Multirate control strategies for avoiding sample losses. Application to UGV path tracking

When in a digital control strategy there are samples lost due to limitations, diff t multirate (MR) control options can be used for solving the problem: Dual-rate inferential control (IC) and model-based dual-rate control (MBDR). The objective of this contribution is to analyze, compare, and to assess their behavior under different perspectives. Is a dual-rate inferential control better than a model-based dual-rate control? Both options lead to a periodically time-varying discrete-time system and for this reason a lifted modeling is considered. An efficient algorithm is used for computing a MR systems frequency response for these control structures. The robust performance and disturbance effects are studied in detail under sample losses and process uncertainty, and some considerations are reported. A new QFT (quantitative feedback theory) procedure for dual-rate systems analysis is also described. Analysis and simulation examples and experimental results for UGV path tracking are introduced in this work, revealing that MBDR outperforms IC when the model contains important uncertainties.Comment: 41 pages, 24 figure

Derivation of Black Carbon Proxies in an Integrated Urban Air Quality Monitoring Network

Air pollution is one of the biggest environmental health challenges in the world, especially in the urban regions where about 90% of the world’s population lives. Black carbon (BC) has been demonstrated to play an important role in climate change, air quality and potential risk for human beings. BC has also been suggested to associate better with health effects of aerosol particles than the commonly monitored particulate matter, which does not solely originate from combustion sources. Furthermore, BC has been recommended to be included as one of the parameters in air quality index (AQI) which is communicated to citizens. However, due to financial constraints and the lack of the national legislation, BC has yet been measured in every air quality monitoring station. Therefore, some researchers developed low-cost sensors which give indicative ambient BC concentrations as an alternative. Even so, due to instrument failure or data corruption, measurements by physical sensors are not always possible and long data gaps can exist. With missing data, the data analysis of interactions between air pollutants becomes more uncertain; therefore, air quality models are needed for data gap imputation and, moreover, for sensor virtualization. To complement the current deficiency, this thesis aims to derive statistical proxies as virtual sensors to estimate BC by using the current air quality monitoring network in Helsinki metropolitan area (HMA). To achieve this, we first characterized the ambient BC concentrations in four types of environments in HMA: traffic site (TR: 0.77–2.08 μg m−3), urban background (UB: 0.51–0.53 μg m−3), detached housing (DH: 0.64–0.80 μg m−3) and regional background (RB: 0.27–0.28 μg m−3). TR, in general, had higher BC concentrations due to the close proximity to vehicular emission but decreasing trends (–10.4 % yr−1) likely thanks to the fast renewal of the city bus fleet in HMA. UB, on the other hand, had a more diverse source of BC, including biomass burning and traffic combustion. Its trend had also been decreasing, but at a smaller rate (e.g. UB1: –5.9 % yr−1). We then narrowed down the dataset to a street canyon site and an urban background site for BC proxy derivation. At both sites, despite the low correlation with meteorological factors, BC correlated well with other commonly monitored air pollutant parameters by both reference instruments and low-cost sensors, such as NOx and PM2.5. Based on this close association, we developed a statistical proxy with adaptive selection of input variables, named input-adaptive proxy (IAP). This white-box model worked better in terms of accuracy at the street canyon site (R2 = 0.81–0.87) than the urban background site (R2 = 0.44–0.60) because of the scarce missing gaps in data in the street canyon. When compared with other white- and black-box models, IAP is preferred because of its flexibility and architectural transparency. We further demonstrated the feasibility of sensor virtualization by using statistical proxies like IAP at both sites. We also stressed that such virtual sensors are location specific, but it might be possible to extend the models from one street canyon site to another with a calibration factor. Similarly, the proposed methodology can also be applied to estimate other air pollutant parameters with scarcity of data, such as lung deposited surface area and ultrafine particles, to complement the existing AQI.

