We propose a methodology for the automatic verification of safety properties of controllers based on dynamical systems, such as those typically used in avionics. In particular, our focus is on proving stability properties of software implementing linear and some non-linear controllers. We develop an abstract interpretation framework that follows closely the Lyapunov methods used in proofs at the model level and describe the corresponding abstract domains, which for linear systems consist of ellipsoidal constraints. These ellipsoidal domains provide abstractions for the values of state variables and must be combined with other domains that model the remaining variables in a program. Thus, the problem of automatically assigning the right type of abstract domain to each variable arises. We provide an algorithm that solves this classification problem in many practical cases and suggest how it could be generalized to more complicated cases. We then find a fixpoint by solving a matrix equation, which in the linear case is just the discrete Lyapunov equation. Contrary to most cases in software analysis, this fixpoint cannot be reached by the usual iterative method of propagating constraints until saturation and so numerical methods become essential. Finally, we illustrate our methodology with several examples.
Beyond the theoretical level, several papers have focused on the analysis at the model level @cite_6 @cite_2 . Among them, the approach most similar to ours was proposed by @cite_15 , who used ellipsoidal calculus to compute overapproximations to the reachability set of controller models. They gave algorithms to estimate unions and intersections of ellipsoids and implemented a tool called VeriSHIFT to automate the reachability computation of models designed with it. Their algorithm has been later improved by @cite_1 .
@cite_15 @cite_1 @cite_6 @cite_2
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A new approach is presented for computing approximations of the reached sets of linear hybrid automata. First, we present some new theoretical results on termination of a class of reachability algorithms, which includes Botchkarev's, based on ellipsoidal calculus. The main contribution of the paper is a revised reachability computation that avoids the approximations caused by the union operation in the discretized flow tube estimation. Therefore, the new algorithm may classify as unreachable states that are reachable according to the previous algorithm because of the looser over-approximations introduced by the union operation. We implemented the new reachability algorithm and tested it successfully on a real-life case modeling a hybrid model of a controlled car engine. This work is concerned with the algorithmic reachability analysis of continuous-time linear systems with constrained initial states and inputs. We propose an approach for computing an over-approximation of the set of states reachable on a bounded time interval. The main contribution over previous works is that it allows us to consider systems whose sets of initial states and inputs are given by arbitrary compact convex sets represented by their support functions. We actually compute two over-approximations of the reachable set. The first one is given by the union of convex sets with computable support functions. As the representation of convex sets by their support function is not suitable for some tasks, we derive from this first over-approximation a second one given by the union of polyhedrons. The overall computational complexity of our approach is comparable to the complexity of the most competitive available specialized algorithms for reachability analysis of linear systems using zonotopes or ellipsoids. The effectiveness of our approach is demonstrated on several examples. In earlier work, we showed that the set of states which can reach a target set of a continuous dynamic game is the zero sublevel set of the viscosity solution of a time dependent Hamilton-Jacobi-Isaacs (HJI) partial differential equation (PDE). We have developed a numerical tool—based on the level set methods of Osher and Sethian—for computing these sets, and we can accurately calculate them for a range of continuous and hybrid systems in which control inputs are pitted against disturbance inputs. The cost of our algorithm, like that of all convergent numerical schemes, increases exponentially with the dimension of the state space. In this paper, we devise and implement a method that projects the true reachable set of a high dimensional system into a collection of lower dimensional subspaces where computation is less expensive. We formulate a method to evolve the lower dimensional reachable sets such that they are each an overapproximation of the full reachable set, and thus their intersection will also be an overapproximation of the reachable set. The method uses a lower dimensional HJI PDE for each projection with a set of disturbance inputs augmented with the unmodeled dimensions of that projection's subspace. We illustrate our method on two examples in three dimensions using two dimensional projections, and we discuss issues related to the selection of appropriate projection subspaces. A general verification algorithm is described. It is then shown how ellipsoidal methods developed by A. B. Kurzhanski and P. Varaiya can be adapted to the algorithm. New numerical algorithms that compute approximations of unions of ellipsoids and intersections of ellipsoids and polyhedra were developed. The presented techniques were implemented in the verification tool called VeriSHIFT and some practical results are discussed.