Mixed Monotonicity for Reachability and Safety in Dynamical Systems

A dynamical system is mixed monotone if its vector field or update-map is decomposable into an increasing component and a decreasing component. In this tutorial paper, we study both continuous-time and discrete-time mixed monotonicity and consider systems subject to an input that accommodates, e.g., unknown parameters, an unknown disturbance input, or an exogenous control input. We first define mixed monotonicity with respect to a decomposition function, and we recall sufficient conditions for mixed monotonicity based on sign properties of the state and input Jacobian matrices for the system dynamics. The decomposition function allows for constructing an embedding system that lifts the dynamics to another dynamical system with twice as many states but where the dynamics are monotone with respect to a particular southeast order. This enables applying the powerful theory of monotone systems to the embedding system in order to conclude properties of the original system. In particular, a single trajectory of the embedding system provides hyperrectangular over-approximations of reachable sets for the original dynamics. In this way, mixed monotonicity enables efficient reachable set approximation for applications such as optimization-based control and abstraction-based formal methods in control systems.

[1]  R. Costantino,et al.  Experimentally induced transitions in the dynamic behaviour of insect populations , 1995, Nature.

[2]  Eduardo Sontag Festschrift in Honor of , 2022 .

[3]  Murat Arcak,et al.  Stability of traffic flow networks with a polytree topology , 2016, Autom..

[4]  Antoine Girard,et al.  Reachability of Uncertain Linear Systems Using Zonotopes , 2005, HSCC.

[5]  John N. Maidens,et al.  Reachability Analysis of Nonlinear Systems Using Matrix Measures , 2015, IEEE Transactions on Automatic Control.

[6]  Bai Xue,et al.  Just scratching the surface: Partial exploration of initial values in reach-set computation , 2017, 2017 IEEE 56th Annual Conference on Decision and Control (CDC).

[7]  Adnane Saoud,et al.  Efficient Synthesis for Monotone Transition Systems and Directed Safety Specifications* , 2019, 2019 IEEE 58th Conference on Decision and Control (CDC).

[8]  Matthias Althoff,et al.  Reachability analysis of linear systems with uncertain parameters and inputs , 2007, 2007 46th IEEE Conference on Decision and Control.

[9]  Paulo Tabuada,et al.  Verification and Control of Hybrid Systems - A Symbolic Approach , 2009 .

[10]  Necmiye Ozay,et al.  Tight decomposition functions for mixed monotonicity , 2019, 2019 IEEE 58th Conference on Decision and Control (CDC).

[11]  Calin Belta,et al.  Traffic Network Control From Temporal Logic Specifications , 2014, IEEE Transactions on Control of Network Systems.

[12]  Samuel Coogan,et al.  Specification-Guided Verification and Abstraction Refinement of Mixed-Monotone Stochastic Systems , 2019 .

[13]  James Kapinski,et al.  Locally optimal reach set over-approximation for nonlinear systems , 2016, 2016 International Conference on Embedded Software (EMSOFT).

[14]  Samuel Coogan,et al.  Computing Robustly Forward Invariant Sets for Mixed-Monotone Systems , 2020, 2020 59th IEEE Conference on Decision and Control (CDC).

[15]  Pravin Varaiya,et al.  Computation of Reach Sets for Dynamical Systems , 2010 .

[16]  E. D. Sontagc,et al.  Nonmonotone systems decomposable into monotone systems with negative feedback , 2005 .

[17]  Dimos V. Dimarogonas,et al.  Hierarchical Decomposition of LTL Synthesis Problem for Nonlinear Control Systems , 2019, IEEE Transactions on Automatic Control.

[18]  Luc Jaulin,et al.  Applied Interval Analysis , 2001, Springer London.

[19]  Mahesh Viswanathan,et al.  Automatic Reachability Analysis for Nonlinear Hybrid Models with C2E2 , 2016, CAV.

[20]  Murat Arcak,et al.  TIRA: toolbox for interval reachability analysis , 2019, HSCC.

[21]  Necmiye Ozay,et al.  On Sufficient Conditions for Mixed Monotonicity , 2018, IEEE Transactions on Automatic Control.

[22]  Murat Arcak,et al.  Networks of Dissipative Systems , 2016 .

[23]  David Angeli,et al.  Monotone control systems , 2003, IEEE Trans. Autom. Control..

[24]  Samuel Coogan,et al.  Tight Decomposition Functions for Continuous-Time Mixed-Monotone Systems With Disturbances , 2020, IEEE Control Systems Letters.

[25]  Samuel Coogan,et al.  Enforcing Safety at Runtime for Systems with Disturbances , 2020, 2020 59th IEEE Conference on Decision and Control (CDC).

[26]  H L Smith,et al.  The Discrete Dynamics of Monotonically Decomposable Maps , 2006, Journal of mathematical biology.

[27]  Calin Belta,et al.  Formal Methods for Discrete-Time Dynamical Systems , 2017 .

[28]  Jörg Raisch,et al.  Abstraction based supervisory controller synthesis for high order monotone continuous systems , 2002 .

[29]  Hal L. Smith,et al.  Monotone Dynamical Systems: An Introduction To The Theory Of Competitive And Cooperative Systems (Mathematical Surveys And Monographs) By Hal L. Smith , 1995 .

[30]  Jean-Luc Gouzé,et al.  A criterion of global convergence to equilibrium for differential systems. Application to Lotka-Volterra systems , 1988 .

[31]  Alex A. Kurzhanskiy,et al.  Mixed monotonicity of partial first-in-first-out traffic flow models , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[32]  M. Arcak,et al.  Sampled-Data Reachability Analysis Using Sensitivity and Mixed-Monotonicity , 2018, IEEE Control Systems Letters.

[33]  Antoine Girard,et al.  Safety control with performance guarantees of cooperative systems using compositional abstractions , 2015, ADHS.

[34]  Murat Arcak,et al.  Efficient finite abstraction of mixed monotone systems , 2015, HSCC.

[35]  J. Cushing An introduction to structured population dynamics , 1987 .

[36]  B. Goh Global Stability in Many-Species Systems , 1977, The American Naturalist.

[37]  David Angeli,et al.  A small-gain result for orthant-monotone systems under mixed feedback , 2014, Syst. Control. Lett..

[38]  H.L. Smith,et al.  Global stability for mixed monotone systems , 2008 .

[39]  M. Hirsch Systems of Differential Equations that are Competitive or Cooperative II: Convergence Almost Everywhere , 1985 .

[40]  James Kapinski,et al.  Simulation-Driven Reachability Using Matrix Measures , 2017, ACM Trans. Embed. Comput. Syst..