Integrated performance prediction and quality control in manufacturing systems

textPredicting the condition of a degrading dynamic system is critical for implementing successful control and designing the optimal operation and maintenance strategies throughout the lifetime of the system. In many situations, especially in manufacturing, systems experience multiple degradation cycles, failures, and maintenance events throughout their lifetimes. In such cases, historical records of sensor readings observed during the lifecycle of a machine can yield vital information about degradation patterns of the monitored machine, which can be used to formulate dynamic models for predicting its future performance. Besides the ability to predict equipment failures, another major component of cost effective and high-throughput manufacturing is tight control of product quality. Quality control is assured by taking periodic measurements of the products at various stages of production. Nevertheless, quality measurements of the product require time and are often executed on costly measurement equipment, which increases the cost of manufacturing and slows down production. One possible way to remedy this situation is to utilize the inherent link between the manufacturing equipment condition, mirrored in the readings of sensors mounted on that machine, and the quality of products coming out of it. The concept of Virtual Metrology (VM) addresses the quality control problem by using data-driven models that relate the product quality to the equipment sensors, enabling continuous estimation of the quality characteristics of the product, even when physical measurements of product quality are not available. VM can thus bring significant production benefits, including improved process control, reduced quality losses and higher productivity. In this dissertation, new methods are formulated that will combine long-term performance prediction of sensory signatures from a degrading manufacturing machine with VM quality estimation, which enables integration of predictive condition monitoring (prediction of sensory signatures) with predictive manufacturing process control (predictive VM model). The recently developed algorithm for prediction of sensory signatures is capable of predicting the system condition by comparing the similarity of the most recent performance signatures with the known degradation patterns available in the historical records. The method accomplishes the prediction of non-Gaussian and non-stationary time-series of relevant performance signatures with analytical tractability, which enables calculations of predicted signature distributions with significantly greater speeds than what can be found in literature. VM quality estimation is implemented using the recently introduced growing structure multiple model system paradigm (GSMMS), based on the use of local linear dynamic models. The concept of local models enables representation of complex, non-linear dependencies with non-Gaussian and non-stationary noise characteristics, using a locally tractable model representation. Localized modeling enables a VM that can detect situations when the VM model is not adequate and needs to be improved, which is one of the main challenges in VM. Finally, uncertainty propagation with Monte Carlo simulation is pursued in order to propagate the predicted distributions of equipment signatures through the VM model to enable prediction of distributions of the quality variables using the readily available sensor readings streaming from the monitored manufacturing machine. The newly developed methods are applied to long-term production data coming from an industrial plasma-enhanced chemical vapor deposition (PECVD) tool operating in a major semiconductor manufacturing fab.Mechanical Engineerin

Controle PID preditivo para plantas com atraso de transporte: estudo de caso em um laminador

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-graduação em Engenharia Elétrica.Os controladores PID (proporcional, integral e derivativo) avançados são controladores compostos de estrutura PID, cujos parâmetros são sintonizados por um dos métodos pertencentes ao controle inteligente, robusto, preditivo, adaptativo, não-linear ou ótimo, sem que se modifique o princípio de atuação desses parâmetros. A proposta desta dissertação é desenvolver e comparar diferentes técnicas de controle PID avançadas aplicadas ao controle, em ambiente de simulação, da espessura de chapas de aço produzidas por um laminador quádruo simples. A motivação deve-se ao fato de ser a laminação a área de maior influência no custo operacional do aço, além do constante esforço dos engenheiros para melhorar o desempenho, a produtividade e a qualidade do produto. No estudo de caso obtém-se a modelagem de uma cadeira de laminação, observando-se as variáveis pertinentes ao controle, bem como a descrição dos principais problemas como o atraso de transporte inerente à medição de espessura e o efeito da excentricidade devida a imperfeição dos cilindros. A dissertação aborda estratégias de controle PID avançados visando melhor o tratamento do atraso de transporte, usando abordagens de controle preditivo generalizado e compensadores PID, apresentando as etapas de desenvolvimento das abordagens avançadas a partir da técnica clássica. As simulações são realizadas para análises do comportamento regulatório da malha de controle, da robustez e da tolerância à excentricidade em regime permanente

