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In manual control, the welding gun’s moving speed can significantly influence the welding results and critical welding operations usually require welders to concentrate consistently in order to react rapidly and accurately. However, human welders always have some habitual action which can have some subtle influence the welding process. It takes countless hours to train an experienced human welder. Using vision and IMU sensor will be able to set up a system and allow the worker got more accurate visual feedback like an experienced worker. The problem is that monitor and measuring of the control process not always easy under a complex working environment like welding. In this thesis, a new method is developed that use two different methods to compensate each other to obtain accurate monitoring results. Vision sensor and IMU sensor both developed to obtain the accurate data from the control process in real-time but don’t influence other. Although both vision and IMU sensor has their own limits, they also have their own advantage which can contribute to the measuring system.

The aim of this work is to introduce a new type of fuzzy system AnYa. This system, unlike the classical fuzzy systems Takagi-Sugeno and Mamdani, uses a type of antecendent based on real data distribution. As part of the work there will be mentioned system programmed and its functionality will be verified on testing data.

The topic of force reconstruction has been studied quite extensively but most of the existing research work that has been done are in the domain of structural and civil engineering construction like bridges and beams. Considerable work in force reconstruction has also being done in fabrication of machines and structures like aircrafts, gear boxes etc. The topic of force reconstruction of the cutting forces during a machining process like turning or milling machines is a recent line of research to suffice the requirement of proactive monitoring of forces generated during the operation of the machine tool. The forces causing vibrations while machining if detected and monitored can enhance system productivity and efficiency of the process. The objective of this study was to investigate the algorithms available in literature for inverse force reconstruction and apply for reconstruction of cutting forces while machining on a computer numerically controlled (CNC) machine. This study has applied inverse force reconstruction technique algorithms 1) Deconvolution method, 2) Kalman filter recursive least square and 3) augmented Kalman filter for inverse reconstruction of forces for multi degree of freedom systems. Results from experiments conducted as part of this thesis work shows the effectiveness of the methods of force reconstruction to monitor the forces generated during the machining process on machine tools in real time without employing dynamometers which are expensive and complex to set-up. This study for developing a cost-effective method of force reconstruction will be instrumental in applications for improving machining efficiency and proactive preventive maintenance. / Graduate

This master’s thesis examines how a small MIMO lighting system can be identified and controlled. Two approaches are examined and compared; the first approach is a dynamic model using state space representation, where the system identification technique is Recursive Least Square, RLS, and the controller is an LQG controller; the second approach is a static model derived from the physical properties of light and a feedback feed-forward controller consisting of a PI controller coupled with a Control Allocation, CA, technique. For the studied system, the CA-PI approach significantly outperforms the LQG-RLS approach, which leads to the conclusion that the system’s static properties are predominant compared to the dynamic properties.

Conselho Nacional de Desenvolvimento Científico e Tecnológico / This Thesis deals with the development of a novel robust model reference adaptive controller (RMRAC), in discrete-time applied to grid-connected systems using LCL filter. This controller uses a modified robust parameters identifier based on a recursive least-squares algorithm. Two control structures are analyzed: state feedback approach and input-output approach. The robust stability analysis of the controller is presented including unmodeled dynamics. Thus, through these analyses, constraints design, in discrete-time, are obtained. For the validation of the proposed control algorithm, simulation and experimental results of a grid-connected power converter with LCL-filter, with current control, are presented. / Esta Tese de Doutorado apresenta o desenvolvimento de um novo controlador adaptativo por modelo de referência, totalmente desenvolvido em tempo discreto, aplicado a sistemas conectados à rede de energia elétrica empregando filtro LCL. Este controlador utiliza um identificador de parâmetros modificado robusto baseado no método dos mínimos quadrados recursivos. Em relação à estrutura do controlador, a abordagem por realimentação de estados e a abordagem entrada-saída são utilizadas. A análise de estabilidade robusta do controlador é apresentada incluindo dinâmicas não-modeladas. Por meio destas análises, restrições de projeto (em tempo discreto) são obtidas. Para a validação do algoritmo proposto, resultados de simulação e experimentais do sistema de controle de corrente em um conversor conectado à rede de energia elétrica com filtro LCL são apresentados.

The aim of this work is to introduce a new type of fuzzy system AnYa. This system, unlike the classical fuzzy systems Takagi-Sugeno and Mamdani, uses a type of antecendent based on real data distribution. As part of the work there will be mentioned system programmed and its functionality will be verified on testing data.