Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control最新文献

{"title":"Robust output feedback control with guaranteed constraint satisfaction","authors":"Sadra Sadraddini, Russ Tedrake","doi":"10.1145/3365365.3382211","DOIUrl":"https://doi.org/10.1145/3365365.3382211","url":null,"abstract":"We propose a method to control linear time-varying (LTV) discrete-time systems subject to bounded process disturbances and measurable outputs with bounded noise, and polyhedral constraints over system inputs and states. We search over control policies that map the history of measurable outputs to the current control input. We solve the problem in two stages. First, using the original system, we build a linear system that predicts future observations using the past observations. The bounded errors are characterized using zonotopes. Next, we propose control laws based on affine maps of such output prediction errors, and show that controllers can be synthesized using convex linear/quadratic programs. Furthermore, we can add constraints on trajectories and guarantee their satisfaction for all allowable sequences of observation noise and process disturbances. Our method does not require any assumptions about system controllability and observability. The controller design does not directly take into account the state-space dynamics, and its implementation does not require an observer. Instead, partial observability is often sufficient to design a correct controller. We provide the polytopic representation of observability errors and reachable sets in the form of zonotopes. Illustrative examples are included.","PeriodicalId":162317,"journal":{"name":"Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115452872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthesizing barrier certificates using neural networks","authors":"Hengjun Zhao, Xia Zeng, Taolue Chen, Zhiming Liu","doi":"10.1145/3365365.3382222","DOIUrl":"https://doi.org/10.1145/3365365.3382222","url":null,"abstract":"This paper presents an approach of safety verification based on neural networks for continuous dynamical systems which are modeled as a system of ordinary differential equations. We adopt the deductive verification methods based on barrier certificates. These are functions over the states of the dynamical system with certain constraints the existence of which entails the safety of the system under consideration. We propose to represent the barrier function by neural networks and provide a comprehensive synthesis framework. In particular, we devise a new type of activation functions, i.e., Bent-ReLU, for the neural networks; we provide sampling based approaches to generate training sets and formulate the loss functions for neural network training which can capture the essence of barrier certificate; we also present practical methods to check a learnt candidate barrier certificate against the criteria of barrier certificates as a formal guarantee. We implement our approaches via proof-of-concept experiments with encouraging results.","PeriodicalId":162317,"journal":{"name":"Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116708206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conformance verification for neural network models of glucose-insulin dynamics","authors":"Taisa Kushner, S. Sankaranarayanan, M. Breton","doi":"10.1145/3365365.3382210","DOIUrl":"https://doi.org/10.1145/3365365.3382210","url":null,"abstract":"Neural networks present a useful framework for learning complex dynamics, and are increasingly being considered as components to closed loop predictive control algorithms. However, if they are to be utilized in such safety-critical advisory settings, they must be provably \"conformant\" to the governing scientific (biological, chemical, physical) laws which underlie the modeled process. Unfortunately, this is not easily guaranteed as neural network models are prone to learn patterns which are artifacts of the conditions under which the training data is collected, which may not necessarily conform to underlying physiological laws. In this work, we utilize a formal range-propagation based approach for checking whether neural network models for predicting future blood glucose levels of individuals with type-1 diabetes are monotonic in terms of their insulin inputs. These networks are increasingly part of closed loop predictive control algorithms for \"artificial pancreas\" devices which automate control of insulin delivery for individuals with type-1 diabetes. Our approach considers a key property that blood glucose levels must be monotonically decreasing with increasing insulin inputs to the model. Multiple representative neural network models for blood glucose prediction are trained and tested on real patient data, and conformance is tested through our verification approach. We observe that standard approaches to training networks result in models which violate the core relationship between insulin inputs and glucose levels, despite having high prediction accuracy. We propose an approach that can learn conformant models without much loss in accuracy.","PeriodicalId":162317,"journal":{"name":"Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132732576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inter-triggering hybrid automata: a formalism for responsibility-sensitive safety","authors":"N. Ozay","doi":"10.1145/3365365.3383470","DOIUrl":"https://doi.org/10.1145/3365365.3383470","url":null,"abstract":"This paper introduces inter-triggering hybrid automata, a formalism to represent multi-agent systems where each agent is represented as a hybrid automaton and agents interact by triggering discrete transitions (jumps and resets) on their \"neighboring\" agents. Using this formalism, we define responsibility-sensitive safety as respecting one another's invariances