Energy-aware MPC co-design for DC-DC converters

In this paper, we propose an integrated controller design methodology for the implementation of an energy-aware explicit model predictive control (MPC) algorithms, illustrat- ing the method on a DC-DC converter model. The power consumption of control algorithms is becoming increasingly important for low-power embedded systems, especially where complex digital control techniques, like MPC, are used. For DC-DC converters, digital control provides better regulation, but also higher energy consumption compared to standard analog methods. To overcome the limitation in energy efficiency, instead of addressing the problem by implementing sub-optimal MPC schemes, the closed-loop performance and the control algorithm power consumption are minimized in a joint cost function, allowing us to keep the controller power efficiency closer to an analog approach while maintaining closed-loop op- timality. A case study for an implementation in reconfigurable hardware shows how a designer can optimally trade closed-loop performance with hardware implementation performance

Guidance, flight mechanics and trajectory optimization. Volume 10 - Dynamic programming

Dynamic programming and multistage decision processes in guidance, flight mechanics, and trajectory optimizatio

Investor Behavior and Fund Performance under a Privatized Retirement Accounts System: Evidence from Chile

In the U.S. and in Chile, there have been heated debates about the relative merits of a decentralized privatized pension system relative to a more traditional social security system. On the firm side, there are concerns that pension funds engage in anticompetitive behavior and take advantage of consumers’ by charging high fees and account maintenance changes. On the consumer side, there are concerns that consumers do not select wisely among funds and take on too much risk. Any pension system with insurance features to protect against low levels of pension accumulations is potentially subject to moral hazard problems, in the form of consumers’ taking on too much risk. In the case of Chile, the government provides a minimum pension benefit to those with low pension accumulations, which can make some consumers more willing to take risks. For these reasons, the Chilean government introduced regulations on pension fund firms’ investments designed to limit risk. This paper analyzes the determinants of consumers’ choices of pension fund and of pension fund characteristics (performance and fees), taking into account governmental regulations. In particular, it estimates a demand and supply model of the pension fund investment market using a longitudinal household dataset gathered in 2002 and 2004 in Chile, administrative data on fund choices, and longitudinal data on cost determinants of pension funds. We find that the existing regulation actually increases the level of risk in the market, reduces heterogeneity across firms, and reduces incentives for consumers to participate in the pension fund program. We suggest alternative more effective forms of regulation.

Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network

Application of advanced on-board processing concepts to future satellite communications systems: Bibliography

Abstracts are presented of a literature survey of reports concerning the application of signal processing concepts. Approximately 300 references are included