Skip to content

Latest commit

 

History

History
87 lines (66 loc) · 3.48 KB

README.md

File metadata and controls

87 lines (66 loc) · 3.48 KB
Raw

ControlTimingSafety

Build Status Documentation Status

Tools for verifying safety of control systems that may experience timing uncertainty.

This package provides tools for calculating safe overapproximations and probabilistic estimations of the maximum deviation a control system may suffer due to deadline misses. If you find these tools useful in your research, please consider citing the papers that the algorithms originated in.

Installation

From the Julia REPL, run:

using Pkg; Pkg.add(url="https://github.com/Ratfink/ControlTimingSafety.jl")

Usage

Given a discrete-time state space model sys from the ControlSystems.jl package, and a feedback gain matrix K, first create an Automaton object using one of the constructors for the strategies described in the paper:

hk = hold_kill(sys, K, max_misses)
hs = hold_skip_next(sys, K, max_misses)
zk = zero_kill(sys, K, max_misses)
zs = zero_skip_next(sys, K, max_misses)

To run the Bounded Runs Iteration algorithm for this automaton, first create the initial set, then run the algorithm for a given per-iteration run length n and number of iterations t:

augbounds = augment(automaton, bounds)
all_bounds = bounded_runs_iter(automaton, augbounds, n, t)

The deviation at each time step can then be computed thusly:

d = deviation(automaton, augbounds, all_bounds)

For a complete example, see the demo Pluto notebook, notebooks/control_timing_safety.jl.

Citing

If you use this code in your research, we ask that you consider citing the relevant papers. For the bounded_runs_iter function for overapproximating the maximum deviation, please cite:

Clara Hobbs, Bineet Ghosh, Shengjie Xu, Parasara Sridhar Duggirala, and Samarjit Chakraborty. "Safety Analysis of Embedded Controllers under Implementation Platform Timing Uncertainties." TCAD 2022. DOI: 10.1109/TCAD.2022.3198905

For the estimate_deviation function, based on Jeffreys' Bayes factor hypothesis testing, please cite:

Bineet Ghosh, Clara Hobbs, Shengjie Xu, Parasara Sridhar Duggirala, James H. Anderson, P. S. Thiagarajan, and Samarjit Chakraborty. "Statistical Hypothesis Testing of Controller Implementations Under Timing Uncertainties." RTCSA 2022. DOI: 10.1109/RTCSA55878.2022.00008

For the synthesize_constraints function to generate the list of safe weakly-hard constraints and schedule_xghtc function to synthesize schedules from weakly-hard constraints, please cite:

Shengjie Xu, Bineet Ghosh, Clara Hobbs, P. S. Thiagarajan, and Samarjit Chakraborty. "Safety-Aware Flexible Schedule Synthesis for Cyber-Physical Systems Using Weakly-Hard Constraints." ASPDAC 2023. DOI: 10.1145/3566097.3567848

As we continue this line of work, we intend to incorporate new algorithms in this package, and the list of papers will be updated accordingly (along with specific the functions described in each).