Takagi-Sugeno fuzzy perpose as speed controller in indirect field oriented control of induction motor drive

This paper deal with the problem in speed controller for Indirect Field Oriented Control of Induction Motor. The problem cause decrease performance of Induction Motor where it widely used in high-performance applications. In order decrease the fault of speed induction motor, Takagi- Sugeno type Fuzzy logic control is used as the speed controller. For this, a model of indirect field oriented control of induction motor is built and simulating using MATLAB simulink. Secondly, error of speed and derivative error as the input and change of torque command as the output for speed control is applied in simulation. Lastly, from the simulation result overshoot is zero persent, rise time is 0.4s and settling time is 0.4s. The important data is steady state error is 0.01 percent show that the speed can follow reference speed. From that simulation result illustrate the effectiveness of the proposed approach

Artificial intelligence based direct torque control of induction motor drive system

In this project, a three-phase Induction motor (IM) under the direct torque control (DTC) technique is studied. IM is known for its simple engines and its self-starter feature but it always suffered a setback in the area of torque and speed control as it is a highly coupled nonlinear plant and proves to be most complex and expensive speed drive. The application of direct torque control (DTC) is beneficial for fast torque reaction in IM but provide high torque and ripples due to harmonic effects. Thus, the speed control of induction motor is important to achieve maximum torque and efficiency. The aim of this study is to improve tracking performance of the induction motor drive using artificial intelligence control system. A method for controlling induction motor drive is presented with Proportional-Integral (PI) controller and Artificial Neural Networks (ANNs) for performance comparison. MATLAB/SIMULINK software is used to develop a three-phase 2 pole-cage type induction motor model. Also the performances of the two controllers have been verified in terms of its speed and torque responses. The ANN is trained so that the speed of the drive tracks the reference speed. This study proved that the performance and dynamics of the induction motor are enhanced using ANN controller as compared with PI controller

Conjugate Gradient Approach for Discrete Time Optimal Control Problems with Model-Reality Differences

In this chapter, an efficient computation approach is proposed for solving a general class of discrete-time optimal control problems. In our approach, a simplified optimal control model, which is adding the adjusted parameters into the model used, is solved iteratively. In this way, the differences between the real plant and the model used are calculated, in turn, to update the optimal solution of the model used. During the computation procedure, the equivalent optimization problem is formulated, where the conjugate gradient algorithm is applied in solving the optimization problem. On this basis, the optimal solution of the modified model-based optimal control problem is obtained repeatedly. Once the convergence is achieved, the iterative solution approximates to the correct optimal solution of the original optimal control problem, in spite of model-reality differences. For illustration, both linear and nonlinear examples are demonstrated to show the performance of the approach proposed. In conclusion, the efficiency of the approach proposed is highly presented

Power Forecasting from Solar Panels Using Artificial Neural Network in UTHM Parit Raja

This paper presents a step-by-step procedure for the simulation of photovoltaic modules with numerical values, using MALTAB/Simulink software. The proposed model is developed based on the mathematical model of PV module, which based on PV solar cell employing one-diode equivalent circuit. The output current and power characteristics curves highly depend on some climatic factors such as radiation and temperature, are obtained by simulation of the selected module. The collected data are used in developing Artificial Neural Network (ANN) model. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) are the techniques used to forecast the outputs of the PV. Various types of activation function will be applied such as Linear, Logistic Sigmoid, Hyperbolic Tangent Sigmoid and Gaussian. The simulation results show that the Logistic Sigmoid is the best technique which produce minimal root mean square error for the system

State Estimation and Optimal Control of Four-Tank System with Stochastic Approximation Approach

This study aims to optimally control the level of a four-tank system at the steady state in the random disturbance environment using the stochastic approximation (SA) approach. Firstly, the stochastic optimal control problem of an equivalent discrete-time is introduced, where the voltages to the pumps are the control inputs. By minimizing the sum of squared errors, the liquid levels are estimated. Then, first-order necessary conditions are derived by defining the Hamiltonian function. Thus, the optimal voltages are calculated based on the estimated liquid levels to update the gradient of the cost function. Finally, for illustration, parameters in the system are considered and a simulation is conducted. The simulation results show that the state estimation and control law design can perform well, and the liquid levels are addressed along the steady state. In conclusion, the applicability of the SA approach for handling a four-tank system with random disturbances is demonstrated

