Elektrikli tekerlekli sandalyenin ayrık-zaman optimal kontrolü

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Anahtar kelimeler: Elektrikli Tekerlekli Sandalye (ETS), Açısal Hız, Bozucu, Gözlemci, Model Öngörülü Kontrol (MÖK), Bozucu Gözleyici Destekli Model Öngörülü Kontrol (BGDMÖK) Elektrikli tekerlekli sandalye (ETS) engelli kişilerce kullanılan hareketlilik yardımcısı cihazlardır. Bağımsız hareket etmesi gereken veya el ile tekerlekli sandalye kullanamayan insanlar için ETS yararlıdır ve gereklidir. Tekerlekli sandalyede hız kontrol edilecek en önemli unsurdur. ETS sürüşü sırasında ortaya çıkan belirsiz çevre etkileri veya bozucuları ETS hız kontrolünün temel problemidir. Bu tez çalışmasında, çok giriş çok çıkışlı ve kublajlı olan ETS'nin sağ tekerlek ve sol tekerlek açısal hızlarını bir birinden bağımsız olarak kontrol etmek ve bozucu etkisini ortadan kaldırmak için kontrol yöntemleri önerilmiş ve tasarımları yapılmıştır. ETS'nin enerji denklemleri yazılır ve bu denklemlerden ayrık-zaman durum denklemleri doğrudan elde edilerek ETS modellenir. Durum uzay modeli kullanılarak Luenberger gözleyici sağ tekerlek ve sol tekerlek DC motor akımlarını ve hızlarını kestirmek için tasarlanır. ETS’nin hız kontrolönü yapmak üzere ayrık-zaman optimal Model Öngörülü Kontrol (MÖK) ve bozucu etkisini ortadan kaldırmak için Bozucu Gözleyici Destekli Model Öngörülü Kontrol (BGDMÖK) önerileri yapılır ve ETS nin ayrık-zaman durum uzay modeli kullanılarak tasarım yapılır. ETS’nin elde edilen ayrık zaman durum uzay model doğrulaması, ETS’nin sağ ve sol teker hızlarının bozucu etkiler altında bağımsız kontrölü için önerilen MÖK ve BGDMÖK yöntemlerinin performans değerlendirmeleri ve karşılaştırmaları benzetim çalışmaları ile verilmektedir. DISCRETE TIME OPTIMAL CONTROL OF ELECTRIC POWERED WHEELCHAIR (EPW)Keywords: Electric Powered Wheelchair (EPW), angular velocity, disturbance, observer, Model Predictive Control (MPC), Disturbance Observer Support to Model Predictive Control (DOSMPC) Electric powered wheelchair (EPW) is the mobility assistive device used by disabled persons. EPW is useful and necessary for people who are not able to use a manual wheelchair or for people who must move independently. The velocity of wheelchair is the important aspect to be controlled. The uncertain environmental effects or disturbances occuring during the EPW driving is the major problem of EPW velocity control. In this thesis, control methods have been proposed and implemented to eliminate the disturbance effect and to independently control the right and left wheel angular velocities of EPW that is a coupled and multi-input multi-output system. The energy equations are written and EPW is modeled by obtaining the discrete time state equations from the energy equations directly. By using state space model, the Luenberger observer is designed to estimate DC motor currents and velocities of right and left wheels. Discrete time optimal Model Predictive Control (MPC) for velocity control of EPW and Disturbance Observer Supported Model Predictive Control (DOSMPC) for eliminating disturbance effect are proposed and state space model of EPW is used in design. The discrete time state space model verification of the EPW is done by providing simulation results giving performance evaluation and comparison of MPC and DOSMPC methods proposed for independent velocity control of right and left wheels of ETS in the presence disturbance effect

Psychophysiological analysis of a pedagogical agent and robotic peer for individuals with autism spectrum disorders.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by ongoing problems in social interaction and communication, and engagement in repetitive behaviors. According to Centers for Disease Control and Prevention, an estimated 1 in 68 children in the United States has ASD. Mounting evidence shows that many of these individuals display an interest in social interaction with computers and robots and, in general, feel comfortable spending time in such environments. It is known that the subtlety and unpredictability of people’s social behavior are intimidating and confusing for many individuals with ASD. Computerized learning environments and robots, however, prepare a predictable, dependable, and less complicated environment, where the interaction complexity can be adjusted so as to account for these individuals’ needs. The first phase of this dissertation presents an artificial-intelligence-based tutoring system which uses an interactive computer character as a pedagogical agent (PA) that simulates a human tutor teaching sight word reading to individuals with ASD. This phase examines the efficacy of an instructional package comprised of an autonomous pedagogical agent, automatic speech recognition, and an evidence-based instructional procedure referred to as constant time delay (CTD). A concurrent multiple-baseline across-participants design is used to evaluate the efficacy of intervention. Additionally, post-treatment probes are conducted to assess maintenance and generalization. The results suggest that all three participants acquired and maintained new sight words and demonstrated generalized responding. The second phase of this dissertation describes the augmentation of the tutoring system developed in the first phase with an autonomous humanoid robot which serves the instructional role of a peer for the student. In this tutoring paradigm, the robot adopts a peer metaphor, where its function is to act as a peer. With the introduction of the robotic peer (RP), the traditional dyadic interaction in tutoring systems is augmented to a novel triadic interaction in order to enhance the social richness of the tutoring system, and to facilitate learning through peer observation. This phase evaluates the feasibility and effects of using PA-delivered sight word instruction, based on a CTD procedure, within a small-group arrangement including a student with ASD and the robotic peer. A multiple-probe design across word sets, replicated across three participants, is used to evaluate the efficacy of intervention. The findings illustrate that all three participants acquired, maintained, and generalized all the words targeted for instruction. Furthermore, they learned a high percentage (94.44% on average) of the non-target words exclusively instructed to the RP. The data show that not only did the participants learn nontargeted words by observing the instruction to the RP but they also acquired their target words more efficiently and with less errors by the addition of an observational component to the direct instruction. The third and fourth phases of this dissertation focus on physiology-based modeling of the participants’ affective experiences during naturalistic interaction with the developed tutoring system. While computers and robots have begun to co-exist with humans and cooperatively share various tasks; they are still deficient in interpreting and responding to humans as emotional beings. Wearable biosensors that can be used for computerized emotion recognition offer great potential for addressing this issue. The third phase presents a Bluetooth-enabled eyewear – EmotiGO – for unobtrusive acquisition of a set of physiological signals, i.e., skin conductivity, photoplethysmography, and skin temperature, which can be used as autonomic readouts of emotions. EmotiGO is unobtrusive and sufficiently lightweight to be worn comfortably without interfering with the users’ usual activities. This phase presents the architecture of the device and results from testing that verify its effectiveness against an FDA-approved system for physiological measurement. The fourth and final phase attempts to model the students’ engagement levels using their physiological signals collected with EmotiGO during naturalistic interaction with the tutoring system developed in the second phase. Several physiological indices are extracted from each of the signals. The students’ engagement levels during the interaction with the tutoring system are rated by two trained coders using the video recordings of the instructional sessions. Supervised pattern recognition algorithms are subsequently used to map the physiological indices to the engagement scores. The results indicate that the trained models are successful at classifying participants’ engagement levels with the mean classification accuracy of 86.50%. These models are an important step toward an intelligent tutoring system that can dynamically adapt its pedagogical strategies to the affective needs of learners with ASD