Showing papers by "Alberto Sangiovanni-Vincentelli published in 2016"

Diagnosis and Repair for Synthesis from Signal Temporal Logic Specifications

Abstract: We address the problem of diagnosing and repairing specifications for hybrid systems, formalized in signal temporal logic (STL). Our focus is on automatic synthesis of controllers from specifications using model predictive control. We build on recent approaches that reduce the controller synthesis problem to solving one or more mixed integer linear programs (MILPs), where infeasibility of an MILP usually indicates unrealizability of the controller synthesis problem. Given an infeasible STL synthesis problem, we present algorithms that provide feedback on the reasons for unrealizability, and suggestions for making it realizable. Our algorithms are sound and complete relative to the synthesis algorithm, i.e., they provide a diagnosis that makes the synthesis problem infeasible, and always terminate with a non-trivial specification that is feasible using the chosen synthesis method, when such a solution exists. We demonstrate the effectiveness of our approach on controller synthesis for various cyber-physical systems, including an autonomous driving application and an aircraft electric power system.

Scalable lazy SMT-based motion planning

Abstract: We present a scalable robot motion planning algorithm for reach-avoid problems. We assume a discrete-time, linear model of the robot dynamics and a workspace described by a set of obstacles and a target region, where both the obstacles and the region are polyhedra. Our goal is to construct a trajectory, and the associated control strategy, that steers the robot from its initial point to the target while avoiding obstacles. Differently from previous approaches, based on the discretization of the continuous state space or uniform discretization of the workspace, our approach, inspired by the lazy satisfiability modulo theory paradigm, decomposes the planning problem into smaller subproblems, which can be efficiently solved using specialized solvers. At each iteration, we use a coarse, obstacle-based discretization of the workspace to obtain candidate high-level, discrete plans that solve a set of Boolean constraints, while completely abstracting the low-level continuous dynamics. The feasibility of the proposed plans is then checked via a convex program, under constraints on both the system dynamics and the control inputs, and new candidate plans are generated until a feasible one is found. To achieve scalability, we show how to generate succinct explanations for the infeasibility of a discrete plan by exploiting a relaxation of the convex program that allows detecting the earliest possible occurrence of an infeasible transition between workspace regions. Simulation results show that our algorithm favorably compares with state-of-the-art techniques and scales well for complex systems, including robot dynamics with up to 50 continuous states.

Smart Connected Buildings Design Automation: Foundations and Trends

Abstract: Buildings are the result of a complex integration of multi-physics subsystems. Besides the obvious civil engineering infrastructure, thermal, electrical, mechanical, control, communication and computing subsystems must co-exist and be operated so that the overall operation is smooth and efficient. This is particularly important for commercial buildings but is also very relevant for residential buildings especially apartment buildings. Unfortunately, the design and deployment of these subsystems is rarely synchronized: lighting, security, heating, ventilation and air conditioning systems are often designed independently. However, simply putting together a collection of sub-systems, albeit optimized, has led to the inefficient buildings of today. Worldwide, buildings consume 42% of all electrical power - more than any other asset - and it can be proven that much of this can be reduced if a holistic approach to design, deployment, and operation is taken. Government agencies, academic institutions, building contractors and owners have realized the significant impact of buildings on the global environment, the electrical grid, and the mission of their organizations. However, the economic impact for all constituencies is still difficult to assess. Government regulations can play a fundamental role, as it has been the case for the transportation industry where regulations on emission and fuel consumption have been the single most important factor of innovation in automotive design. We are convinced that by leveraging technology and utilizing a system-level approach to buildings, they will provide comfort, safety and functionality while minimizing energy cost, supporting a robust electric grid and mitigating environmental impact. Realizing this vision requires adding intelligence from the beginning of the design phase, to deployment, from commissioning to operation, all the way to the end of the building's life cycle. In this issue, we attempt to provide an as-complete-as-possible overview of the activities in the field of smart connected building design automation that attempts to make the vision a reality. The overarching range of such activities includes developing simulation tools for modeling and the design of buildings, and consequently control algorithms proposed to make buildings smarter and more efficient. Furthermore, we will review real-world and large-scale implementation of such control strategies on physical buildings. We then present a formal co-design methodology to design buildings, taking the view that buildings are prime examples of cyber-physical systems where the virtual and physical worlds meet as more traditional products such as thermostats are able to connect online and perform complicated computational tasks to control building temperature effectively. We complete the presentation describing the growing role of buildings in the operation of the smart grid where buildings are not only consumers of energy, but are themselves also providers of services and energy to the grid. The audiences for this monograph are industry professionals and researchers who work in the area of smart buildings, smart cities, and smart grid, with emphasis on energy efficiency, simulation tools, optimal control, and cyber-physical systems for the emerging power markets.

