diff --git a/docs/src/tutorial-solve.md b/docs/src/tutorial-solve.md
index 33978239..8ada8bca 100644
--- a/docs/src/tutorial-solve.md
+++ b/docs/src/tutorial-solve.md
@@ -148,7 +148,7 @@ for i in 1:n # state
 end
 
 for i in 1:n # costate
-    plot!(plt[n+i], t, t -> p(t)[i]; color=2, linestyle=:dash, label=:none)
+    plot!(plt[n+i], t, t -> -p(t)[i]; color=2, linestyle=:dash, label=:none)
 end
 
 for i in 1:m # control
diff --git a/ext/Descriptions/quadrotor.md b/ext/Descriptions/quadrotor.md
deleted file mode 100644
index 517946d2..00000000
--- a/ext/Descriptions/quadrotor.md
+++ /dev/null
@@ -1,90 +0,0 @@
-This problem models the **minimum-time maneuvering of a quadrotor** from an initial position to a target position while respecting thrust and tilt constraints.  
-The quadrotor is modelled as a rigid body with translational and rotational dynamics, subject to actuator limits and tilt angle restrictions.
-
-### System Description
-
-The system has **8 states** and **4 controls**:
-
-- **States**: 
-  - $p_1, p_2, p_3$: position coordinates of the quadrotor  
-  - $v_1, v_2, v_3$: linear velocities  
-  - $\phi$: roll angle  
-  - $\theta$: pitch angle  
-
-- **Controls**: 
-  - $a_t$: total thrust magnitude  
-  - $\dot{\phi}$: roll rate  
-  - $\dot{\theta}$: pitch rate  
-  - $\psi$: yaw angle
-
-### Constraints
-
-- **Time horizon**: $t_f \ge 0.1$  
-- **State constraints**: 
-
-```math
--\pi/2 \le \phi(t), \theta(t) \le \pi/2
-```
-
-- **Control constraints**: 
-
-```math
-a_{\min} \le a_t(t) \le a_{\max}, \quad
--d_{\text{tilt,max}} \le \dot{\phi}(t), \dot{\theta}(t) \le d_{\text{tilt,max}}
-```
-
-- **Tilt path constraint**: 
-
-```math
-\cos\theta(t) \cos\phi(t) \ge \cos(\text{tiltmax})
-```
-
-- **Boundary conditions**:  
-
-```math
-p(0) = p_0, \quad v(0) = v_0, \quad \phi(0) = \phi_0, \quad \theta(0) = \theta_0
-```
-
-```math
-p(t_f) = p_f, \quad v(t_f) = v_f
-```
-
-### Dynamics
-
-The quadrotor dynamics are described by
-
-```math
-\dot{p} = v, \quad \dot{v} = a
-```
-
-```math
-\dot{\phi} = \dot{\phi}_{\rm control}, \quad \dot{\theta} = \dot{\theta}_{\rm control}
-```
-
-where the acceleration $a$ is computed from the total thrust $a_t$ and orientation angles $(\phi, \theta, \psi)$:
-
-```math
-R = 
-\begin{bmatrix}
-c_\psi c_\theta & c_\psi s_\theta s_\phi - s_\psi c_\phi & c_\psi s_\theta c_\phi + s_\psi s_\phi \\
-s_\psi c_\theta & s_\psi s_\theta s_\phi + c_\psi c_\phi & s_\psi s_\theta c_\phi - c_\psi s_\phi \\
--s_\theta & c_\theta s_\phi & c_\theta c_\phi
-\end{bmatrix}, \quad
-a = R \begin{bmatrix} 0 \\ 0 \\ a_t \end{bmatrix} + \begin{bmatrix} 0 \\ 0 \\ -g \end{bmatrix}
-```
-
-with $c_\phi = \cos\phi$, $s_\phi = \sin\phi$, etc.
-
-### Objective
-
-The goal is to **minimize the final time** $t_f$, with a small regularization on control inputs and yaw angle:
-
-```math
-J = t_f + \int_0^{t_f} \Big( 1e-8 (a_t^2 + \phi^2 + \theta^2 + \psi^2) + 1e2 (\psi - \psi_0)^2 \Big) dt \to \min
-```
-
-### References
-
-- Vanroye, L., Sathya, A., De Schutter, J., & Decré, W. (2023). FATROP: A Fast Constrained Optimal Control Problem Solver for Robot Trajectory Optimization and Control. *arXiv preprint arXiv:2303.16746*. Retrieved from https://arxiv.org/pdf/2303.16746
-- Mellinger, D., & Kumar, V. (2011). Minimum snap trajectory generation and control for quadrotors. *2011 IEEE International Conference on Robotics and Automation*, 2520–2525.  
