Intelligent controller for a cooling system for a three-phase power transformer

Eskom, the national electricity supplier of South Africa struggle to supply sufficient amount of power each year. This is partially due to residential consumers that use appliances that required significant amount of power. This results in transformers that overheat which decrease the efficiency of the transformer. Also due to the heat the life expectancy of the transformers decreases. A solution to this problem is to cool the transformer in a controlled manner. This paper therefore provides the design of a controlled cooling system for a threephase power transformer that is energy efficient and displays the experimental results of this design. The cooling of the transformer make use of a thermoelectric cooling module (TECM) which is controlled by an programmable logic controller (PLC) which receives its data from a PT 100 thin sensors which is placed between the windings and core of the transformer. A prototype model of the actual transformer was used to perform the tests to indicate the cooling results. Solidworks® where used for the transformer simulation which were then implemented based on the results. A Flir thermal image camera was used to confirm the results and is compared to the simulated results. The payback period was determined to see how long the project will take before profit is made

Intelligent Control of Vehicles: Preliminary Results on the Application of Learning Automata Techniques to Automated Highway System

We suggest an intelligent controller for an automated vehicle to plan its own trajectory based on sensor and communication data received. Our intelligent controller is based on an artificial intelligence technique called learning stochastic automata. The automaton can learn the best possible action to avoid collisions using the data received from on-board sensors. The system has the advantage of being able to work in unmodeled stochastic environments. Simulations for the lateral control of a vehicle using this AI method provides encouraging results

Design an intelligent controller for full vehicle nonlinear active suspension systems

The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF) technique to design a robust controller to meet the control objectives. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road. The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order (FOPID) controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function

Multiple Stochastic Learning Automata for Vehicle Path Control in an Automated Highway System

This paper suggests an intelligent controller for an automated vehicle planning its own trajectory based on sensor and communication data. The intelligent controller is designed using the learning stochastic automata theory. Using the data received from on-board sensors, two automata (one for lateral actions, one for longitudinal actions) can learn the best possible action to avoid collisions. The system has the advantage of being able to work in unmodeled stochastic environments, unlike adaptive control methods or expert systems. Simulations for simultaneous lateral and longitudinal control of a vehicle provide encouraging result

A simple and efficient feedback control strategy for wastewater denitrification

Due to severe mathematical modeling and calibration difficulties open-loop feedforward control is mainly employed today for wastewater denitrification, which is a key ecological issue. In order to improve the resulting poor performances a new model-free control setting and its corresponding "intelligent" controller are introduced. The pitfall of regulating two output variables via a single input variable is overcome by introducing also an open-loop knowledge-based control deduced from the plant behavior. Several convincing computer simulations are presented and discussed.Comment: IFAC 2017 World Congress, Toulouse, Franc

Brain Emotional Learning Based Intelligent Controller And Its Application To Continuous Stirred Tank Reactor

This paper investigates an intelligent control approach towards Continuous Stirred Tank Reactor in chemical engineering. CSTR is a well known in process control and it offers a diverse range of research in chemical and control engineering. Brain emotional learning based intelligent controller (BELBIC) is an intelligent controller based on the model of Limbic system of brain. Our objective is to implement Computational Model of Brain Emotional Learning Based Intelligence Controller(BELBIC) and its Application To CSTR . Model design and simulations are done in MATLAB™ SIMULINK® software. Keywords: CSTR, BELBIC, Limbic system, Amygdala, Orbitofrontal corte