SMT-based observer design for cyber-physical systems under sensor attacks

Abstract: We introduce a scalable observer architecture to estimate the states of a discrete-time linear-time-invariant (LTI) system whose sensors can be manipulated by an attacker. Given the maximum number of attacked sensors, we build on previous results on necessary and sufficient conditions for state estimation, and propose a novel multi-modal Luenberger (MML) observer based on efficient Satisfiability Modulo Theory (SMT) solving. We present two techniques to reduce the complexity of the estimation problem. As a first strategy, instead of a bank of distinct observers, we use a family of filters sharing a single dynamical equation for the states, but different output equations, to generate estimates corresponding to different subsets of sensors. Such an architecture can reduce the memory usage of the observer from an exponential to a linear function of the number of sensors. We then develop an efficient SMT-based decision procedure that is able to reason about the estimates of the MML observer to detect at runtime which sets of sensors are attack-free, and use them to obtain a correct state estimate. We provide proofs of convergence for our algorithm and report simulation results to compare its runtime performance with alternative techniques. Our algorithm scales well for large systems (including up to 5000 sensors) for which many previously proposed algorithms are not implementable due to excessive memory and time requirements. Finally, we illustrate the effectiveness of our algorithm on the design of resilient power distribution systems.

The ultimate IoT application: A cyber-physical system for ambient assisted living

Abstract: We propose a novel approach that integrates wireless, non-invasive devices with fast, real-time algorithms for large data analysis and biofeedback reaction, to discern the voluntariness of human movement through direct sensing of brain potentials combined with muscular action signal monitoring. The system has been tested in real situations.

Constrained Synthesis from Component Libraries

Abstract: Synthesis from component libraries is the problem of building a network of components from a given library, such that the network realizes a given specification. This problem is undecidable in general. It becomes decidable if we impose a bound on the number of chosen components. However, the bounded problem remains computationally hard and brute-force approaches do not scale. In this paper we study scalable methods for solving the problem of bounded synthesis from libraries, proposing a solution based on the CounterExample-Guided Inductive Synthesis paradigm. Although our synthesis algorithm does not assume a specific formalism a priori, we present a parallel implementation which instantiates components defined as Linear Temporal Logic-based Assume/Guarantee Contracts. We show the potential of our approach and evaluate our implementation by applying it to an industrial case study.

Diagnosis and Repair for Synthesis from Signal Temporal Logic Specifications

Abstract: We address the problem of diagnosing and repairing specifications for hybrid systems formalized in signal temporal logic (STL). Our focus is on the setting of automatic synthesis of controllers in a model predictive control (MPC) framework. We build on recent approaches that reduce the controller synthesis problem to solving one or more mixed integer linear programs (MILPs), where infeasibility of a MILP usually indicates unrealizability of the controller synthesis problem. Given an infeasible STL synthesis problem, we present algorithms that provide feedback on the reasons for unrealizability, and suggestions for making it realizable. Our algorithms are sound and complete, i.e., they provide a correct diagnosis, and always terminate with a non-trivial specification that is feasible using the chosen synthesis method, when such a solution exists. We demonstrate the effectiveness of our approach on the synthesis of controllers for various cyber-physical systems, including an autonomous driving application and an aircraft electric power system.