Η παρούσα διδακτορική διατριβή έχει ως κύριο αντικείμενο μελέτης την μοντελοποίηση και έλεγχο ενός μικρορομποτικού βραχίονα αναλυόμενου σε σφαιρικά πεπερασμένα στοιχεία σε περιβάλλον με εξωτερικές δυνάμεις Van Der Waals και συνυπολογίζοντας την τριβή. Τα κύρια σημεία είναι η εισαγωγή των εξωτερικών δυνάμεων στο μοντέλο του μικρορομπότ, η δημιουργία προσαρμοστικού ελέγχου για την επίτευξη ακολουθίας τροχιάς με αναγνώριση και ακύρωση των ισχυρών μεταβαλλόμενων εξωτερικών δυνάμεων, η αναγνώριση της θέσης και η αποφυγή εμποδίων σε άγνωστο περιβάλλον κλίμακας μικρομέτρων και ο καθορισμός τροχιάς για προσέγγιση σημείων στον χώρο εργασίας του μικρορομπότ. Προτείνεται επίσης ένα σύστημα επενέργησης σε διάταξη τένοντα με νανοκαλώδια και γίνεται μελέτη της αντοχής του σε σχέση με τις μέγιστες δυνάμεις-ροπές που παρουσιάζονται κατά τον έλεγχο. Για την αναγνώριση των εξωτερικών δυνάμεων δοκιμάζονται διαφορετικά είδη εκτιμητών και εξετάζεται η απόδοσή τους στο συνολικό σύστημα. / The present PhD thesis has a key object the modeling and control of a micro robotic manipulator, represented by spherical particles in an environment with external Van Der Waals forces and taking friction into account. The main points are a) the insertion of the external forces in the micro robot model, b) the adaptive control used in order to follow a desired trajectory, with identification and cancellation of the external forces, the position identification and avoidance of obstacles in an unstructured micrometer scale environment and the trajectory planning towards a target point in the task space of the microrobot. Also a tendon like actuation system is proposed, using nanowires and its mechanical properties are studied in order to determine the viability of its use in relation to the required torques during the control process. For the external force identification scheme, various types of estimators are proposed and their efficiency in the system is studied.

In the nearest future, the human driver is viewed as a reliable backup even for the fully automated road motor vehicles (cars). Indeed, the driver is assumed to swiftly take the control of the car in cases of suddenly occurring (i) challenging environmental conditions, (ii) complex unforeseen driving situations, or (iii) degradation of performance of the car. However, due to the cognitive overload in such a sudden, stressful takeover of the control, the driver would often experience the startle effect, which usually results in an unconscious, instinctive, yet incorrect response. An extreme case of startle is freezing, in which the driver might be incapable to respond to the sudden takeover of control at all. The possible approaches to alleviate the startle during the takeover of control (i.e., the automation startle) include an offset- (i.e., either early- or delayed-), gradual yielding the controls to the driver. In the cases considered above, however, these approaches are hardly applicable because of (i) the presumed unpredictability of the events that result in the need of takeover of control, and (ii) the severe time constraints of the latter. Conversely, the objective of our research is to propose an approach of minimizing the need of yielding the control to the driver in challenging environmental conditions by guaranteeing an adequate automated control in these conditions. Focusing on slippery roads as an instance of challenging conditions, and steering control as an instance of control, we aim at developing such an automated steering that controls the car adequately in various road surfaces featuring low friction coefficients without the need of driver’s intervention.In order to develop such an automated steering we employed an in-house evolutionary computation framework – XML-based genetic programming (XGP) – which offers a flexible, portable, and human readable representation of the evolved optimal steering functions. The trial runs of the evolved steering functions were performed in the Open Source Racing Car Simulator (TORCS), which features a realistic, yet computationally efficient simulation of the car and its environment. The obtained experimental results indicate that due to the challenging dynamics of the unstable car on slippery roads, neither the canonical (tuned) servo-control (as a variant of PD) nor the (tuned) PID-controller could control the car adequately on slippery roads. On the other hand, the controller, featuring a relaxed, arbitrary structure evolved by XGP outperforms both the servo- and PID controllers in that it results in a minimal deviation of the car from its intended trajectory in rainy, snowy, and icy road conditions. Moreover, the evolved steering that employs anticipated perceptions is even superior as it could anticipate the imminent understeering of the car at the entry of the turns and consequently – to compensate for such an understeering by proactively turning the steering wheels in advance – well before entering the turn. The obtained results suggest a human competitiveness of the evolved automated steering as it outperforms the commonly used alternative steering controllers proposed by human experts. The research could be viewed as a step towards the evolutionary development of automated steering of cars in challenging environmental conditions. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University