A Hybrid Reachability Algorithm

A Hybrid Reachability Algorithm Using Zonotopes


In this section we present an algorithm implemented using LazySets that computes the reach sets of a hybrid system of linear ordinary differential equations (ODE). This algorithm is an extension of the one presented in A Reachability Algorithm Using Zonotopes.

We consider a simple case here where modes do not have invariants and transitions do not have updates. In set-based analysis like ours, it may make sense to take a transition as soon as one state in the current set of states can take it. Note that this is not equivalent to must semantics of hybrid automata (also called urgent transitions), which is defined on single trajectories. We also offer the usual may transitions interpretation.

Hybrid algorithm

The hybrid algorithm maintains a queue of triples $(m, X, t)$ where $m$ is a mode, $X$ is a set of states, and $t$ is a time point. For each element in the queue the algorithm calls the Continuous algorithm to compute the reachable states in the current mode $m$, starting in the current states $X$ at time $t$. The result is a flowpipe, i.e., a sequence of sets of states. For each of those sets we check intersection with the guards of $m$'s outgoing transitions. Depending on the transition semantics, we add the discrete successors to the queue and continue with the next iteration until the queue is empty.

using Plots, LazySets

function reach_hybrid(As, Ts, init, δ, μ, T, max_order, instant_transitions)
    # initialize queue with initial mode and states at time t=0
    queue = [(init[1], init[2], 0.)]

    res = Tuple{LazySet, Int}[]
    while !isempty(queue)
        init, loc, t = pop!(queue)
        println("currently in location $loc at time $t")
        R = reach_continuous(As[loc], init, δ, μ, T-t, max_order)
        found_transition = false
        for i in 1:length(R)-1
            S = R[i]
            push!(res, (S, loc))
            for (guard, tgt_loc) in Ts[loc]
                if !is_intersection_empty(S, guard)
                    new_t = t + δ * i
                    push!(queue, (S, tgt_loc, new_t))
                    found_transition = true
                    println("transition $loc -> $tgt_loc at time $new_t")
            if instant_transitions && found_transition
        if !instant_transitions || !found_transition && length(R) > 0
            push!(res, (R[end], loc))
    return res

Continuous algorithm

This is basically the same implementation as outlined in the section A Reachability Algorithm Using Zonotopes, only that this time we use concrete operations on zonotopes.

function reach_continuous(A, X0, δ, μ, T, max_order)
    # bloating factors
    Anorm = norm(A, Inf)
    α = (expm(δ*Anorm) - 1 - δ*Anorm)/norm(X0, Inf)
    β = (expm(δ*Anorm) - 1)*μ/Anorm

    # discretized system
    n = size(A, 1)
    ϕ = expm(δ*A)
    N = floor(Int, T/δ)

    # preallocate array
    R = Vector{LazySet}(N)
    if N == 0
        return R

    # initial reach set in the time interval [0, δ]
    ϕp = (I+ϕ)/2
    ϕm = (I-ϕ)/2
    c = X0.center
    gens = hcat(ϕp * X0.generators, ϕm * c, ϕm * X0.generators)
    R[1] = minkowski_sum(Zonotope(ϕp * c, gens),
                         Zonotope(zeros(n), Matrix((α + β)*I, n, n)))
    if order(R[1]) > max_order
        R[1] = reduce_order(R[1], max_order)

    # set recurrence for [δ, 2δ], ..., [(N-1)δ, Nδ]
    ballβ = Zonotope(zeros(n), Matrix(β*I, n, n))
    for i in 2:N
        R[i] = minkowski_sum(linear_map(ϕ, R[i-1]), ballβ)
        if order(R[i]) > max_order
            R[i] = reduce_order(R[i], max_order)
    return R

Plotting results

For illustration purposes it is helpful to plot the flowpipes in different colors, depending on the current mode. The following function does that for 2-mode models.

function plot_res(res)
    p = plot()
    for i in 1:length(res)
        if res[i][2] == 1
            c = "blue"
        elseif res[i][2] == 2
            c = "red"
        plot!(p, reduce_order(res[i][1], 2), color=c, alpha=0.1)
    return p


We consider an extension of the example presented in Reachability of uncertain linear systems using zonotopes, A. Girard, HSCC. Vol. 5. 2005 to a hybrid system with two modes $\ell_i$, $i = 1, 2$, with initial states $[0.9, 1.1] \times [-0.1, 0.1]$ and uncertain inputs from a set $u$ with $\mu = \Vert u \Vert_\infty = 0.001$.

The dynamics matrices $A_i$ are defined as follows:

\[ A_1 = \begin{pmatrix} -1 & -4 \\ 4 & -1 \end{pmatrix}, \qquad A_2 = \begin{pmatrix} 1 & 4 \\ -4 & -1 \end{pmatrix}.\]

We add a transition $t_i$ from mode $\ell_i$ to $\ell_{3-i}$ with a hyperplane guard $g_i$:

\[ g_1 \triangleq x_1 = -0.5 \qquad g_2 \triangleq x_2 = -0.3\]

LazySets offers an order reduction function for zonotopes, which we used here with an upper bound of 10 generators. We plot the reachable states for the time interval $[0, 4]$ and time step $δ = 0.001$.

    # dynamics
    A1 = [-1 -4; 4 -1]
    A2 = [1 4; -4 -1]
    As = [A1, A2]

    # transitions
    t1 = [(Hyperplane([1., 0.], -0.5), 2)]
    t2 = [(Hyperplane([0., 1.], -0.3), 1)]
    Ts = [t1, t2]

    # initial condition
    X0 = Zonotope([1.0, 0.0], Matrix(0.1*I, 2, 2))
    init_loc = 1
    init = (X0, init_loc)

    # input uncertainty
    μ = 0.001

    # discretization step
    δ = 0.001

    # time bound
    T = 4.

    # maximum order of zonotopes
    max_order = 10

    # take transitions only the first time they are enabled?
    instant_transitions = true

    # run analysis
    res = reach_hybrid(As, Ts, init, δ, μ, T, max_order, instant_transitions)

    # plot result
-0.3 0.0 0.3 0.6 0.9 -0.25 0.00 0.25 0.50 0.75