A fault in the system operation is deemed to occur when the system practically experiences an abnormal condition, such as a malfunction in the actuators/sensors. Hence, detection and isolation of the faulty components is crucial in control applications. Effective control and monitoring of a system requires accurate information of internal behaviour of the system. This internal behaviour can be analyzed by system's states. Practically, in many real systems, state space variables are not fully available for measurements. The two critical problems stated have motivated significant research work in the area of robust state and fault estimation. Fault reconstruction and estimation is regarded as a stronger extension to fault detection and isolation (FDI) since accurate fault estimation automatically implies fault detection. It is well known that two promising control strategies to cope with uncertain control processes are H_infinity Control and Sliding Mode Control. Therefore, in this PhD thesis, we employ these tools and we propose observer based robust fault reconstruction (RFR) by integrating H_infinity filtering and Sliding Mode Control. We also employ adaptive control on the sliding motion to deal with faults with unknown bounds. Another open problem in the context of FDI and RFR is due to systems with multiple faults at different system's components since it is often the case where actuators and also sensors suffer from faults during the course of the system's operation. Both actuators and sensors can suffer from faults either alone, at separate times or simultaneously. The co-existence of unknown fault at both sensor(s) and actuator(s) has not been addressed in any earlier design of fault reconstruction schemes. In this Thesis, inspired by the theory of singular systems, we aim at solving this problem. A New structure for reduced-order unknown input observers (UIOs) with application to chaotic communication and sensor fault reconstruction is also proposed. / Controls

Los procesos industriales tienen tres fases diseño del producto del proceso y producción en las que experimentos cuidadosamente diseñados pueden aumentar la calidad del producto y la productividad del proceso. En el capitulo 2 se propone un algoritmo para asignar las variables físicas del experimento a los factores del diseño de forma que el diseño obtenido por proyección sea lo mas informativo posible. En el capitulo 3 se propone un nuevo método mas eficiente y preciso que el hasta ahora conocido para estimar los efectos de las variables sobre la dispersión en la respuesta.

This thesis presents a detailed design, calibration, and control of a modular and reconfigurable robot (MRR) system. A MRR system not only includes modular mechanical hardware, but also modular electrical hardware, control algorithms and software. Also, those modular components can be easily constructed into various manipulator configurations to accomplish a wider range of tasks. MRRs represent the next generation of industrial manipulators that cope with the transition from mass to customer-oriented production. The main contributions of this thesis are: 1) mechanical design and calibration of multi-input multi-output (MIMO) joint modules of MRR, and 2) control design to handle multiple configuration and overcome disturbance due to dynamics uncertainty. From the mechanical design point of view, this thesis presents two main topics: 1) each joint is not only modularly designed, but also has multiple-input multiple-output (MIMO) physical connection ports, which contributes to the concept of reconfigurability. Strictly speaking, single-input single-output (SISO) modular joint falls into the category of modular manipulator, and the robot reconfiguration is achieved by integrating different types of modules. For example, with single revolute MIMO joint module, both rotary and pivotal joint can be generated. On the other hand, if you would like to switch from rotary movement to pivotal movement with a SISO joint module, using another pivotal joint module is the only way to achieve this goal, and 2) for precise automation application, joints and links should be accurately connected and oriented when reconfigured. Our proposed modular joint has four connection ports which can be configured as either a rotary joint or a pivotal joint. In addition, key and keyway connection mechanism provides high accuracy in positioning the link onto the joint. Therefore, this structure reduces or eliminates MRRs system calibration time when reconfigured. Furthermore, zero link offset when used as a pivotal joint increases the robot dexterity, maximizes the reachability, and results in kinematics simplicity. The main challenge in the control of an MRR system with harmonic drives (HD) is the significant uncertainties due to friction, unmodelled dynamics, varying payload, gravitation, dynamic coupling between motions of joints, and the configuration changes. In order to compensate all unpredictable effects, we proposed a decentralized saturation-type robust control scheme based on direct-Lyapunov method and backstepping techniques. To better understand the system dynamics behavior, the HD flexspline compliance and friction calibration and results are also provided. The results are used for controller design. The proposed controller is verified through both computer simulation and experimental analysis.

