Global ETD Search

71	The behaviour of S.C.R. amplifiers in closed loop control systems Farmer, A. R. January 1966 (has links) No description available. 003.5
72	The mathematical representation of distributed flow processes for automatic control Bush, S. F. January 1965 (has links) No description available. 003.5
73	Problems arising from the analysis of randomly disturbed non-linear control systems Barrett, J. F. January 1960 (has links) No description available. 003.5
74	Cooperation by observation for heterogeneous robots Kagalidis, Ioannis January 2009 (has links) No description available. 003.5
75	A Modular Framework for Multi-robot Localization Scenarios Kondaxakis, Polychronis January 2004 (has links) Localization, in mobile robotics, is the ability of the vehicles to discern their position and orientation into their environment. Today, there are two main approaches to the localization problem. Deterministic techniques use recursive estimation algorithms to determine the robots state vector directly while at the same time filtering out potential noise, which corrupts the system. Alternatively,. probabilistic techniques recursively update the probability of a mobile robot to occupy a particular position in space. Both of these contrasting techniques exhibit particular advantages and disadvantages in real-time implementation. For example the deterministic algorithms have less memory and processing requirements from their counterparts, and that makes them ideal for real-time applications. This thesis presents a modular localization framework based on extended Kalman filter estimators, which is applied in both single and multi robot scenarios. The modularity of the framework is demonstrated in the multi-robot scenarios where robots can be easily added and removed from the group in real-time with only changing (degrading or upgrading) the system performance but not disabling it completely. The key idea for performing collective localization is that the group of robots must be viewed as a single centralized system where a,Centralized Extended Kalman Filter (CEKF) is implemented to track the sequential positions of a number of robots in an initially known environment. The system is tested for situations ~ where the robots have access to al1solute environmental landmarks and where the robots rely only on relative distance and bearing measurements. Furthermore, methods to parallelize the CEKF algorithm and distribute it amongst the number of robots that occupy the team are demonstrated and teste'd. This technique can be used for real-time implementation scenarios where the designer needs to take full advantage of the robots' local processing capabilities, which otherwise would be impossible with the centralized form ofthe algorithm. 003.5
76	Off-line and on-line parameter estimation of induction machines Huynh, Duy Chau January 2010 (has links) No description available. 003.5
77	Multiple-model structures applied to fault tolerant control in heating, ventilation and air conditioning systems Silva, Pedro Miguel Abreu January 2011 (has links) No description available. 003.5
78	J-Spectral Factorisaiton for Automotive Powertrain Controls Ward, Christopher Patrick January 2009 (has links) No description available. 003.5
79	The optimization of control systems by the hill-climbing technique Levy, N. M. January 1973 (has links) No description available. 003.5
80	An investigation into the compensation, stability and random-input analysis of sampled-data control systems Ziedan, I. E. M. January 1967 (has links) No description available. 003.5

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