An event-driven approach to control and optimization of multi-agent systems
MetadataShow full item record
This dissertation studies the application of several event-driven control schemes in multi-agent systems. First, a new cooperative receding horizon (CRH) controller is designed and applied to a class of maximum reward collection problems. Target rewards are time-variant with finite deadlines and the environment contains uncertainties. The new methodology adapts an event-driven approach by optimizing the control for a planning horizon and updating it for a shorter action horizon. The proposed CRH controller addresses several issues including potential instabilities and oscillations. It also improves the estimated reward-to-go which enhances the overall performance of the controller. The other major contribution is that the originally infinite-dimensional feasible control set is reduced to a finite set at each time step which improves the computational cost of the controller. Second, a new event-driven methodology is studied for trajectory planning in multi-agent systems. A rigorous optimal control solution is employed using numerical solutions which turn out to be computationally infeasible in real time applications. The problem is then parameterized using several families of parametric trajectories. The solution to the parametric optimization relies on an unbiased estimate of the objective function's gradient obtained by the "Infinitesimal Perturbation Analysis" method. The premise of event-driven methods is that the events involved are observable so as to "excite" the underlying event-driven controller. However, it is not always obvious that these events actually take place under every feasible control in which case the controller may be useless. This issue of event excitation, which arises specially in multi-agent systems with a finite number of targets, is studied and addressed by introducing a novel performance measure which generates a potential field over the mission space. The effect of the new performance metric is demonstrated through simulation and analytical results.
Showing items related by title, author, creator and subject.
Processing of Synthetic Aperture Radar Images by the Boundary Contour System and Feature Contour System Cruthirds, Dan; Gove, Alan; Grossberg, Stephen; Mingolla, Ennio; Nowak, Nicholas; Williamson, James (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1992-02)An improved Boundary Contour System (BCS) and Feature Contour System (FCS) neural network model of preattentive vision is applied to two large images containing range data gathered by a synthetic aperture radar (SAR) sensor. ...
Processing of Synthetic Aperture Radar Images by a Multiscale Boundary Contour System and Feature Contour System Grossberg, Stephen; Mingolla, Ennio; Ross, William D. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1993-01)An improved Boundary Contour System (BCS) and Feature Contour System (FCS) neural network model of preattentive vision is applied to large images containing range data gathered by a synthetic aperture radar (SAR) sensor. ...
A study of educational development in Jamaica; of interacting forces affecting its development; of the place of each phase within the system and of the implications the newly evolving system hold for the future. Dilworth, Walbert Wilberforce Gladstone (Boston University, 1960)