Experimental and theoretical control of a smart projectile fin using piezoelectric bimorph actuator

The goal of this work is to develop efficient control algorithms for the control of a smart projectile fin. Smart fins are deployed as soon as the projectile reaches the apogee and are used to steer the projectile towards its target by controlling the rotation angle of the fin. The fin is actuated using the piezoelectric macro-fiber composite (MFC) bimorph actuator which is completely enclosed within the aero-shell. The actuator is composed of two Macro Fiber Composites (MFC\u27s), manufactured by Smart Material Co. The presented smart fin design minimizes the volume and weight of the unit; Two different models of the smart fin are developed. One is mathematical model that uses finite element approach to describe dynamics of the smart fin system. This model includes the aerodynamic moment which is a function of the angle of attack of the projectile. Second model is based on system identification approach. A linear model of the actuator and fin is identified experimentally by exciting the system using a chirp signal. Comparison is done between these two models based on open-loop step response of the smart fin system; In this dissertation, five kinds of control systems based on fuzzy logic, inverse dynamics and adaptive structure theory are developed. The aerodynamic disturbances and parameter uncertainties are considered in these controllers. The simulation results illustrate that asymptotic trajectory tracking of the fin angle is achieved, in spite of uncertainties in the system parameters and presence of aerodynamic disturbance. A prototype model of the projectile fin is developed in the laboratory for real-time control. The designed controllers are validated using the subsonic wind tunnel at University of Nevada, Las Vegas (UNLV) for various wind speeds. Experimental results show that the designed controllers accomplish fin angle control

An adaptive system for process control

"Researchers at the U.S. Bureau of Mines have developed adaptive process control system in which genetic algorithms (GAs) are used to augment fuzzy logic controllers (FLCs). GAs are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by loosely modeling the search procedures of natural genetics. FLCs are rule based systems which efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GAs and FLCs include all of the capabilities necessary to produce powerful, efficient, and robust adaptive control systems." - NIOSHTIC-2NIOSHTIC no. 20039275199

Comparative Study of P&O and Fuzzy MPPT Controllers and Their Optimization Using PSO and GA to Improve Wind Energy System

Many academics have recently focused on wind energy installations. WECS (wind energy conversion system) is a renewable energy source that has seen significant development in recent years. Furthermore, compared to the use of power grid supply, the use of the WECS in the water pumping field is a cost-free option (economically). The purpose of this study is to demonstrate a wind-powered pumping mechanism. To obtain the best option, it considers and contrasts four distinct approaches. This research aims to improve the system\u27s performance and the quality of the generated power. The objective of the control of WECS with a permanent magnet synchronous generator (PMSG) is to carefully maximize power generation. Finally, this research employed the fuzzy logic control (FLC) and particle swarm optimization (PSO) algorithms improved using a genetic algorithm (GA). The proposed system\u27s performance was tested using the generated output voltage, current, and power waveforms, as well as the intermediate circuit voltage waveform and generator speed. The provided data show that the control technique used in this study was effective

Data Mining Technology for Structural Control Systems: Concept, Development, and Comparison

Structural control systems are classified into four categories, that is, passive, active, semi-active, and hybrid systems. These systems must be designed in the best way to control harmonic motions imposed to structures. Therefore, a precise powerful computer-based technology is required to increase the damping characteristics of structures. In this direction, data mining has provided numerous solutions to structural damped system problems as an all-inclusive technology due to its computational ability. This chapter provides a broad, yet in-depth, overview in data mining including knowledge view (i.e., concept, functions, and techniques) as well as application view in damped systems, shock absorbers, and harmonic oscillators. To aid the aim, various data mining techniques are classified in three groups, that is, classification-, prediction-, and optimization-based data mining methods, in order to present the development of this technology. According to this categorization, the applications of statistical, machine learning, and artificial intelligence techniques with respect to vibration control system research area are compared. Then, some related examples are detailed in order to indicate the efficiency of data mining algorithms. Last but not least, capabilities and limitations of the most applicable data mining-based methods in structural control systems are presented. To the best of our knowledge, the current research is the first attempt to illustrate the data mining applications in this domain

Multi particle swarm optimisation algorithm applied to supervisory power control systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPower quality problems come in numerous forms (commonly spikes, surges, sags, outages and harmonics) and their resolution can cost from a few hundred to millions of pounds, depending on the size and type of problem experienced by the power network. They are commonly experienced as burnt-out motors, corrupt data on hard drives, unnecessary downtime and increased maintenance costs. In order to minimise such events, the network can be monitored and controlled with a specific control regime to deal with particular faults. This study developed a control and Optimisation system and applied it to the stability of electrical power networks using artificial intelligence techniques. An intelligent controller was designed to control and optimise simulated models for electrical system power stability. Fuzzy logic controller controlled the power generation, while particle swarm Optimisation (PSO) techniques optimised the system’s power quality in normal operation conditions and after faults. Different types of PSO were tested, then a multi-swarm (M-PSO) system was developed to give better Optimisation results in terms of accuracy and convergence speed.. The developed Optimisation algorithm was tested on seven benchmarks and compared to the other types of single PSOs. The developed controller and Optimisation algorithm was applied to power system stability control. Two power electrical network models were used (with two and four generators), controlled by fuzzy logic controllers tuned using the Optimisation algorithm. The system selected the optimal controller parameters automatically for normal and fault conditions during the operation of the power network. Multi objective cost function was used based on minimising the recovery time, overshoot, and steady state error. A supervisory control layer was introduced to detect and diagnose faults then apply the correct controller parameters. Different fault scenarios were used to test the system performance. The results indicate the great potential of the proposed power system stabiliser as a superior tool compared to conventional control systems