-- Faessler, M., Franchi, A., & Scaramuzza, D. (2018). Differential flatness of quadrotor dynamics subject to rotor drag for accurate tracking of high-speed trajectories. *IEEE Robotics and Automation Letters*, 3(2), 620–626.  
diff --git a/ext/JuMPModels/quadrotor.jl b/ext/JuMPModels/quadrotor.jl
deleted file mode 100644
index 4740e835..00000000
--- a/ext/JuMPModels/quadrotor.jl
+++ /dev/null
@@ -1,153 +0,0 @@
-"""
-$(TYPEDSIGNATURES)
-
-Constructs and returns a JuMP model for the **Quadrotor Optimal Control Problem**.  
-The model represents the 3D dynamics of a quadrotor with translational and rotational states, subject to thrust and tilt constraints.  
-The objective is to minimise the final time (`tf`) to reach a specified target position while respecting path and actuation constraints.
-
-# Arguments
-
-- `::JuMPBackend`: Specifies the backend for building the JuMP model.
-- `N::Int=50`: (Keyword) Number of discretisation steps for the time grid.
-
-# Returns
-
-- `model::JuMP.Model`: A JuMP model representing the Quadrotor optimal control problem.
-
-# Example
-
-```julia-repl
-julia> using OptimalControlProblems
-julia> using JuMP
-
-julia> model = OptimalControlProblems.quadrotor(JuMPBackend(); N=20)
-```
-
-# References
-
-- Problem formulation available at: https://arxiv.org/pdf/2303.16746
-"""
-function OptimalControlProblems.quadrotor(
-    ::JuMPBackend, args...; N::Int=steps_number_data(:quadrotor), kwargs...
-)
-
-    # parameters
-    g = 9.81
-    atmin = 0
-    atmax = 9.18 * 5
-    tiltmax = 1.1 / 2
-    dtiltmax = 6 / 2
-    p0 = [0, 0, 2.5]
-    v0 = [0, 0, 0]
-    u0 = [9.81, 0, 0, 0]
-    pf = [0.01, 5, 2.5]
-    vf = [0, 0, 0]
-
-    # model
-    model = JuMP.Model(args...; kwargs...)
-
-    # 
-    @variables(
-        model,
-        begin
-
-            # variable
-            0.1 <= tf, (start = 1)
-
-            # state
-            p₁[0:N], (start = 0.1)
-            p₂[0:N], (start = 0.1)
-            p₃[0:N], (start = 0.1)
-            v₁[0:N], (start = 0.1)
-            v₂[0:N], (start = 0.1)
-            v₃[0:N], (start = 0.1)
-            -π / 2 <= ϕ[0:N] <= π / 2, (start = 0.1)
-            -π / 2 <= θ[0:N] <= π / 2, (start = 0.1)
-
-            # control
-            atmin <= at[0:N] <= atmax, (start = 10)
-            -dtiltmax <= dϕ[0:N] <= dtiltmax, (start = 0.1)
-            -dtiltmax <= dθ[0:N] <= dtiltmax, (start = 0.1)
-            ψ[0:N], (start = 0.1)
-        end
-    )
-
-    # path constraints
-    @constraints(
-        model,
-        begin
-            cond_tiltmax[i = 0:N], cos(θ[i]) * cos(ϕ[i]) >= cos(tiltmax)
-        end
-    )
-
-    # initial and final conditions
-    @constraints(
-        model,
-        begin
-            p₁_i, p₁[0] == p0[1]
-            p₂_i, p₂[0] == p0[2]
-            p₃_i, p₃[0] == p0[3]
-            v₁_i, v₁[0] == v0[1]
-            v₂_i, v₂[0] == v0[2]
-            v₃_i, v₃[0] == v0[3]
-            ϕ_i, ϕ[0] == u0[2]
-            θ_i, θ[0] == u0[3]
-            p₁_f, p₁[N] == pf[1]
-            p₂_f, p₂[N] == pf[2]
-            p₃_f, p₃[N] == pf[3]
-            v₁_f, v₁[N] == vf[1]
-            v₂_f, v₂[N] == vf[2]