Carbon dioxide is a main greenhouse gas that is responsible for global warming and climate change. The reduction in greenhouse gas emission is required to comply with the Kyoto Protocol. Looking at CO2 emissions distribution in Canada, the electricity and heat generation sub-sectors are among the largest sources of CO2 emissions. In this study, the focus is to reduce CO2 emissions from electricity generation through capacity expansion planning for utility companies. In order to reduce emissions, different mitigation options are considered including structural changes and non structural changes. A drawback of existing capacity planning models is that they do not consider uncertainties in parameters such as demand and fuel prices. Stochastic planning of power production overcomes the drawback of deterministic models by accounting for uncertainties in the parameters. Such planning accounts for demand uncertainties by using scenario sets and probability distributions. However, in past literature different scenarios were developed by either assigning arbitrary values or by assuming certain percentages above or below a deterministic demand. Using forecasting techniques, reliable demand data can be obtained and can be inputted to the scenario set. The first part of this thesis focuses on long term forecasting of electricity demand using autoregressive, simple linear, and multiple linear regression models. The resulting models using different forecasting techniques are compared through a number of statistical measures and the most accurate model was selected. Using Ontario electricity demand as a case study, the annual energy, peak load, and base load demand were forecasted, up to year 2025. In order to generate different scenarios, different ranges in economic, demographic and climatic variables were used. The second part of this thesis proposes a robust optimization capacity expansion planning model that yields a less sensitive solution due to the variation in the above parameters. By adjusting the penalty parameters, the model can accommodate the decision maker’s risk aversion and yield a solution based upon it. The proposed model is then applied to Ontario Power Generation, the largest power utility company in Ontario, Canada. Using forecasted data for the year 2025 with a 40% CO2 reduction from the 2005 levels, the model suggested to close most of the coal power plants and to build new natural gas combined cycle turbines and nuclear power plants to meet the demand and CO2 constraints. The model robustness was illustrated on a case study and, as expected, the model was found to be less sensitive than the deterministic model.

This thesis presents a detailed design, calibration, and control of a modular and reconfigurable robot (MRR) system. A MRR system not only includes modular mechanical hardware, but also modular electrical hardware, control algorithms and software. Also, those modular components can be easily constructed into various manipulator configurations to accomplish a wider range of tasks. MRRs represent the next generation of industrial manipulators that cope with the transition from mass to customer-oriented production. The main contributions of this thesis are: 1) mechanical design and calibration of multi-input multi-output (MIMO) joint modules of MRR, and 2) control design to handle multiple configuration and overcome disturbance due to dynamics uncertainty. From the mechanical design point of view, this thesis presents two main topics: 1) each joint is not only modularly designed, but also has multiple-input multiple-output (MIMO) physical connection ports, which contributes to the concept of reconfigurability. Strictly speaking, single-input single-output (SISO) modular joint falls into the category of modular manipulator, and the robot reconfiguration is achieved by integrating different types of modules. For example, with single revolute MIMO joint module, both rotary and pivotal joint can be generated. On the other hand, if you would like to switch from rotary movement to pivotal movement with a SISO joint module, using another pivotal joint module is the only way to achieve this goal, and 2) for precise automation application, joints and links should be accurately connected and oriented when reconfigured. Our proposed modular joint has four connection ports which can be configured as either a rotary joint or a pivotal joint. In addition, key and keyway connection mechanism provides high accuracy in positioning the link onto the joint. Therefore, this structure reduces or eliminates MRRs system calibration time when reconfigured. Furthermore, zero link offset when used as a pivotal joint increases the robot dexterity, maximizes the reachability, and results in kinematics simplicity. The main challenge in the control of an MRR system with harmonic drives (HD) is the significant uncertainties due to friction, unmodelled dynamics, varying payload, gravitation, dynamic coupling between motions of joints, and the configuration changes. In order to compensate all unpredictable effects, we proposed a decentralized saturation-type robust control scheme based on direct-Lyapunov method and backstepping techniques. To better understand the system dynamics behavior, the HD flexspline compliance and friction calibration and results are also provided. The results are used for controller design. The proposed controller is verified through both computer simulation and experimental analysis.

