SySCoRe stands for Synthesis via Stochastic Coupling Relations for stochastic continuous state systems. It is a toolbox that synthesizes controllers for stochastic continuous-state systems to satisfy linear temporal logic (LTL) specifications. Starting from a system description and a co-safe linear temporal logic specification (scLTL), SySCoRe provides all necessary functions for synthesizing a robust controller and quantifying the associated formal robustness guarantees. It distinguishes itself from other available tools by supporting nonlinear dynamics, complex co-safe temporal logic specifications over infinite horizons and model-order reduction.
To achieve this, SySCoRe first generates a finite-state abstraction of the provided model and performs probabilistic model checking. Then, it establishes a probabilistic coupling to the original stochastic system encoded in an approximate simulation relation, based on which a lower bound on the satisfaction probability is computed. SySCoRe provides non-trivial lower bounds for infinite-horizon properties and unbounded disturbances since its computed error does not grow linear in the horizon of the specification. It exploits a tensor representation to facilitate the efficient computation of transition probabilities. We showcase these features on several tutorials.
See the LICENSE file for the license terms of this toolbox.
See the folder SySCoRe-software/doc for full documentation and a GettingStarted file.
This toolbox can be running on Windows, Ubuntu, and Mac OS. Note that all paths in this tutorial follow the Linux convention. Beware of changes if you are using Windows or Mac OS.
This toolbox is created by: Birgit van Huijgevoort, Oliver Schön, Sadegh Soudjani and Sofie Haesaert.
Install MATLAB toolboxes Control System Toolbox, Statistics and Machine Learning Toolbox and Deep Learning Toolbox. This can be done by running the installation package of MATLAB and select the corresponding terms in the installation options. You can also install them in the MATLAB Window.
Install the tbxmanager following this instruction which includes a cluster MATLAB toolboxes for you, including YALMIP, the sedumi solver, mpt that works with polyhedrons, which are necessary dependencies for this toolbox. Let us use $TBX_MANAGER_DIR to denote the path of the root directory of the installation. The associated MATLAB toolboxes are placed under $TBX_MANAGER_DIR/toolboxes/. You may also replace them with your own installations, although it is not necessary.
The mosek solver is also needed, which can be installed following this instruction. Note that mosek has different versions among Conda, Windows, Linux, and Mac OS. Be careful that you are downloading the correct version according to your operating system.
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IMPORTANT information about license!!!: to use mosek, you need to obtain a personal license file
mosek.licfollowing this intruction and place it in the directory regulated by the installation instruction. This license is only for a specific user. But it can be used on different operating systems. -
IMPORTANT for Windows user!!!: it is recommended to use the Manual Installers (Alternative) option to directly download the archive of mosek and extract it to your preferred directory, instead of installing from a
.msifile using the Default Installers (Preferred) option. The reason is that the latter will defaultly install mosek in yourC:/Program Filesdirectory. Note that MATLAB does not accept spaces in a path...
Supposing that the path of the root directory of your mosek is $MOSEK_DIR, what we are concerned with is the path of the MATLAB toolbox of mosek, i.e., $MOSEK_MATLAB_DIR=$MOSEK_DIR/mosek/X.X/toolbox/r20xxa, where X and x denote the version numbers.
Download the tensor toolbox following this instruction. Note down its root path as $TENSOR_DIR.
Before you run any codes in this toolbox, you need to add the root paths of your tbxmanager, mosek solver, and tensor_toolbox to MATLAB. We recommend to simplify this process via one-time configuration. Supposing that the root directory of this toolbox is $SYSCORE_ROOT=SySCoRe-software, open $SYSCORE_ROOT/config.m, and fill the paths of the above toolboxes in your system to the corresponding addpath statements.
