Model Predictive Control (MPC) is a model-based technique widely and successfully used over the past years to improve control systems performance. A key factor prohibiting the widespread adoption of MPC for complex systems such as buildings is related to the difficulties (cost, time and effort) associated with the identification of a predictive model of a building. To overcome this problem, we introduce a novel idea for predictive control based on historical building data leveraging machine learning algorithms like regression trees and random forests. We call this approach Data-driven model Predictive Control (DPC), and we apply it to three different case studies to demonstrate its performance, scalability, and robustness. In the first case study we consider a benchmark MPC controller using a bilinear building model, then we apply DPC to a data-set simulated from such bilinear model and derive a controller based only on the data. Our results demonstrate that DPC can provide comparable performance with respect to MPC applied to a perfectly known mathematical model. In the second case study, we apply DPC to a 6 story 22 zone building model in EnergyPlus, for which model-based control is not economical and practical due to extreme complexity, and address a Demand Response problem. Our results demonstrate scalability and efficiency of DPC showing that DPC provides the desired power curtailment with an average error of 3%. In the third case study, we implement and test DPC on real data from an off-grid house located in L' Aquila, Italy. We compare the total amount of energy saved with respect to the classical bang-bang controller, showing that we can perform an energy saving up to 49.2%. Our results demonstrate the robustness of our method to uncertainties both in real data acquisition and weather forecast.

Abstract-We present a method to stabilize a plant with a network of resource constrained wireless nodes. As opposed to traditional networked control schemes where the nodes simply route information to and from a dedicated controller (perhaps performing some encoding along the way), our approach treats the network itself as the controller. Specifically, we formulate a strategy for each node in the network to follow, where at each time-step, each node updates its internal state to be a linear combination of the states of the nodes in its neighborhood. We show that this causes the entire network to behave as a linear dynamical system, with sparsity constraints imposed by the network topology. We provide a numerical design procedure to determine appropriate linear combinations to be applied by each node so that the transmissions of the nodes closest to the actuators will stabilize the plant. We also show how our design procedure can be modified to maintain mean square stability under packet drops in the network, and present a distributed scheme that can handle node failures while preserving stability. We call this architecture a Wireless Control Network, and show that it introduces very low computational and communication overhead to the nodes in the network, allows the use of simple transmission scheduling algorithms, and enables compositional design (where the existing wireless control infrastructure can be easily extended to handle new plants that are brought online in the vicinity of the network).

Modern control systems, like controllers for swarms of quadrotors, must satisfy complex control objectives while withstanding a wide range of disturbances, from bugs in their software to attacks on their sensors and changes in their environments. These requirements go beyond stability and tracking, and involve temporal and sequencing constraints on system response to various events. This work formalizes the requirements as formulas in Metric Temporal Logic (MTL), and designs a controller that maximizes the robustness of the MTL formula. Formally, if the system satisfies the formula with robustness r, then any disturbance of size less than r cannot cause it to violate the formula. Because robustness is not differentiable, this work provides arbitrarily precise, infinitely differentiable, approximations of it, thus enabling the use of powerful gradient descent optimizers. Experiments on a temperature control example and a two-quadrotor system demonstrate that this approach to controller design outper-forms existing approaches to robustness maximization based on Mixed Integer Linear Programming and stochastic heuristics. Moreover, it is not constrained to linear systems. Abstract-Modern control systems, like controllers for swarms of quadrotors, must satisfy complex control objectives while withstanding a wide range of disturbances, from bugs in their software to attacks on their sensors and changes in their environments. These requirements go beyond stability and tracking, and involve temporal and sequencing constraints on system response to various events. This work formalizes the requirements as formulas in Metric Temporal Logic (MTL), and designs a controller that maximizes the robustness of the MTL formula. Formally, if the system satisfies the formula with robustness r, then any disturbance of size less than r cannot cause it to violate the formula. Because robustness is not differentiable, this work provides arbitrarily precise, infinitely differentiable, approximations of it, thus enabling the use of powerful gradient descent optimizers. Experiments on a temperature control example and a two-quadrotor system demonstrate that this approach to controller design outperforms existing approaches to robustness maximization based on Mixed Integer Linear Programming and stochastic heuristics. Moreover, it is not constrained to linear systems. Keywords

Abstract. The design and implementation of software for medical devices is challenging due to their rapidly increasing functionality and the tight coupling of computation, control, and communication. The safetycritical nature and the lack of existing industry standards for verification, make this an ideal domain for exploring applications of formal modeling and analysis. In this study, we use a dual chamber implantable pacemaker as a case study for modeling and verification of control algorithms for medical devices in UPPAAL. We begin with detailed models of the pacemaker, based on the specifications and algorithm descriptions from Boston Scientific. We then define the state space of the closed-loop system based on its heart rate and developed a heart model which can nondeterministically cover the whole state space. For verification, we first specify unsafe regions within the state space and verify the closed-loop system against corresponding safety requirements. As stronger assertions are attempted, the closed-loop unsafe state may result from healthy openloop heart conditions. Such unsafe transitions are investigated with two clinical cases of Pacemaker Mediated Tachycardia and their corresponding correction algorithms in the pacemaker. Along with emerging tools for code generation from UPPAAL models, this effort enables modeldriven design and certification of software for medical devices.

