S-TaLiRo is a Matlab toolbox that searches for trajectories of minimal robustness in Simulink / Stateflow. It can analyze arbitrary Simulink models or user-defined functions that model the system. At the heart of the tool, we use randomized testing based on stochastic optimization techniques including Monte-Carlo methods, Ant-Colony Optimization and so on. Among the advantages of the toolbox is the seamless integration inside the Matlab environment, which is widely used in the industry for model-based development of control software. 

S-TaLiRo has been designed to be seamlessly integrated in the model-based design process of Matlab/Simulink (tm). The user designs the model in the Simulink/Stateflow (tm) environment as before. At present, the only requirement is that input signals must be provided to the Simulink model through input ports. Then S-TaLiRo is executed with the name of the Simulink model as a parameter along with the set of initial conditions, the constraints on the input signals (if any) and the Metric Temporal Logic (MTL) specification. The user may select one of the following stochastic optimization algorithms: Simulated Annealing, Ant Colony Optimization, Genetic Algorithms and Cross Entropy. However, the architecture of S-TaLiRo is modular and, thus, any other stochastic optimization method can be readily implemented.
  • Run setup_staliro to install and setup the toolbox.
  • Type help staliro to get a detailed information on the usage of the toolbox.
  • Also take a look at the Benchmarks and Demos folders.


  • Found S-Taliro useful in your research?
  • Wish to report a bug?
  • Have any suggestions or questions?
  • Would you like to be added to future releases of S-Taliro?

Please feel free to contact Georgios Fainekos at fainekos at asu dot edu. When you contact us, please mention the revision number from the SVN which you are using (or the version number if you are using an older version).