Search arXivSearch

arXiv · 1410.0083

Integrating active sensing into reactive synthesis with temporal logic constraints under partial observations

Abstract

We introduce the notion of online reactive planning with sensing actions for systems with temporal logic constraints in partially observable and dynamic environments. With incomplete information on the dynamic environment, reactive controller synthesis amounts to solving a two-player game with partial observations, which has impractically computational complexity. To alleviate the high computational burden, online replanning via sensing actions avoids solving the strategy in the reactive system under partial observations. Instead, we only solve for a strategy that ensures a given temporal logic specification can be satisfied had the system have complete observations of its environment. Such a strategy is then transformed into one which makes control decisions based on the observed sequence of states (of the interacting system and its environment). When the system encounters a belief---a set including all possible hypotheses the system has for the current state---for which the observation-based strategy is undefined, a sequence of sensing actions are triggered, chosen by an active sensing strategy, to reduce the uncertainty in the system's belief. We show that by alternating between the observation-based strategy and the active sensing strategy, under a mild technical assumption of the set of sensors in the system, the given temporal logic specification can be satisfied with probability 1.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jie Fu, Ufuk Topcu. 2014-10-01. Integrating active sensing into reactive synthesis with temporal logic constraints under partial observations. https://arxiv.org/abs/1410.0083

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Cooperative Multi-Agent Assignment over Stochastic Graphs via Constrained Reinforcement Learning

Constrained multi-agent reinforcement learning offers the framework to design scalable and almost surely feasible solutions for teams of agents operating in dynamic environments to carry out conflicting tasks. We address the challenges of multi-agent coordination through an unconventional formulation in which the dual variables are not driven to convergence but are free to cycle, enabling agents to adapt their policies dynamically based on real-time constraint satisfaction levels. The coordination relies on a light single-bit communication protocol over a network with stochastic connectivity. Using this gossiped information, agents update local estimates of the dual variables. Furthermore, we modify the local dual dynamics by introducing a contraction factor, which lets us use finite communication buffers and keep the estimation error bounded. Under this model, we provide theoretical guarantees of almost sure feasibility and corroborate them with numerical experiments in which a team of robots successfully patrols multiple regions, communicating under a time-varying ad-hoc network.

eess.SY

Which Top Energy-Intensive Manufacturing Countries Can Compete in a Renewable Energy Future?

In a world increasingly powered by renewables and aiming for greenhouse gas-neutral industrial production, the future competitiveness of todays top manufacturing countries is questioned. This study applies detailed energy system modeling to quantify the Renewable Pull, an incentive for industry relocation exerted by countries with favorable renewable conditions. Results reveal that the Renewable Pull is not a cross-industrial phenomenon but strongly depends on the relationship between energy costs and transport costs. The intensity of the Renewable Pull varies, with China, India, and Japan facing a significantly stronger effect than Germany and the United States. Incorporating national capital cost assumptions proves critical, reducing Germanys Renewable Pull by a factor of six and positioning it as the second least affected top manufacturing country after Saudi Arabia. Using Germany as a case study, the analysis moreover illustrates that targeted import strategies, especially within the EU, can nearly eliminate the Renewable Pull, offering policymakers clear options for risk mitigation.

eess.SY

Certifying Frequency Stability for Systems with Line Dynamics and Heterogeneous Bus Dynamics

This work presents a framework for certifying small-signal frequency stability of a power system with line dynamics and heterogeneous bus dynamics. This framework can certify the stability of systems which include synchronous generators, synchronous condensers, and converter-interfaced resources with a wide range of controls. Moreover, it can do so without detailed or precise knowledge of the network topology. With this framework, we also provide a detailed analysis of how proportional-derivative (PD) droop can improve the stability margin of the frequency response. The stability certificates presented in this work, which extend prior results by incorporating line dynamics, provide insight into how the control parameters for different units in the system impact the overall frequency stability. While damper windings have long been understood to improve the frequency synchronization between machines, the dynamics of the damper windings are complex, making them difficult to analyze. To address this gap, this paper derives a novel reduced-order model of the damper windings in the form of a derivative droop term. Moreover, we show that derivative droop terms used in grid-forming (GFM) control can be understood as a form of damper winding emulation. Our analytical stability conditions highlight the importance of damper windings (or their emulation) in facilitating frequency synchronization and suppressing unstable interactions between GFM converters. These results are validated with electromagnetic-transient (EMT) simulation.

eess.SY