%% Add the paths of the dependencies
% IMPORTANT!!!: this is just a template. You need to replace the paths below using your own paths of installation
try % Add the path to the tbxmanager
addpath(genpath('~/tbxmanager/'));
catch
warning('Could not find mpt3 toolbox. Synthesis may fail.');
end
try % Add the path to the tensor_toolbox
addpath(genpath('~/tensor_toolbox/'));
catch
warning('Could not find tensor toolbox. Synthesis may fail.');
end
try % Add the path to the mosek solver
addpath(genpath('~/mosek/mosek/10.0/toolbox/r2017a'));
catch
warning('Could not find mosek solver. Synthesis may fail.');
end
Meanwhile, the configuration file also add the paths of all subfolders of this toolbox via
%% Add all subfolders of this toolbox to the MATLAB path.
% You do not need to modify this part
addpath(genpath(fileparts(which(mfilename))));
This configuration only needs to be conducted once. Whenever you run a code using this toolbox, run this config.m file first.
You can run all six tutorial scripts under the SYSCORE_ROOT/Tutorials/ directory.
$SYSCORE_ROOT/Tutorials/CarPark1D.m$SYSCORE_ROOT/Tutorials/CarPark2D_RunningExample.m$SYSCORE_ROOT/Tutorials/CarPark2D_interfaceOption.m$SYSCORE_ROOT/Tutorials/PackageDelivery.m$SYSCORE_ROOT/Tutorials/BAS.m$SYSCORE_ROOT/Tutorials/VanderPol.m
Note that all tutorial codes start with the following statements:
clc; clear; close all; % Clean the environment;
cd([fileparts(which(mfilename)), '/..']); % Navigate back to the root directory of the toolbox;
run config.m % Run the configuration file.
It is important that the running should be conducted under the root directory SYSCORE_ROOT since many functions in this toolbox use relative paths. Your code can also start with these statements, but do change the relative path /.. in the statement according to the specific position of your m-script.
Use the following classes and functions to solve your problems.
Use the following classes to build a model:
$SYSCORE_ROOT/Models/LinModel.m: for linear models;$SYSCORE_ROOT/Models/NonlinModel.m: for nonlinear models.
Use the following function to translate an LTL formula to a DFA:
$SYSCORE_ROOT/Specification/TranslateSpec.m
This function calls a series functions following TranslateSpec -> spec2buchi -> create_buchi, and the last one runs the executable $SYSCORE_ROOT/Specification/LTL2BA/ltl2ba/ltl2ba_X which generates a Büchi Automaton, where X can be either win, linux, or mac, respectively representing the executables that should be run on Windows, Ubuntu, or Mac OS. The source code of these executables is written in C and can be obtained in here. An online tool of translating an LTL specification to a Büchi Automaton can be found here.
Use the following functions to compute abstract models:
$SYSCORE_ROOT/Abstractions/FSabstraction.m: for finite state abstraction;$SYSCORE_ROOT/Abstractions/ModelReduction.mfor a reduced-order model;$SYSCORE_ROOT/Abstractions/PWAapproximation.mfor a piecewise-affine approximation.
Use the following function to quantify the simulation relation between the abstract and original model
$SYSCORE_ROOT/Similarity/QuantifySim.m
Use the following classes and functions to synthesize a robust controller
$SYSCORE_ROOT/Controllers/RefineController.m: the class for a refined controller;$SYSCORE_ROOT/Controllers/SynthesizeRobustController.m: synthesize a robust controller;$SYSCORE_ROOT/Controllers/ImplementController.m: simulate the closed loop system with the synthesized controller.
- van Huijgevoort, B. C. and Haesaert, S. (2020). Similarity quantification for linear stochastic systems: A coupling compensator approach. Automatica, 144, 110476.
- Haesaert, S., **Soudjani, S. **, and Abate, A. (2017). Verification of general Markov decision processes by approximate similarity relations and policy refinement. SIAM Journal on Control and Optimization, 55(4), 2333-2367.
- Haesaert, S. and Soudjani, S. (2020). Robust dynamic programming for temporal logic control of stochastic systems. IEEE Transactions on Automatic Control, 66(6), 2496-2511.
- van Huijgevoort, B.C. and Haesaert, S. (2022). Temporal logic control of nonlinear stochastic systems using a piecewise-affine abstraction. [Link]