text:
using JuMP, EAGO

                     m = Model()

                     EAGO.register_eago_operators!(m)

                     @variable(m, -1 <= x[i=1:3] <= 1)

                     @variable(m, -96.83657520547105 <= q <= 99.93991688919364)

                     add_NL_constraint(m, :(softplus(-0.425318666230349 + 0.38103828240531046*softplus(-0.8665046595235606 + 0.28748284249652833*softplus(-0.8375600615394445 + -0.3789154415482061*softplus(0.2477035392889526 + 0.08135346709319213*$(x[1]) + -0.01347278979219979*$(x[2]) + -0.3271447095671336*$(x[3])) + 0.07189450245344364*softplus(-0.3084010115418012 + -0.34365741154933094*$(x[1]) + 0.16330662932230755*$(x[2]) + 0.9533697617837236*$(x[3])) + 0.2809909552456431*softplus(-0.592488977002581 + -0.38831122310783517*$(x[1]) + 0.866474047614362*$(x[2]) + 0.9220781096274604*$(x[3])) + 0.287569201630955*softplus(0.40202873042762777 + -0.8029907135438785*$(x[1]) + -0.15012505567524315*$(x[2]) + -0.8014191780350699*$(x[3])) + 0.8855658095965167*softplus(0.21118749062127584 + -0.4763169362085753*$(x[1]) + -0.771339928691416*$(x[2]) + -0.9994771582642419*$(x[3]))) + 0.09303041223572528*softplus(-0.17900884698891417 + 0.025489018355529858*softplus(0.2477035392889526 + 0.08135346709319213*$(x[1]) + -0.01347278979219979*$(x[2]) + -0.3271447095671336*$(x[3])) + 0.1209472934083613*softplus(-0.3084010115418012 + -0.34365741154933094*$(x[1]) + 0.16330662932230755*$(x[2]) + 0.9533697617837236*$(x[3])) + -0.07672913568138462*softplus(-0.592488977002581 + -0.38831122310783517*$(x[1]) + 0.866474047614362*$(x[2]) + 0.9220781096274604*$(x[3])) + -0.5303319518893166*softplus(0.40202873042762777 + -0.8029907135438785*$(x[1]) + -0.15012505567524315*$(x[2]) + -0.8014191780350699*$(x[3])) + 0.9964375681117983*softplus(0.21118749062127584 + -0.4763169362085753*$(x[1]) + -0.771339928691416*$(x[2]) + -0.9994771582642419*$(x[3]))) + -0.7940643909981655*softplus(0.2763185052996153 + 0.4415642159321509*softplus(0.2477035392889526 + 0.08135346709319213*$(x[1]) + -0.01347278979219979*$(x[2]) + -0.3271447095671336*$(x[3])) + -0.3641634760666457*softplus(-0.3084010115418012 + -0.34365741154933094*$(x[1]) + 0.16330662932230755*$(x[2]) + 0.9533697617837236*$(x[3])) + -0.6145120373835589*softplus(-0.592488977002581 + -0.38831122310783517*$(x[1]) + 0.866474047614362*$(x[2]) + 0.9220781096274604*$(x[3])) + -0.9072464291184885*softplus(0.40202873042762777 + -0.8029907135438785*$(x[1]) + -0.15012505567524315*$(x[2]) + -0.8014191780350699*$(x[3])) + -0.5931047030579473*softplus(0.21118749062127584 + -0.4763169362085753*$(x[1]) + -0.771339928691416*$(x[2]) + -0.9994771582642419*$(x[3]))) + -0.6706641926349142*softplus(0.7070002419162837 + 0.7141753288734574*softplus(0.2477035392889526 + 0.08135346709319213*$(x[1]) + -0.01347278979219979*$(x[2]) + -0.3271447095671336*$(x[3])) + 0.9092263208615878*softplus(-0.3084010115418012 + -0.34365741154933094*$(x[1]) + 0.16330662932230755*$(x[2]) + 0.9533697617837236*$(x[3])) + -0.9578645519047213*softplus(-0.592488977002581 + -0.38831122310783517*$(x[1]) + 0.866474047614362*$(x[2]) + 0.9220781096274604*$(x[3])) + 0.28982257172438475*softplus(0.40202873042762777 + -0.8029907135438785*$(x[1]) + 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-0.771339928691416*$(x[2]) + -0.9994771582642419*$(x[3]))))) - $q <= 0.0))

                     @objective(m, Min, q)

                     return m


text:
# DESCRIPTION
# Computes the linearized dynamics of a nonlinear system (acfun), given the
# trim state and control vectors (x0 and u0).
#
# INPUT
# - acfun: name of system dynamics function
# - x0: trim state vector
# - u0: trim control vector
#
# OUTPUT
# - A, B, C, D: coefficient matrices of linearized dynamics
#
# ------------------------------------------------------------------------------

function linearize(acfun,x0,u0)
    #xdot0=H60!(xdot_in,x0,u0,t)
    #xdot0=acfun(xdot_in,x0,u0,t)
    A=zeros(NSTATES,NSTATES)
    B=zeros(NSTATES,NCTRLS)
    C=zeros(NOUT,NSTATES)
    D=zeros(NOUT,NCTRLS)
    # A and B matrices
    x_p=zeros(NSTATES)
    u_p=zeros(NCTRLS)
    for k=1:NSTATES
        x_p[:]=x0[:]
        x_p[k]=x_p[k]+DELXLIN[k]
        xdot_p1, y_p1=acfun(x_p,u0)
        x_p[k]=x_p[k]-2*DELXLIN[k]
        xdot_p2, y_p2=acfun(x_p,u0)
        #
        A[:,k]=(xdot_p1-xdot_p2)/(2*DELXLIN[k])
        C[:,k]=(y_p1-y_p2)/(2*DELXLIN[k])
    end
    # C and D matrices
    for k=1:NCTRLS
        u_p[:]=u0[:]
        u_p[k]=u_p[k]+DELCLIN[k]
        xdot_p1, y_p1=acfun(x0,u_p)
        u_p[k]=u_p[k]-2*DELCLIN[k]
        xdot_p2, y_p2=acfun(x0,u_p)
        B[:,k]=(xdot_p1-xdot_p2)/(2*DELCLIN[k])
        D[:,k]=(y_p1-y_p2)/(2*DELCLIN[k])
    end
    #
    return A, B, C, D
end

ext: jl