while triggering jumps and resets. This allows us to make a formal connection between responsibility and robust controlled invariant sets for individual agents, therefore leading to a compositional verification framework for the safety of the overall multi-agent system. We discuss several advantages of this viewpoint and illustrate it on a highway driving example.","PeriodicalId":162317,"journal":{"name":"Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129911253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A simple hierarchy for computing controlled invariant sets","authors":"Tzanis Anevlavis, P. Tabuada","doi":"10.1145/3365365.3382205","DOIUrl":"https://doi.org/10.1145/3365365.3382205","url":null,"abstract":"In this paper we revisit the problem of computing controlled invariant sets for controllable discrete-time linear systems and present a novel hierarchy for their computation. The key insight is to lift the problem to a higher dimensional space where the maximal controlled invariant set can be computed exactly and in closed-form for the lifted system. By projecting this set into the original space we obtain a controlled invariant set that is a subset of the maximal controlled invariant set for the original system. Building upon this insight we describe in this paper a hierarchy of spaces where the original problem can be lifted into so as to obtain a sequence of increasing controlled invariant sets. The algorithm that results from the proposed hierarchy does not rely on iterative computations. We illustrate the performance of the proposed method on a variety of scenarios exemplifying its appeal.","PeriodicalId":162317,"journal":{"name":"Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128586603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A computable and compositional semantics for hybrid automata","authors":"Davide Bresolin, P. Collins, Luca Geretti, R. Segala, T. Villa, Sanja Živanović Gonzalez","doi":"10.1145/3365365.3382202","DOIUrl":"https://doi.org/10.1145/3365365.3382202","url":null,"abstract":"Hybrid Systems are systems having a mixed discrete and continuous behaviour that cannot be characterized faithfully using either only discrete or only continuous models. A good framework for hybrid systems should support their compositional description and analysis, since commonly systems are specified by a composition of smaller subsystems, to cope with the complexity of their monolithic representation. Moreover, since the reachability problem for hybrid systems is undecidable, one should investigate the conditions that guarantee approximate computability of composition, when only approximations to the exact problem data are available. In this paper, we propose an automata-based formalism (HIOA) for hybrid systems that is compositional and for which the evolution can be computed approximately. The main results are that the composition of compatible HIOA yields a pre-HIOA; a dominance result on the composition of HIOA by which we can replace any component in a composition by another one that exhibits the same external behaviour without affecting the behaviour of the composition; finally, the key result that the composition of two compatible upper(lower)-semicontinuous HIOA is a computable upper(lower)-semicontinuous pre-HIOA, which entails that the evolution of the composition is upper(lower)-semicomputable. A discussion on how compositionality/computability are handled in state-of-art libraries for reachability analysis closes the paper.","PeriodicalId":162317,"journal":{"name":"Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127301248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AReN","authors":"James Ferlez, Yasser Shoukry","doi":"10.1145/3365365.3382213","DOIUrl":"https://doi.org/10.1145/3365365.3382213","url":null,"abstract":"In this paper, we consider the problem of automatically designing a Rectified Linear Unit (ReLU) Neural Network (NN) architecture that is sufficient to implement the optimal Model Predictive Control (MPC) strategy for an LTI system with quadratic cost. Specifically, we propose AReN, an algorithm to generate Assured ReLU Architectures. AReN takes as input an LTI system with quadratic cost specification, and outputs a ReLU NN architecture with the assurance that there exist network weights that exactly implement the associated MPC controller. AReN thus offers new insight into the design of ReLU NN architectures for the control of LTI systems: instead of training a heuristically chosen NN architecture on data - or iterating over many architectures until a suitable one is found - AReN can suggest an adequate NN architecture before training begins. While several previous works were inspired by the fact that ReLU NN controllers and optimal MPC controllers are both Continuous, Piecewise-Linear (CPWL) functions, exploiting this similarity to design NN architectures with correctness guarantees has remained elusive. AReN achieves this using two novel features. First, we reinterpret a recent result about the implementation of CPWL functions via ReLU NNs to show that a CPWL function may be implemented by a ReLU architecture that is determined by the number of distinct affine regions in the function. Second, we show that we can efficiently over-approximate the number of affine regions in the optimal MPC controller without solving the MPC problem exactly. Together, these results connect the MPC problem to a ReLU NN implementation without explicitly solving the MPC: the result is a NN architecture that has the assurance that it can implement the MPC controller. We show through numerical results the effectiveness of AReN in designing an NN architecture.","PeriodicalId":162317,"journal":{"name":"Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124169760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sufficient