-            v₃_f, v₃[N] == vf[3]
-        end
-    )
-
-    # dynamics
-    @expressions(
-        model,
-        begin
-
-            #
-            step, tf / N
-
-            # dynamics
-            cr[i = 0:N], cos(ϕ[i])
-            sr[i = 0:N], sin(ϕ[i])
-            cp[i = 0:N], cos(θ[i])
-            sp[i = 0:N], sin(θ[i])
-            cy[i = 0:N], cos(ψ[i])
-            sy[i = 0:N], sin(ψ[i])
-            R[i = 0:N],
-            [
-                (cy[i] * cp[i]) (cy[i] * sp[i] * sr[i] - sy[i] * cr[i]) (cy[i] * sp[i] * cr[i] + sy[i] * sr[i])
-                (sy[i] * cp[i]) (sy[i] * sp[i] * sr[i] + cy[i] * cr[i]) (sy[i] * sp[i] * cr[i] - cy[i] * sr[i])
-                (-sp[i]) (cp[i] * sr[i]) (cp[i] * cr[i])
-            ]
-            at_[i = 0:N], R[i] * [0; 0; at[i]]
-            g_, [0; 0; -g]
-            a[i = 0:N], at_[i] + g_
-
-            # objective
-            dc[i = 0:N],
-            1e-8 * (at[i]^2 + ϕ[i]^2 + θ[i]^2 + ψ[i]^2) + 1e2 * (ψ[i] - u0[3])^2
-        end
-    )
-
-    @constraints(
-        model,
-        begin
-            ∂p₁[i = 1:N], p₁[i] == p₁[i - 1] + 0.5 * step * (v₁[i] + v₁[i - 1])
-            ∂p₂[i = 1:N], p₂[i] == p₂[i - 1] + 0.5 * step * (v₂[i] + v₂[i - 1])
-            ∂p₃[i = 1:N], p₃[i] == p₃[i - 1] + 0.5 * step * (v₃[i] + v₃[i - 1])
-            ∂v₁[i = 1:N], v₁[i] == v₁[i - 1] + 0.5 * step * (a[i][1] + a[i - 1][1])
-            ∂v₂[i = 1:N], v₂[i] == v₂[i - 1] + 0.5 * step * (a[i][2] + a[i - 1][2])
-            ∂v₃[i = 1:N], v₃[i] == v₃[i - 1] + 0.5 * step * (a[i][3] + a[i - 1][3])
-            ∂ϕ[i = 1:N], ϕ[i] == ϕ[i - 1] + 0.5 * step * (dϕ[i] + dϕ[i - 1])
-            ∂θ[i = 1:N], θ[i] == θ[i - 1] + 0.5 * step * (dθ[i] + dθ[i - 1])
-        end
-    )
-
-    # objective
-    @objective(model, Min, tf + 0.5 * step * sum(dc[i] + dc[i - 1] for i in 1:N))
-
-    return model
-end
diff --git a/ext/MetaData/quadrotor.jl b/ext/MetaData/quadrotor.jl
deleted file mode 100644
index 72cfabfa..00000000
--- a/ext/MetaData/quadrotor.jl
+++ /dev/null
@@ -1,10 +0,0 @@
-quadrotor_meta = OrderedDict(
-    :name => "quadrotor",
-    :N => 50,
-    :minimise => true,
-    :state_name => ["p₁", "p₂", "p₃", "v₁", "v₂", "v₃", "ϕ", "θ"],
-    :costate_name => ["∂p₁", "∂p₂", "∂p₃", "∂v₁", "∂v₂", "∂v₃", "∂ϕ", "∂θ"],
-    :control_name => ["at", "dϕ", "dθ", "ψ"],
-    :variable_name => ["tf"],
-    :final_time => (:free, 1), # first component of the variable
-)
diff --git a/ext/OptimalControlModels/quadrotor.jl b/ext/OptimalControlModels/quadrotor.jl
deleted file mode 100644
index 65a0debd..00000000
--- a/ext/OptimalControlModels/quadrotor.jl
+++ /dev/null
@@ -1,126 +0,0 @@
-"""
-$(TYPEDSIGNATURES)
-
-Constructs an **OptimalControl problem** for the Quadrotor benchmark model.  
-This function defines the state and control variables, system dynamics, bounds, initial and final conditions, and the cost functional, which minimises the final time to reach a target position while including small penalties on control inputs.  
-It returns both a discretised direct optimal control problem (DOCP) and the corresponding nonlinear programming (NLP) model.
