Many communication channels have a power-to-noise ratio (PNR) which is not constant in time, producing a time-varying error probability. If a feedback channel is available, the receiver can request changes in certain transmitter parameters in response to the changing channel conditions. In this thesis a scheme for adaptively changing the data rate by varying the duration of the transmitted pulses in order to compensate for this fluctuating PNR is described and analyzed. Implicit equations for the optimum rate request as a function of past and current instantaneous PNR have been derived for an arbitrary probability density function of the PNR. The effects of a bandwidth constraint, of time delay in the feedback link, and of time and amplitude discrete rate requests have been included in the analysis. Application of adaptive rate control to the Rayleigh fading channel can produce an enormous reduction in required transmitter power over a fixed rate non-diversity system, up to 50 db for typical values of error probability. For the same values of bandwidth, data rate, and error probability, and for typical values of feedback delay, the variable rate system can still effect a power reduction in the range 15-18 db, or a factor of 30-60, over the best alternative scheme, known as maximal-ratio predetection combined frequency diversity. A method is given which allows tradeoffs between power, bandwidth and data rate for two-way communication over Rayleigh fading channels to be examined graphically. Adaptive rate control on multi-user channels produces a smaller improvement. For the range of parameters considered likely, there is a maximum of about 1.7 db improvement over a fixed rate system. Although the magnitude of the improvement introduced by adaptive rate control is strongly dependent on the probability density function of the PNR, it has been shown that for at least one commonly occurring class of time-varying channels the savings are well worth the cost of implementation. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

D.Ing. / The grasping strategy for a three dimensional object by a robotic gripper requires a geometrical reasoning and analysis of the physical gripper design, control and operation. The work addresses the problem of data acquisition and processing required for an object recognition and its application in the selection of grasping strategy for a given gripper. The system described in the thesis integrates the analyses of image data, object geometry and grasping operation in a systematic way. It is hierarchically constructed in several levels of analyses and processes including object recognition, grasping feature representation and classification, matching strategy for objects and the gripper and grasping description and operation. Object shape features are taken for recognition based on the image data collected through an infrared sensor. With a face relation graph proposed, an object model is built for describing the object geometrical properties and extracting its grasping features. A coding system based on group technology concepts is proposed for object classification. It describes object features relative to grasping operation. Gripping models are established and incorporated with the coding system for analysis of object gripping features. By means of the gripping models and the coding system, objects to be grasped are classified and grouped into specific families according to their similarities in gripping. The information transformation between the object and the gripper is made through a matrix representation. An object matrix describes the selection of gripping faces and object geometry for gripping , while a gripper matrix describes the fingers selection and its configuration in correspondence with the object to be grasped. The matching of the matrices is established through a knowledge-based reasoning approach. The grasping operation is controlled by a computer in terms of the commands generated by the gripper matrix through a gripper code. The design of the generic gripper for this application is described.

The broad theory of adaptive control is introduced, with m o t i v a t i o n for using such techniques. The two mos t popu l a r techniques, the Model Re f er e n c e A d a ptive C o n t r o l l e r s (MRAC) and the Self Tuning C o n t r o l l e r s (STC) are studied in more d e t a i l . The MRAC and the STC often lead to identical solutions. The c on d i t i o n s for which these two techni q u e s are e q u i v a l e n t are discussed. P a r a m e t e r Adap t a ti o n A l go r i t h m s (PAA) are required by both the MRA a n : the STC. For this reason the PAA is e x a m i ne d in some det.ai . This is i n itiated by de r i v i ng an o f f - l i n e lea; -squares PAA. This is then c o n v e r t e d into a r ec u r s i v e on-l in e estimator. Using intuitive arguments, the various choices of gain p a r a m e t e r as well as the v a r ia t i o n s of the nasic form o f the a l g o r i t h m are discussed. This i n c l ud e s a w a r n in g as to w here the p i tf a l l s of such a l g o r i t h m s may lie. In order to examine the s t a b il i t y of these a lgorithms, the H y p e r s t a b i l i t y theorem is introduced. This requires k n o w l e d g e of the Popov i n e q ua l i t y and Stric t l y P o s itive Real (SPR) functions. This is intro d u c ed initially using i n t u i t i v e ene i g y concepts after which the r i g o r ou s m a t h e m a t i c a l representa* ion is d e r i v e d . The H y p e r s t a b i l i t y T h e o r e m is then used to exam i n e the s t a b i l i t y condition for various forms of the PAA.

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This thesis investigates the broad theme of guaranteeing the stability of nonlinear control systems. In the first section, we describe the application of discrete controller for the stabilization of certain nonlinear stochastic control systems subject to unavailability of state measurements. In the second section, we consider input constrained nonlinear systems and characterize the region from which stabilization to the origin is possible. We then use this information to design a controller which stabilizes everywhere in this set. / Thesis / Master of Applied Science (MASc)

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