conditions for satisfaction of formulas with until operators in hybrid systems","authors":"Hyejin Han, M. Maghenem, R. Sanfelice","doi":"10.1145/3365365.3382223","DOIUrl":"https://doi.org/10.1145/3365365.3382223","url":null,"abstract":"In this paper, we introduce tools to verify the satisfaction of temporal logic specifications using the until operator for hybrid dynamical systems. Hybrid dynamical systems are given in terms of differential and difference inclusions, which capture the continuous and discrete dynamics (or events), respectively. For such systems, conditional invariance and eventual conditional invariance are employed to characterize dynamical properties associated with the until operators. Sufficient conditions for the satisfaction of temporal logic specifications involving the until operator are provided by guaranteeing properties of the data defining the systems and the existence of barrier functions or Lyapunov-like functions. Examples illustrate the results throughout the paper.","PeriodicalId":162317,"journal":{"name":"Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116768024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AMYTISS","authors":"Abolfazl Lavaei, Mahmoud Khaled, S. Soudjani, Majid Zamani","doi":"10.1145/3365365.3383469","DOIUrl":"https://doi.org/10.1145/3365365.3383469","url":null,"abstract":"Large-scale stochastic systems have recently received significant attentions due to their broad applications in various safety-critical systems such as traffic networks and self-driving cars. In this poster, we describe the software tool AMYTISS, implemented in C++/OpenCL, for designing correct-by-construction controllers for large-scale discrete-time stochastic systems. This tool is employed to (i) build finite Markov decision processes (MDPs) as finite abstractions of given original systems, and (ii) synthesize controllers for the constructed finite MDPs satisfying bounded-time safety, reachability, and reach-avoid specifications. In AMYTISS, scalable parallel algorithms are designed such that they support the parallel execution within CPUs, GPUs and hardware accelerators (HWAs). Unlike all existing tools for stochastic systems, AMYTISS can utilize high-performance computing (HPC) platforms and cloud-computing services to mitigate the effects of the state-explosion problem, which is always present in analyzing large-scale stochastic systems. We benchmark AMYTISS against the most recent tools in the literature using several physical case studies including robot examples, room temperature and road traffic networks. We also apply our algorithms to a 3-dimensional autonomous vehicle and a 7-dimensional nonlinear model of a BMW 320i car by synthesizing autonomous parking controllers. Related works. There exist a limited number of software tools on the verification and synthesis of stochastic systems with different classes of models. SReachTools [1] performs the stochastic reachability analysis for linear, potentially time-varying, discrete-time stochastic systems. FAUST2 [2] generates formal abstractions for continuous-space discrete-time stochastic processes, and performs the verification and synthesis for safety and reachability specifications. However, FAUST2 is originally implemented in MATLAB and handles finite-horizon specifications. StocHy [3] deals with a class of discrete-time stochastic hybrid systems, constructs finite abstractions, and performs the verification and synthesis for both finite- and infinite-horizon safety and reachability specifications. AMYTISS differs from FAUST2 and StocHy in two main directions. First, AMYTISS implements novel parallel algorithms and data structures targeting HPC platforms to reduce the undesirable effects of the state-explosion problem. Accordingly, it is able to perform the parallel execution in different heterogeneous computing platforms including CPUs, GPUs and hardware accelerators (HWAs). Whereas, FAUST2 and StocHy can only run serially in one CPU, and consequently, they are limited to small systems. Additionally, AMYTISS can handle the abstraction construction and controller synthesis for two and a halfplayer games (e.g., stochastic systems with bounded disturbances), whereas FAUST2 and StocHy only handle one and a halfplayer games (disturbance-free systems). Unlike all existing tools, AMYTISS of","PeriodicalId":162317,"journal":{"name":"Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115407580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classic and non-prophetic model checking for hybrid Petri nets with stochastic firings","authors":"Carina Pilch, A. Hartmanns, Anne Remke","doi":"10.1145/3365365.3382198","DOIUrl":"https://doi.org/10.1145/3365365.3382198","url":null,"abstract":"Nondeterminism occurs naturally in Petri nets whenever multiple events are enabled at the same time. Traditionally, it is resolved at specification time using probability weights and priorities. In this paper, we focus on model checking for hybrid Petri nets with an arbitrary but finite number of stochastic firings (HPnGs) while preserving the inherent nondeterminism as a first-class modelling and analysis feature. We present two algorithms to compute optimal non-prophetic and prophetic schedulers. The former can be applied to all HPnG models while the latter is only applicable if information on the firing times of general transitions is specifically encoded in the model. Both algorithms make use of recent work on the parametric location tree, which symbolically unfolds the state space of an HPnG. A running example illustrates the approach and confirms the feasibility of the presented algorithm.","PeriodicalId":162317,"journal":{"name":"Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130119747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}