-
-# Arguments
-
-- `::OptimalControlBackend`: Placeholder type specifying the OptimalControl backend or solver interface.
-- `N::Int=50`: (Keyword) Number of discretisation points for the direct transcription grid.
-
-# Returns
-
-- `docp`: The direct optimal control problem object representing the Quadrotor problem.
-- `nlp`: The corresponding nonlinear programming model obtained from the DOCP, suitable for numerical optimisation.
-
-# Example
-
-```julia-repl
-julia> using OptimalControlProblems
-
-julia> docp = OptimalControlProblems.quadrotor(OptimalControlBackend(); N=50);
-```
-"""
-function OptimalControlProblems.quadrotor(
-    ::OptimalControlBackend, description::Symbol...; N::Int=steps_number_data(:quadrotor), kwargs...
-)
-
-    # parameters
-    g = 9.81
-    atmin = 0
-    atmax = 9.18 * 5
-    tiltmax = 1.1 / 2
-    dtiltmax = 6 / 2
-    p0 = [0, 0, 2.5]
-    v0 = [0, 0, 0]
-    u0 = [9.81, 0, 0, 0]
-    pf = [0.01, 5, 2.5]
-    vf = [0, 0, 0]
-
-    ocp = @def begin
-        tf ∈ R, variable
-        t ∈ [0, tf], time
-        x = (p₁, p₂, p₃, v₁, v₂, v₃, ϕ, θ) ∈ R⁸, state
-        u = (at, dϕ, dθ, ψ) ∈ R⁴, control
-
-        # state constraints
-        tf ≥ 0.1, (tf_con)
-        -π / 2 ≤ ϕ(t) ≤ π / 2, (ϕ_con)
-        -π / 2 ≤ θ(t) ≤ π / 2, (θ_con)
-
-        # control constraints
-        atmin ≤ at(t) ≤ atmax, (at_con)
-        -dtiltmax ≤ dϕ(t) ≤ dtiltmax, (ϕdot_con)
-        -dtiltmax ≤ dθ(t) ≤ dtiltmax, (θdot_con)
-
-        # path constraints
-        cos(θ(t)) * cos(ϕ(t)) ≥ cos(tiltmax), (tiltmax_con)
-
-        # initial constraints
-        p₁(0) == p0[1], (p₁_i)
-        p₂(0) == p0[2], (p₂_i)
-        p₃(0) == p0[3], (p₃_i)
-        v₁(0) == v0[1], (v₁_i)
-        v₂(0) == v0[2], (v₂_i)
-        v₃(0) == v0[3], (v₃_i)
-        ϕ(0) == u0[2], (ϕ_i)
-        θ(0) == u0[3], (θ_i)
-
-        # final constraints
-        p₁(tf) == pf[1], (p₁_f)
-        p₂(tf) == pf[2], (p₂_f)
-        p₃(tf) == pf[3], (p₃_f)
-        v₁(tf) == vf[1], (v₁_f)
-        v₂(tf) == vf[2], (v₂_f)
-        v₃(tf) == vf[3], (v₃_f)
-
-        # dynamics
-        ẋ(t) == dynamics(x(t), u(t))
-
-        # objective  
-        tf + ∫(1e-8 * (at(t)^2 + ϕ(t)^2 + θ(t)^2 + ψ(t)^2) + 1e2 * (ψ(t) - u0[3])^2) → min
-    end
-
-    function dynamics(x, u)
-        p₁, p₂, p₃, v₁, v₂, v₃, ϕ, θ = x
-        at, dϕ, dθ, ψ = u
-
-        cr = cos(ϕ)
-        sr = sin(ϕ)
-        cp = cos(θ)
-        sp = sin(θ)
-        cy = cos(ψ)
-        sy = sin(ψ)
-        R = [
-            (cy * cp) (cy * sp * sr - sy * cr) (cy * sp * cr + sy * sr)
-            (sy * cp) (sy * sp * sr + cy * cr) (sy * sp * cr - cy * sr)
-            (-sp) (cp * sr) (cp * cr)
-        ]
-        at_ = R * [0; 0; at]
-        g_ = [0; 0; -g]
-        a = at_ + g_
-
-        return [v₁, v₂, v₃, a[1], a[2], a[3], dϕ, dθ]
-    end
-
-    # initial guess
-    xinit = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]  # [p₁, p₂, p₃, v₁, v₂, v₃, ϕ, θ]
-    uinit = [10, 0.1, 0.1, 0.1]  # [at, dϕ, dθ, ψ] 
-    varinit = [1]  # [tf]
-    init = (state=xinit, control=uinit, variable=varinit)
-
-    # DOCP and NLP
-        docp = direct_transcription(
-        ocp, 
-        description...; 
-        lagrange_to_mayer=false,
-        init=init, 
-        grid_size=N, 
-        disc_method=:trapeze, 
-        kwargs...