A lifelogging system supporting multimodal access

Today, technology has progressed to allow us to capture our lives digitally such as taking pictures, recording videos and gaining access to WiFi to share experiences using smartphones. People’s lifestyles are changing. One example is from the traditional memo writing to the digital lifelog. Lifelogging is the process of using digital tools to collect personal data in order to illustrate the user’s daily life (Smith et al., 2011). The availability of smartphones embedded with different sensors such as camera and GPS has encouraged the development of lifelogging. It also has brought new challenges in multi-sensor data collection, large volume data storage, data analysis and appropriate representation of lifelog data across different devices. This study is designed to address the above challenges. A lifelogging system was developed to collect, store, analyse, and display multiple sensors’ data, i.e. supporting multimodal access. In this system, the multi-sensor data (also called data streams) is firstly transmitted from smartphone to server only when the phone is being charged. On the server side, six contexts are detected namely personal, time, location, social, activity and environment. Events are then segmented and a related narrative is generated. Finally, lifelog data is presented differently on three widely used devices which are the computer, smartphone and E-book reader. Lifelogging is likely to become a well-accepted technology in the coming years. Manual logging is not possible for most people and is not feasible in the long-term. Automatic lifelogging is needed. This study presents a lifelogging system which can automatically collect multi-sensor data, detect contexts, segment events, generate meaningful narratives and display the appropriate data on different devices based on their unique characteristics. The work in this thesis therefore contributes to automatic lifelogging development and in doing so makes a valuable contribution to the development of the field

Model-based fault detection and control design - applied to a pneumatic Stewart-Gough platform

The safety and functionality of engineering systems can be affected adversely by faults or wear in system components. Therefore, methods for detecting such faults/wear and ameliorating their effects to avoid system failure are important. Designing schemes for the detection and diagnosis of faults is becoming increasingly important in engineering due to the complexity of modern industrial systems and growing demands for quality, cost efficiency, reliability, and the safety issue. In safety/mission critical applications, fault detection can be combined with accommodation/reconfiguration (after a fault) to achieve fault tolerance allowing the system to complete or abort its function in a way that is sub-optimal but does achieve the design objective. This thesis discusses research carried-out on the development and validation of a model-based fault detection and isolation (FDI) system for a pneumatically actuated Stewart platform. The Stewart-Gough platform provides six degrees of freedom consisting of three translational and three rotational degrees of freedom (x, y, z, pitch, roll, ;yaw). As these platforms can be fast acting (rapid motion) and can handle reasonable loads, they can become dangerous, especially when fault(s) in the platform mechanism, drivetrain or control system occur. Therefore, as a safety critical application it is imperative that fault tolerant schemes are applied in order to provide a safe working environment. The design concept of the FDI scheme for the full Stewart-Gough platform is first designed using a single cylinder set-up. This modular concept is adopted so that a robust fault tolerant control scheme can be designed basically off-line (i.e. not attached to the Stewart Gough platform). This approach is adopted as requirements are easier to understand using a single cylinder set-up. The modular design approach subdivides the whole system into smaller sections (modules) that can be independently created and then used in the complete Stewart-Gough platform. The main contributions of the work are that a pneumatically actuated Stewart-Gough platform has been designed, built, and commissioned. A mathematical model has been developed and has been validated against experimental results. Two control approaches have been designed and compared. A fundamental comparative study of parity equations and Kalman filter observer banks for fault detection in pneumatic actuators has been conducted. The parity equations and Kalman filter approaches have been extended to provide a combined fault detection scheme. The FDI and control schemes have been combined in a modular Fault Tolerant Control (FTC) scheme for a pneumatic cylinder. The resulting FTC scheme has been validated by experimentation and demonstrated on the single cylinder test rig. The FTC scheme has been extended to all 6 cylinders (and including fault management at top level) of Stewart-Gough platform. The FTC scheme has been validated by experimentation and demonstrated on the Stewart-Gough platform test rig.EThOS - Electronic Theses Online ServiceGBUnited Kingdo