Wireless sensor networks have traditionally focused on low duty-cycle applications where sensor data are reported periodically in the order of seconds or even longer. This is due to typically slow changes in physical variables, the need to keep node costs low and the goal of extending battery lifetime. However, there is a growing need to support real-time streaming of audio and/or low-rate video even in wireless sensor networks for use in emergency situations and shortterm intruder detection. In this paper, we describe a real-time voice stream-capability in wireless sensor networks and summarize our deployment experiences of voice streaming across a large sensor network of FireFly nodes in an operational coal mine. FireFly is composed of several integrated layers including specialized low-cost hardware, a sensor network operating system, a real-time link layer and network scheduling. We are able to provide efficient support for applications with timing constraints by tightly coupling the network and task scheduling with hardware-based global time synchronization. We use this platform to support 2-way audio streaming concurrently with sensing tasks. For interactive voice, we investigate TDMA-based slot scheduling with balanced bi-directional latency while meeting audio timeliness requirements. Finally, we describe our experimental deployment of 42 nodes in a coal mine, and present measurements of the end-to-end throughput, jitter, packet loss and voice quality. ©2006 IEEE Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Keywords Scheduling Algorithms for Embedded Wireless Networks AbstractWireless sensor networks have traditionally focused on low duty-cycle applications where sensor data are reported periodically in the order of seconds or even longer. This is due to typically slow changes in physical variables, the need to keep node costs low and the goal of extending battery lifetime. However, there is a growing need to support real-time streaming of audio and/or low-rate video even in wireless sensor networks for use in emergency situations and shortterm intruder detection. In this paper, we describe a real-time voice stream-capability in wireless sensor networks and summarize our deployment experiences of voice streaming across a large sensor network of FireFly nodes in an operational coal mine. FireFly is composed of several integrated layers including specialized low-cost hardware, a sensor network operating system, a real-time link layer and network scheduling. We are able to provide efficient support for applications with timing constraints by tightly coupling the network and task scheduling with hardware-based global time synchronization. We use this platform to support 2-way audio streaming concurrently with sensing tasks. F...

The problem of safe planning and control for multi-drone systems across a variety of missions is of critical impor-tance, as the scope of tasks assigned to such systems increases. In this paper, we present an approach to solve this problem for multi-quadrotor missions. Given a mission expressed in Signal Temporal Logic (STL), our controller maximizes robustness to generate trajectories for the quadrotors that satisfy the STL specification in continuous-time. We also show that the constraints on our optimization guarantees that these trajectories can be tracked nearly perfectly by lower level off-the-shelf position and attitude controllers. Our approach avoids the oversimplifying abstractions found in many planning methods, while retaining the expressiveness of missions encoded in STL allowing us to handle complex spatial, temporal and reactive requirements. Through experiments, both in simulation and on actual quadrotors, we show the performance, scalability and real-time applicability of our method. KeywordsMulti-drone fleets, control, signal temporal logic, robustness maximization, quadrotors, crazyflie Disciplines Computer Engineering | Electrical and Computer EngineeringThis conference paper is available at ScholarlyCommons: https://repository.upenn.edu/mlab_papers/107 Abstract-The problem of safe planning and control for multidrone systems across a variety of missions is of critical importance, as the scope of tasks assigned to such systems increases. In this paper, we present an approach to solve this problem for multi-quadrotor missions. Given a mission expressed in Signal Temporal Logic (STL), our controller maximizes robustness to generate trajectories for the quadrotors that satisfy the STL specification in continuous-time. We also show that the constraints on our optimization guarantees that these trajectories can be tracked nearly perfectly by lower level off-the-shelf position and attitude controllers. Our approach avoids the oversimplifying abstractions found in many planning methods, while retaining the expressiveness of missions encoded in STL allowing us to handle complex spatial, temporal and reactive requirements. Through experiments, both in simulation and on actual quadrotors, we show the performance, scalability and real-time applicability of our method.

Building systems such as heating, air quality control and refrigeration operate independently of each other and frequently result in temporally correlated energy demand surges. As peak power prices are 200-400 times that of the nominal rate, this uncoordinated activity is both expensive and operationally inefficient. We present an approach to fine-grained coordination of energy demand by scheduling the control systems within a constrained peak while ensuring custom climate environments are facilitated. The peak constraint is minimized for energy efficiency, while we provide feasibility conditions for the constraint to be realizable by a scheduling policy for the control systems. The physical systems are then coordinated by the scheduling controller so as both the peak constraint and the climate/safety constraint are satisfied. We also introduce a simple scheduling approach called lazy scheduling. The proposed control and scheduling strategy is implemented in simulation examples from small to large scales, which show that it can achieve significant peak demand reduction while being efficient and scalable.

We propose RT-Link, a