Abstract. We present a scalable reachability algorithm for hybrid systems with piecewise affine, non-deterministic dynamics. It combines polyhedra and support function representations of continuous sets to compute an over-approximation of the reachable states. The algorithm improves over previous work by using variable time steps to guarantee a given local error bound. In addition, we propose an improved approximation model, which drastically improves the accuracy of the algorithm. The algorithm is implemented as part of SpaceEx, a new verification platform for hybrid systems, available at spaceex.imag.fr. Experimental results of full fixed-point computations with hybrid systems with more than 100 variables illustrate the scalability of the approach.

Abstract. In 1995, HyTech broke new ground as a potentially powerful tool for verifying hybrid systems -yet it has remained severely limited in its applicability to more complex systems. We address the main problems of HyTech with PHAVer, a new tool for the exact verification of safety properties of hybrid systems with piecewise constant bounds on the derivatives. Affine dynamics are handled by on-the-fly overapproximation and by partitioning the state space based on user-definable constraints and the dynamics of the system. PHAVer's exact arithmetic is robust due to the use of the Parma Polyhedra Library, which supports arbitrarily large numbers. To manage the complexity of the polyhedral computations, we propose methods to conservatively limit the number of bits and constraints of polyhedra. Experimental results for a navigation benchmark and a tunnel diode circuit show the effectiveness of the approach.

Abstract. In 1995, HyTech broke new ground as a potentially powerful tool for verifying hybrid systems -yet it has remained severely limited in its applicability to more complex systems. We address the main problems of HyTech with PHAVer, a new tool for the exact verification of safety properties of hybrid systems with piecewise constant bounds on the derivatives. Affine dynamics are handled by on-the-fly overapproximation and by partitioning the state space based on user-definable constraints and the dynamics of the system. PHAVer's exact arithmetic is robust due to the use of the Parma Polyhedra Library, which supports arbitrarily large numbers. To manage the complexity of the polyhedral computations, we propose methods to conservatively limit the number of bits and constraints of polyhedra. Experimental results for a navigation benchmark and a tunnel diode circuit show the effectiveness of the approach.

Reachability analysis consists in computing the set of states that are reachable by a dynamical system from all initial states and for all admissible inputs and parameters. It is a fundamental problem motivated by many applications in formal verification, controller synthesis, and estimation, to name only a few. This article focuses on a class of methods for computing a guaranteed overapproximation of the reachable set of continuous and hybrid systems, relying predominantly on set propagation; starting from the set of initial states, these techniques iteratively propagate a sequence of sets according to the system dynamics. After a review of set representation and computation, the article presents the state of the art of set propagation techniques for reachability analysis of linear, nonlinear, and hybrid systems. It ends with a discussion of successful applications of reachability analysis to real-world problems. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4 is May 3, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

International audienceSet-based reachability analysis computes all possible states a system may attain, and in this sense provides knowledge about the system with a completeness, or coverage, that a finite number of simulation runs can not deliver. Due to its inherent complexity, the application of reachability analysis has been limited so far to simple systems, both in the continuous and the hybrid domain. In this paper we present recent advances that, in combination, significantly improve this applicability, and allow us to find better balance between computational cost and accuracy. The presentation covers, in a unified manner, a variety of methods handling increasingly complex types of continuous dynamics (constant derivative, linear, nonlinear). The improvements include new geometrical objects for representing sets, new approximation schemes, and more flexible combinations of graph-search algorithm and partition refinement. We report briefly some preliminary experiments that have enabled the analysis of systems previously beyond reach

In this paper, we present an approximation of the set of reachable states, called flowpipe, for a continuous system with affine dynamics. Our approach is based on a representation we call flowpipe sampling, which consists of a set of continuous, interval-valued functions over time. A flowpipe sampling attributes to each time point a polyhedral enclosure of the set of states reachable at that time point, and is capable of representing a nonconvex enclosure of a nonconvex flowpipe. The use of flowpipe samplings allows us to represent and approximate the nonconvex flowpipe efficiently. In particular, we can measure the error incurred by the initial approximation and by further processing such as simplification and convexification. A flowpipe sampling can be efficiently translated into a set of convex polyhedra in a way that minimizes the number of convex sets for a given error bound. When applying flowpipe approximation for the reachability of hybrid systems, a reduction in the number of convex sets spawned by each image computation can lead to drastic performance improvements.

Abstract. Our goal is to find the set of parameters for which a given linear hybrid automaton does not reach a given set of bad states. The problem is known to be semi-solvable (if the algorithm terminates the result is correct) by introducing the parameters as state variables and computing the set of reachable states. This is usually too expensive, however, and in our experiments only possible for very simple systems with few parameters. We propose an adaptation of counterexample-guided abstraction refinement (CEGAR) with which one can obtain an underapproximation of the set of good parameters using linear programming. The adaptation is generic and can be applied on top of any CEGAR method where the counterexamples correspond to paths in the concrete system. For each counterexample, the cost incurred by underapproximating the parameters is polynomial in the number of variables, parameters, and the length of counterexample. We identify a syntactic condition for which the approach is complete in the sense that the underapproximation is empty only if the problem has no solution. Experimental results are provided for two CEGAR methods, a simple discrete version and iterative relaxation abstraction (IRA), both of which show a drastic improvement in performance compared to standard reachability.

Abstract-Simulation relations can be used to verify refinement between a system and its specification, or between models of different complexity. It is known that for the verification of safety properties, simulation between hybrid systems can be defined based on their labeled transition system semantics. We show that for hybrid systems without shared variables, which therefore only interact at discrete events, this simulation preorder is compositional, and present assume-guarantee rules that help to counter the state explosion problem. Some experimental results for simulation checking of linear hybrid automata are provided using a prototype tool with exact arithmetic and unlimited digits.