-    )
-
-    return docp
-end
diff --git a/test/Project.toml b/test/Project.toml
index b71fe07e..89cf12fd 100644
--- a/test/Project.toml
+++ b/test/Project.toml
@@ -14,6 +14,7 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 [compat]
 Aqua = "0.8"
 CTBase = "0.16"
+CTDirect = "0.16"
 Interpolations = "0.15, 0.16"
 Ipopt = "1"
 JuMP = "1"
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diff --git a/test/figures/solution/electric_vehicle.pdf b/test/figures/solution/electric_vehicle.pdf
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diff --git a/test/figures/solution/glider.pdf b/test/figures/solution/glider.pdf
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diff --git a/test/figures/solution/insurance.pdf b/test/figures/solution/insurance.pdf
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diff --git a/test/figures/solution/jackson.pdf b/test/figures/solution/jackson.pdf
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diff --git a/test/figures/solution/moonlander.pdf b/test/figures/solution/moonlander.pdf
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diff --git a/test/figures/solution/robot.pdf b/test/figures/solution/robot.pdf
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diff --git a/test/figures/solution/rocket.pdf b/test/figures/solution/rocket.pdf
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diff --git a/test/figures/solution/space_shuttle.pdf b/test/figures/solution/space_shuttle.pdf
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diff --git a/test/figures/solution/steering.pdf b/test/figures/solution/steering.pdf
index 1c6a9f7c..4be292ca 100644
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diff --git a/test/figures/solution/truck_trailer.pdf b/test/figures/solution/truck_trailer.pdf
index 20cd932f..ea5788f5 100644
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diff --git a/test/figures/solution/vanderpol.pdf b/test/figures/solution/vanderpol.pdf
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diff --git a/test/runtests.jl b/test/runtests.jl
index e7fc0377..a4df4153 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -30,7 +30,7 @@ end
 
 # Remove from the tests the following problems
 # problems_to_exclude = [
-
+#     :quadrotor
 # ]
 # list_of_problems = setdiff(list_of_problems, problems_to_exclude)
 
diff --git a/test/utils.jl b/test/utils.jl
index 107e12a0..4355ae31 100644
--- a/test/utils.jl
+++ b/test/utils.jl
@@ -258,9 +258,7 @@ function comparison(; max_iter, test_name)
                             L2_jp = L2_norm(t_oc, xi_jp)
                             L2_bd = max(0.5*(L2_oc + L2_jp)*ε_rel_state, ε_abs_state)
                             res = @my_test_broken L2_di < L2_bd
-                            if f != :quadrotor
-                                keep_problem = keep_problem && (typeof(res) == Test.Pass)
-                            end
+                            keep_problem = keep_problem && (typeof(res) == Test.Pass)
 
                             DEBUG && println("├─  state $(x_vars[i])")
                             DEBUG && println("│")
@@ -293,9 +291,7 @@ function comparison(; max_iter, test_name)
                             L2_jp = L2_norm(t_oc, ui_jp)
                             L2_bd = max(0.5*(L2_oc + L2_jp)*ε_rel_control, ε_abs_control)
                             res = @my_test_broken L2_di < L2_bd
-                            if f != :quadrotor
-                                keep_problem = keep_problem && (typeof(res) == Test.Pass)
-                            end
+                            keep_problem = keep_problem && (typeof(res) == Test.Pass)
 
                             DEBUG && println("├─  control $(u_vars[i])")
                             DEBUG && println("│")
@@ -414,7 +410,7 @@ function comparison(; max_iter, test_name)
             end
 
             # JuMP
-            labelJP = (test_name == :solution) ? "JuMP: " * string(i_oc) * " it" : "JuMP"
+            labelJP = (test_name == :solution) ? "JuMP: " * string(i_jp) * " it" : "JuMP"
             for i in eachindex(x_vars) # state
                 xi_jp = [x_jp[k][i] for k in eachindex(t_jp)]
                 label = i == 1 ? labelJP : :none