Carbon dioxide is a main greenhouse gas that is responsible for global warming and climate change. The reduction in greenhouse gas emission is required to comply with the Kyoto Protocol. Looking at CO2 emissions distribution in Canada, the electricity and heat generation sub-sectors are among the largest sources of CO2 emissions. In this study, the focus is to reduce CO2 emissions from electricity generation through capacity expansion planning for utility companies. In order to reduce emissions, different mitigation options are considered including structural changes and non structural changes. A drawback of existing capacity planning models is that they do not consider uncertainties in parameters such as demand and fuel prices. Stochastic planning of power production overcomes the drawback of deterministic models by accounting for uncertainties in the parameters. Such planning accounts for demand uncertainties by using scenario sets and probability distributions. However, in past literature different scenarios were developed by either assigning arbitrary values or by assuming certain percentages above or below a deterministic demand. Using forecasting techniques, reliable demand data can be obtained and can be inputted to the scenario set. The first part of this thesis focuses on long term forecasting of electricity demand using autoregressive, simple linear, and multiple linear regression models. The resulting models using different forecasting techniques are compared through a number of statistical measures and the most accurate model was selected. Using Ontario electricity demand as a case study, the annual energy, peak load, and base load demand were forecasted, up to year 2025. In order to generate different scenarios, different ranges in economic, demographic and climatic variables were used. The second part of this thesis proposes a robust optimization capacity expansion planning model that yields a less sensitive solution due to the variation in the above parameters. By adjusting the penalty parameters, the model can accommodate the decision maker’s risk aversion and yield a solution based upon it. The proposed model is then applied to Ontario Power Generation, the largest power utility company in Ontario, Canada. Using forecasted data for the year 2025 with a 40% CO2 reduction from the 2005 levels, the model suggested to close most of the coal power plants and to build new natural gas combined cycle turbines and nuclear power plants to meet the demand and CO2 constraints. The model robustness was illustrated on a case study and, as expected, the model was found to be less sensitive than the deterministic model.

Samhället och världen vi lever i är föränderlig, kanske mer än någonsin. Det finns en strävan efter tålighet och snabb återhämtning för att på ett så bra sätt som möjligt hantera händelser och förändringar som vi inte kunnat förutse. Robusta samhällen kan anses framstå som önskvärda i en föränderlig värld. Då samhällen består av människor kan det antas att robusta människor skapar goda förutsättningar för robusta samhällen. Syftet med denna studie är att få mer kännedom om hur en robust människa upplevs och om det finns likheter i beskrivningarna av ett robust samhälle och en robust människa. Studien genomfördes i form av tre semistrukturerade intervjuer med tolkande fenomenologisk analys. Tre stycken professionella, som i sin profession mött totalt ca 3000 människor som varit med om större händelser och/eller kriser, intervjuades. Studiens resultat visar att upplevelsen av en robust människa är kärleksfull, ansvarsfull i nuet och känslomedveten: Kärleksfull till sig själv, andra och livet. Ansvarsfull och i nuet, oavsett konsekvenserna Medveten om, och känner, sina känslor och gränser. Studien berör även likheter och skillnader mellan begreppen robust, resiliens, resilient och Känsla Av SAMmanhang (KASAM). Vidare visas att det finns likheter mellan hur robusta samhällen och hur robusta människor beskrivs. Robust samhälle          Robust människa Ekologisk robusthet        Strävar efter god hälsa. Social robusthet             Kan skilja på vad som är sina och andras känslor. Teknisk robusthet           Är kvar i sig själv vid yttre störningar. Studien kommer fram till att robusta människor troligen är en förutsättning för robusta samhällen. / The society and the world we live are in constant change, maybe more now than ever. There is an aim to be robust and to recover fast to be able meet unpredictable events and changes. Robust societies seem desirable in a changing world. Societies consist of individuals and robust individuals can be assumed to create good conditions for a robust society. The purpose of this study is to gain more knowledge regarding how a robust individual is perceived and if there are similarities in the way a robust society and a robust individual are described. The study was conducted using three semi-structured interviews and interpretive phenomenological analysis. Three professionals, who had meet in total about 3000 individuals who had experienced large events and/or crisis, were interviewed. The result of the study demonstrates the experience of a robust individual being loving, responsible in the present and aware of feelings. Loving to himself/herself, others and life. Responsible and in the present, disregarding the consequences Aware of, and feels, his/hers feelings and boundaries. Similarities and differences between robust, resilience, resilient and Sense Of Coherence (SOC) are briefly covered in the study. There are similarities between how a robust society and a robust individual are described. Robust society           Robust individual Ecological robustness    Aims at good health. Social robustness          Able to distinguish between his/her own feelings and others feelings. Technical robustness     Stays as himself/herself when disturbed. The result of the study shows that robust individuals are probably a prerequisite for a robust society.