Battery Charge Control in Solar Photovoltaic Systems Based on Fuzzy Logic and Jellyfish Optimization Algorithm

The study focuses on the integration of a fuzzy logic-based Maximum Power Point Tracking (MPPT) system, an optimized proportional Integral-based voltage controller, and the Jellyfish Optimization Algorithm into a solar PV battery setup. This integrated approach aims to enhance energy harvesting efficiency under varying environmental conditions. The study’s innovation lies in effectively addressing challenges posed by diverse environmental factors and loads. The utilization of MATLAB 2022a Simulink for modeling and the Jellyfish Optimization Algorithm for PI-controller tuning further strengthens our findings. Testing scenarios, including constant and variable irradiation, underscore the significant enhancements achieved through the integration of fuzzy MPPT and the Jellyfish Optimization Algorithm with the PI-based voltage controller. These enhancements encompass improved power extraction, optimized voltage regulation, swift settling times, and overall efficiency gains.The authors were supported by the Vitoria-Gasteiz Mobility Lab Foundation, an organization of the government of the Provincial Council of Araba and the City Council of Vitoria-Gasteiz through the following project grant (“Generación de mapas mediante drones e Inteligencia Computacional”)

Intelligent STATCOM Voltage Regulation using Fuzzy Logic Control

Reactive power compensation is a very important and challenging task in electrical power systems today. Future trends foreseen in power systems such as high interconnectivity and the integration of renewable energy resources produce even more issues related to power system control and stability. Flexible AC transmission systems are vastly used in power systems in order to mitigate several performance aspects found in typical power systems. One shunt connected device in particular, STATCOM, is very powerful and commonly used in voltage regulation at the power transmission level. STATCOM uses voltage sourced converters to inject or absorb reactive power from the power grid as commanded to stabilize the transmission line voltage at the point of connection. The control of STATCOM has relied historically on using traditional PI controllers, however, since the dynamic response of STATCOM highly affects its ability to perform its task, improving the capabilities of STATCOM using more advanced control approaches has become vital for both manufacturers and power systems operators. Fuzzy logic control, as one area of artificial intelligence techniques, has been emerging in recent years as a complement to the conventional methods in various areas of power systems control. The most significant advantage of fuzzy controller as an intelligent controller is that it doesn’t require mathematical modelling. It is robust and nonlinear in its nature, and expert’s knowledge can be utilized in generating control rules. The main contribution is to use fuzzy logic control theory to design a pure fuzzy logic control and another fuzzy adaptive PI control strategies for STATCOM that are superior in performance to traditional PI control approach. This will increase STATCOM’s ability to seamlessly perform their task in voltage regulation. This work investigates the performance of classical PI controlled STATCOM then compares it with fuzzy logic based STATCOM and fuzzy adaptive PI controlled STATCOM. Simulations done using MATLAB on a three generator test system show that adaptive fuzzy PI control technique is faster in responding to voltage variations and better in tracking the reactive current reference. Results also show that a direct control using fuzzy logic provides even faster voltage regulation and acts almost as a perfect tracker for reference reactive current

Development of an adaptive fuzzy logic controller for HVAC system

An adaptive approach to control a cooling coil chilled water valve operation, called adaptive fuzzy logic control (AFLC), is developed and validated in this study. The AFLC calculates the error between the supply air temperature and supply air temperature set point for air in an air handling unit (AHU) of a heating, ventilating, and air conditioning (HVAC) system and determines optimal fuzzy rule matrix to minimize the hydronic energy consumption while maintaining occupant comfort. The AFLC uses genetic algorithms and evolutionary strategies to determine the fuzzy rule matrix and fuzzy membership functions for an AHU in HVAC systems;Cooling coil models are developed using neural network, general regression neural network and lump capacitance methods to predict the supply air temperature. These models helped with the development of the adaptive fuzzy logic controller;Two types of validation experiments were conducted, one with cyclically changing supply air temperatures and the second with cyclically changing supply air flow rates. Experiments conducted on two identical real HVAC systems were used to compare the performances of the AFLC to a conventional proportional, integral and derivative (PID) controller. To remove bias between the testing systems, the controllers were switched from one system to the other;The validation experiments indicate that the HVAC system operated under the AFLC consumes 1 to 7 % less hydronic energy when compared with a conventional PID controlled system. More actuator travel distance was observed when using the AFLC. The AFLC maintained better occupant comfort conditions when compared with the conventional PID controller. It was observed that the controlled variable for the AFLC system required 0 to 185% more rise time, had 9 to 68% less overshoot and required 11 to 45% less settling time as compared to the conventional PID controlled system