ROCS: A Robustly Complete Control Synthesis Tool for Nonlinear Dynamical Systems

This paper presents ROCS, an algorithmic control synthesis tool for nonlinear dynamical systems. Different from other formal control synthesis tools, it guarantees to generate a control strategy with respect to a robustly realizable specification for a nonlinear system. At the core of ROCS is the interval branch-and-bound scheme with a precision control parameter that reflects the robustness of the realizability of the specification. It also supports multiple variable precision control parameters to achieve higher efficiency.

[1]  Ufuk Topcu,et al.  Synthesis of Reactive Switching Protocols From Temporal Logic Specifications , 2013, IEEE Transactions on Automatic Control.

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

[3]  Chin-Laung Lei,et al.  Efficient Model Checking in Fragments of the Propositional Mu-Calculus (Extended Abstract) , 1986, LICS.

[4]  Jun Liu,et al.  Robust Abstractions for Control Synthesis: Completeness via Robustness for Linear-Time Properties , 2017, HSCC.

[5]  Thomas A. Henzinger,et al.  From verification to control: dynamic programs for omega-regular objectives , 2001, Proceedings 16th Annual IEEE Symposium on Logic in Computer Science.

[6]  Hadas Kress-Gazit,et al.  LTLMoP: Experimenting with language, Temporal Logic and robot control , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Ufuk Topcu,et al.  TuLiP: a software toolbox for receding horizon temporal logic planning , 2011, HSCC '11.

[8]  Gunther Reissig,et al.  Feedback Refinement Relations for the Synthesis of Symbolic Controllers , 2015, IEEE Transactions on Automatic Control.

[9]  Christel Baier,et al.  Principles of Model Checking (Representation and Mind Series) , 2008 .

[10]  Edmund M. Clarke,et al.  State space reduction using partial order techniques , 1999, International Journal on Software Tools for Technology Transfer.

[11]  Manuel Mazo,et al.  Symbolic Models for Nonlinear Control Systems Without Stability Assumptions , 2010, IEEE Transactions on Automatic Control.

[12]  Edmund M. Clarke,et al.  Model Checking , 1999, Handbook of Automated Reasoning.

[13]  Jun Liu,et al.  Robustly Complete Reach-and-Stay Control Synthesis for Switched Systems via Interval Analysis , 2018, 2018 Annual American Control Conference (ACC).

[14]  Amir Pnueli,et al.  Synthesis of Reactive(1) designs , 2006, J. Comput. Syst. Sci..

[15]  Manuel Mazo,et al.  PESSOA: A Tool for Embedded Controller Synthesis , 2010, CAV.

[16]  Jun Liu,et al.  Finite abstractions with robustness margins for temporal logic-based control synthesis , 2016 .

[17]  Majid Zamani,et al.  SCOTS: A Tool for the Synthesis of Symbolic Controllers , 2016, HSCC.

[18]  Christel Baier,et al.  Principles of model checking , 2008 .

[19]  Paulo Tabuada,et al.  Approximately Bisimilar Symbolic Models for Incrementally Stable Switched Systems , 2008, IEEE Transactions on Automatic Control.

[20]  Paulo Tabuada,et al.  Approximately bisimilar symbolic models for nonlinear control systems , 2007, Autom..

[21]  Jun Liu,et al.  Invariance Control Synthesis for Switched Nonlinear Systems: An Interval Analysis Approach , 2018, IEEE Transactions on Automatic Control.

[22]  Antoine Girard,et al.  CoSyMA: a tool for controller synthesis using multi-scale abstractions , 2013, HSCC '13.

[23]  Petter Nilsson,et al.  Augmented finite transition systems as abstractions for control synthesis , 2017, Discret. Event Dyn. Syst..

[24]  Wei Chen,et al.  dReach: δ-Reachability Analysis for Hybrid Systems , 2015, TACAS.