The explosive growth of embedded electronics is bringing information and control systems of increasing complexity to every aspects of our lives. The most challenging designs are safety-critical systems, such as transportation systems (e.g., airplanes, cars, and trains), industrial plants and health care monitoring. The difficulties reside in accommodating constraints both on functionality and implementation. The correct behavior must be guaranteed under diverse states of the environment and potential failures; implementation has to meet cost, size, and power consumption requirements. The design is therefore subject to extensive mathematical analysis and simulation. However, traditional models of information systems do not interface well to the continuous evolving nature of the environment in which these devices operate. Thus, in practice, different mathematical representations have to be mixed to analyze the overall behavior of the system. Hybrid systems are a particular class of mixed models that focus on the combination of discrete and continuous subsystems. There is a wealth of tools and languages that have been proposed over the years to handle hybrid systems. However, each tool makes different assumptions on the environment, resulting in somewhat different notions of hybrid system. This makes it difficult to share information among tools. Thus, the community cannot maximally leverage the substantial amount of work that has been directed to this important topic. In this paper, we review and compare hybrid system tools by highlighting their differences in terms of their underlying semantics, expressive power and mathematical mechanisms. We conclude our review with a comparative summary, which suggests the need for a unifying approach to hybrid systems design. As a step in this direction, we make the case for a semantic-aware interchange format, which would enable the use of joint techniques, make a formal comparison between different approaches possible, and facilitate exporting and importing design representations.

A hierarchical control architecture for balancing comfort and energy consumption in buildings is presented. The control design is based on a simplified, yet accurate model of the temperature within each room of the building. The model is validated against real measurements. The control architecture comprises a first level that regulates low level quantities such as air flow, and a second level that balances comfort (i.e. distance between the desired and actual temperature) and energy consumption (i.e. total energy consumed for the required level of comfort). We show the effectiveness of our approach by simulation using validated models.

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In this paper, we propose a hierarchical mission planner where the state of the world and of the mission are abstracted into corresponding states of a Markov Decision Process (MDP). Transitions in the MDP represent abstract motion actions that are planned by a lower level probabilistic planner. The cost structure of the MDP is multi-dimensional: each state-action pair is annotated with a vector of metrics such as time and resource requirements. A mission specification is divided into three parts: a temporal logic formula defined over state propositions, the choice of the primary cost, and constraints on the remaining secondary costs. The planning problem is formulated as finding the optimal policy of a Constrained Markov Decision Process with above mission specification. The resulting planning system is tested in a mission where an agent is tasked with a complex mission in a urban hostile environment.

Abstract-We present a methodology and a software framework for the automatic design exploration of the communication network among sensors, actuators and controllers in building automation systems. Given 1) a set of end-toend latency, throughput and packet error rate constraints between nodes, 2) the building geometry, and 3) a library of communication components together with their performance and cost characterization, a synthesis algorithm produces a network implementation that satisfies all end-to-end constraints and that is optimal with respect to installation and maintenance cost. The methodology is applied to the synthesis of wireless networks for an essential step in any control algorithm in a distributed environment: the estimation of control variables such as temperature and air-flow in buildings.