The traditional AC/DC rectifier usually has the defects of low power factor and serious harmonic distortion and it results in serious pollution to the power system. This thesis proposes active power factor correction technique using a new AC/DC Dual Boost Converter. For power factor correction, inductor current is operated in the continuous conduction mode. First, the converter is analyzed by state space averaging method. Furthermore, we design applicable compensator by frequency analysis to implement a good power factor system. A classical PFC circuit with PI control law has low power factor under light load. In order to overcome problem, the thesis proposes a Dual Boost Converter circuit with robust performance. Comparing with circuits using PFC IC ¡§UC3854¡¨, the proposed system obtains higher power factor under the condition of the same light load.

A digital position servo controller for the Electro-hydraulic system is proposed based on robust integral structure control (RISC) scheme. The main aims of the proposed system are to enhance the performance while driving the servo machine rod to track a sine-wave or step command. According to the state feed-back theorems, a simplified plant model of the Electro-hydraulic is conducted. Close-loop characteristic function of the control system will be assigned on the stable plane to ensure the state variables so that it can rapidly converge to stable point. The design steps and theoretical analysis will be described in detail. Both simulation and experimental results are shown for proving the performance.

This thesis aims to cover some aspects of synthesis and design of Proportional- Integral-Derivative (PID) controllers. The topics include computer aided design of discrete time controllers, data-based design of discrete PID controllers and data- robust design of PID controllers. These topics are of paramount in control systems literature where a lot of stress is laid upon identification of plant and robust design. The computer aided design of discrete time controllers introduces a Graphical User Interface (GUI) based software. The controllers are: Proportional (P), Proportional-Derivative (PD),Proportional-Integral (PI) and Proportional-Integral- Derivative (PID) controllers. Different performance based design methods with these controllers have been introduced. The user can either explore the performance by interactively choosing controllers one by one from the entire set and visualizing its performance or specify some performance constraints and obtaining the resulting set. In data-based design, the thesis presents a way of designing PID controllers based on input-output data. Thus, the intermediate step of identification of model from data is removed, saving considerable effort. Moreover, the data required is step response data which is easier to obtain in case of discrete time system than frequency response data. Further, a GUI developed for interactive design is also described. In data-robust design, the problem of uncertainty in data is explored. The design method developed finds the stabilizing set which can robustly stabilize the plant with uncertainty. It has been put forward as an application to interval linear programming. The main results of this research include a new way of designing discrete time PID controllers directly from the data. The simulations further confirm the results. Robust design of PID controllers with data uncertainty has also been established. Additionally, as a part of this research, a GUI based software has been developed which is expected to be very beneficial to the designers in manufacturing, aerospace and petrochemical industries. PID controllers are widely used in the industry. Any progress in this field is well acknowledged both in the industry and the academia alike. This thesis attempts a small step further in this direction.