Performance comparison of PEMFC hydrogen reformer with different controllers

The renewable energy technology has become very popular due to major constraint in the existing electrical system such as high electricity demand, increased in fuel prices and concern of environmental pollution. The aims of this project are to develop a complete Proton Exchange Membrane Fuel Cell (PEMFC) model with hydrogen reformer by using MATLAB/ Simulink with three different controllers and comparison between the three controllers will be discussed. This project presents the development of methods to solve the problem of PEMFC output voltage by using different controllers which are Proportional Integral (PI), Proportional Integral Derivatives (PID) and Proportional Integral Fuzzy (PI-Fuzzy) controllers. The Ziegler Nicholas tuning method is used to tune PI and PID gains in a Simulink model. It helps the system to achieve a balance between performance and robustness for both controllers. The Mamdani type was used to develop the fuzzy controller in Simulink model. The transient performances that will be discussed are rise time, settling time, maximum overshoot, and percentage of overshoot. The results show that the proposed PI-Fuzzy is better than the conventionally used PI and PID controllers

Bending mechanism for piezoelectric transducer

This paper tests and analyzes a piezoelectric transducer with bending mechanism based on the bending condition of piezoelectric transducer after applying a force on the bending mechanism that increase the stress on the piezoelectric surface and thus increase the electrical charges produced An impact force is being exerted onto bending mechanism to bend the piezoelectric transducer and hence generating useable electrical power. The proposed prototype bending mechanism was built by using SolidWork soft-ware and manufactured by employing 3D printer technology to conduct the evaluation. This bending mechanism is divided into two parts, presser and support housing. The presser is design with four different diameter to test the piezoelectric transducer. A round piezoelectric transducer with the size of 50 mm in diameter is used to demonstrate the experiment. This piezoelectric transducer is placed in support housing and the presser is used to give pressure to the piezoelectric transducer. A subject is assigned to hold the presser and press in on the surface of the piezoelectric transducer. It can be seen from the experiment that the last presser with diameter 22.35 mm generated the highest output voltage about 44.0 V. The reported mechanism is a promising candidate in the application of energy harvesting by using piezoelectric transducer for powering various low power output devices

Study on footstep power generation using piezoelectric tile

Electrical energy is important and had been demand increasingly. A lot of energy resources have been wasted and exhausted. An alternative way to generate electricity by using a population of human had been discovered When walking, the vibration that generates between the surface and the footstep is wasted. By utilizing this wasted energy, the electrical energy can be generated and fulfill the demand. The transducer that use to detect the vibration is a piezoelectric transducer. This transducer converts the mechanical energy into electrical energy. When the pressure from the footstep is applied to the piezoelectric transducer, it will convert the pressure or the force into the electrical energy. The piezoelectric transducer is connected in series-parallel coonection. Then, it is placed on the tile that been made from wood as a model for footstep tile to give pressure to the piezoelectric transducers. This tile can be placed in the crowded area, walking pavement or exercise instruments. The electric energy that generates from this piezoelectric tile can be power up low power appliances

Development of prototype smart helmet and blind spot detection for motorcyclist safety features

Motorcycle safety concerns many aspects of vehicle and equipment design as well as operator skill and training that are unique to motorcycle riding. This project focus on the development of prototype smart helmet and blind spot detection features for motorcyclist safety system. The main objective of this work is to reduce the blind spot area for motorcyclists. The developed system are capable of measure appropriate distances (~ 10 cm to 50 cm) of insecurity among motorcyclists and vehicle behind by means of ultrasonic sensors and vibrator motor. The ultrasonic sensor play a role to detect any vehicle from behind the motorcyclist and system will alert the rider through the vibrator motor, LEDs and buzzer that install at the helmet as a warning to the rider about the range of insecurity. The system employed Arduino UNO as the main processing unit to control all the connectivity components. OLED display are also connected to the Arduino UNO mounted in front of the rider for displaying the distance detected by the ultrasonic sensor. Wireless transceiver module is use to serve as a data transmitter and receiver in which transmitted data collected by ultrasonic sensor will wirelessly transmit to the helmet node which is the receiver unit. In conclusion, this developed prototype project work are able to provide better solution for motorcyclist safety features in order to reduce the probability of accidental rate

Development of chicken egg hatching incubator machine (Natasha-CEHIM)

This paper presents a development of egg incubator system for chicken eggs incubation. The chicken egg hatching incubator machine are equipped with DHT22 and PIR sensors that able to automatically regulate and monitor the temperature and humidity in the incubator to create the ideal condition to incubate chicken egg. The entire system will be controlled using Arduino Uno control system as the brain of the system which control and operate the motor and sensors to detect necessary environment condition which then produce output signal for incubate operation. A DHT22 sensor that act as input detects temperature and humidity in the incubator and send it to the Arduino to display the condition of the incubator on the LCD screen display. The function of egg incubator is to be able to hatch chicks from egg while the mother hen can be free to lay more eggs hence increase the production of chicken egg, reduce the manpower, reduce hatching time and give a better efficiency during operation