic arm and moreover, enables feedbacks from user. The control circuit board uses a powerful integrated microcontroller, a PIC (Programmable Interface Controller). The PIC is firstly programmed using BASIC (Beginner-s All-purpose Symbolic Instruction Code) and it is used as the 'brain' of the robot. In addition a user friendly Graphical User Interface (GUI) is developed as the serial servo interface software using Microsoft-s Visual Basic 6. The second part of the project is to use speech recognition control on the robotic arm. A speech recognition circuit board is constructed with onboard components such as PIC and other integrated circuits. It replaces the computers- Graphical User Interface. The robotic arm is able to receive instructions as spoken commands through a microphone and perform operations with respect to the commands such as picking and placing operations.

Adaptive Impedance Control for Unknown Non-Flat Environment

This paper presents a new adaptive impedance control strategy, based on Function Approximation Technique (FAT) to compensate for unknown non-flat environment shape or time-varying environment location. The target impedance in the force controllable direction is modified by incorporating adaptive compensators and the uncertainties are represented by FAT, allowing the update law to be derived easily. The force error feedback is utilized in the estimation and the accurate knowledge of the environment parameters are not required by the algorithm. It is shown mathematically that the stability of the controller is guaranteed based on Lyapunov theory. Simulation results presented to demonstrate the validity of the proposed controller.

A New Stability Analysis and Stabilization of Discrete-Time Switched Linear Systems Using Vector Norms Approach

In this paper, we aim to investigate a new stability analysis for discrete-time switched linear systems based on the comparison, the overvaluing principle, the application of Borne-Gentina criterion and the Kotelyanski conditions. This stability conditions issued from vector norms correspond to a vector Lyapunov function. In fact, the switched system to be controlled will be represented in the Companion form. A comparison system relative to a regular vector norm is used in order to get the simple arrow form of the state matrix that yields to a suitable use of Borne-Gentina criterion for the establishment of sufficient conditions for global asymptotic stability. This proposed approach could be a constructive solution to the state and static output feedback stabilization problems.

Neural Network Tuned Fuzzy Controller for MIMO System

In this paper, a neural network tuned fuzzy controller is proposed for controlling Multi-Input Multi-Output (MIMO) systems. For the convenience of analysis, the structure of MIMO fuzzy controller is divided into single input single-output (SISO) controllers for controlling each degree of freedom. Secondly, according to the characteristics of the system-s dynamics coupling, an appropriate coupling fuzzy controller is incorporated to improve the performance. The simulation analysis on a two-level mass–spring MIMO vibration system is carried out and results show the effectiveness of the proposed fuzzy controller. The performance though improved, the computational time and memory used is comparatively higher, because it has four fuzzy reasoning blocks and number may increase in case of other MIMO system. Then a fuzzy neural network is designed from a set of input-output training data to reduce the computing burden during implementation. This control strategy can not only simplify the implementation problem of fuzzy control, but also reduce computational time and consume less memory.

A Design of Fractional-Order PI Controller with Error Compensation

Fractional-order controller was proven to perform better than the integer-order controller. However, the absence of a pole at origin produced marginal error in fractional-order control system. This study demonstrated the enhancement of the fractionalorder PI over the integer-order PI in a steam temperature control. The fractional-order controller was cascaded with an error compensator comprised of a very small zero and a pole at origin to produce a zero steady-state error for the closed-loop system. Some modification on the error compensator was suggested for different order fractional integrator that can improve the overall phase margin.

Multivariable Predictive PID Control for Quadruple Tank

In this paper multivariable predictive PID controller has been implemented on a multi-inputs multi-outputs control problem i.e., quadruple tank system, in comparison with a simple multiloop PI controller. One of the salient feature of this system is an adjustable transmission zero which can be adjust to operate in both minimum and non-minimum phase configuration, through the flow distribution to upper and lower tanks in quadruple tank system. Stability and performance analysis has also been carried out for this highly interactive two input two output system, both in minimum and non-minimum phases. Simulations of control system revealed that better performance are obtained in predictive PID design.

Micro-Controller Based Oxy-Fuel Profile Cutting System

In today-s era of plasma and laser cutting, machines using oxy-acetylene flame are also meritorious due to their simplicity and cost effectiveness. The objective to devise a Computer controlled Oxy-Fuel profile cutting machine arose from the increasing demand for metal cutting with respect to edge quality, circularity and lesser formation of redeposit material. The System has an 8 bit micro controller based embedded system, which assures stipulated time response. A new window based Application software was devised which takes a standard CAD file .DXF as input and converts it into numerical data required for the controller. It uses VB6 as a front end whereas MS-ACCESS and AutoCAD as back end. The system is designed around AT89C51RD2, powerful 8 bit, ISP micro controller from Atmel and is optimized to achieve cost effectiveness and also maintains the required accuracy and reliability for complex shapes. The backbone of the system is a cleverly designed mechanical assembly along with the embedded system resulting in an accuracy of about 10 microns while maintaining perfect linearity in the cut. This results in substantial increase in productivity. The observed results also indicate reduced inter laminar spacing of pearlite with an increase in the hardness of the edge region.

Power Quality Improvement Using PI and Fuzzy Logic Controllers Based Shunt Active Filter

In recent years the large scale use of the power electronic equipment has led to an increase of harmonics in the power system. The harmonics results into a poor power quality and have great adverse economical impact on the utilities and customers. Current harmonics are one of the most common power quality problems and are usually resolved by using shunt active filter (SHAF). The main objective of this work is to develop PI and Fuzzy logic controllers (FLC) to analyze the performance of Shunt Active Filter for mitigating current harmonics under balanced and unbalanced sinusoidal source voltage conditions for normal load and increased load. When the supply voltages are ideal (balanced), both PI and FLC are converging to the same compensation characteristics. However, the supply voltages are non-ideal (unbalanced), FLC offers outstanding results. Simulation results validate the superiority of FLC with triangular membership function over the PI controller.

Fuzzy Control of the Air Conditioning System at Different Operating Pressures

The present work demonstrates the design and simulation of a fuzzy control of an air conditioning system at different pressures. The first order Sugeno fuzzy inference system is utilized to model the system and create the controller. In addition, an estimation of the heat transfer rate and water mass flow rate injection into or withdraw from the air conditioning system is determined by the fuzzy IF-THEN rules. The approach starts by generating the input/output data. Then, the subtractive clustering algorithm along with least square estimation (LSE) generates the fuzzy rules that describe the relationship between input/output data. The fuzzy rules are tuned by Adaptive Neuro-Fuzzy Inference System (ANFIS). The results show that when the pressure increases the amount of water flow rate and heat transfer rate decrease within the lower ranges of inlet dry bulb temperatures. On the other hand, and as pressure increases the amount of water flow rate and heat transfer rate increases within the higher ranges of inlet dry bulb temperatures. The inflection in the pressure effect trend occurs at lower temperatures as the inlet air humidity increases.