Browsing by Author "Coraluppi, Stephano P."
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Item Optimal Estimation of Domains of Attraction for Nonlinear Dynamical Systems(1992) Coraluppi, Stephano P.; Tits, A.; Abed, E.; ISRThis thesis implements a recently proposed algebraic methodology for optimal domain of attraction estimation, and extends the method to include optimal estimation of the largest inscribed ball. In addition, a numerical optimal estimation methodology is proposed. The thesis addresses the important issues of Liapunov function construction and the optimal choice of parameters in the family of Liapunov functions. Several examples are included, including a detailed discussion of the classical inverted pendulum. Finally, the thesis addresses the importance of including a measure of the size of the domain of attraction as part of a generalized objective function in optimization-based controller design.Item Risk-Sensitive and Minimax Control of Discrete-Time, Finite-State Markov Decision Processes(1998) Coraluppi, Stephano P.; Marcus, Steven I.; ISRThis paper analyzes a connection between risk-sensitive and minimaxcriteria for discrete-time, finite-states Markov Decision Processes(MDPs). We synthesize optimal policies with respect to both criteria,both for finite horizon and discounted infinite horizon problems. Ageneralized decision-making framework is introduced, which includes asspecial cases a number of approaches that have been considered in theliterature. The framework allows for discounted risk-sensitive andminimax formulations leading to stationary optimal policies on theinfinite horizon. We illustrate our results with a simple machinereplacement problem.Item Risk-Sensitive, Minimax, and Mixed Risk-Neutral/Minimax Control of Markov Decision Processes(1998) Coraluppi, Stephano P.; Marcus, Steven I.; ISRThis paper analyzes a connection between risk-sensitive and minimaxcriteria for discrete-time, finite-state Markov Decision Processes(MDPs). We synthesize optimal policies with respect to both criteria,both for finite horizon and discounted infinite horizon problems. Ageneralized decision-making framework is introduced, leading tostationary risk-sensitive and minimax optimal policies on theinfinite horizon with discounted costs.We introduce the mixed risk-neutral/minimaxobjective, and utilize results from risk-neutral and minimax controlto derive an information state process and dynamic programmingequations for the value function. We synthesize optimal control lawsboth on the finite and infinite horizon, and establish the effectivenessof the controller as a tool to trade off risk-neutral and minimaxobjectives.