solve warnings generated by Documenter.jl

Author: franckgaga

docs/src/manual/linmpc.md

@@ -146,11 +146,14 @@ function plot_data(t_data, u_data, y_data, ry_data)
     plot!(t_data, ry_data[2,:],label="setpoint", linestyle=:dash, linetype=:steppost)
     p3 = plot(t_data,u_data[1,:],label="cold", linetype=:steppost); ylabel!("flow rate")
     plot!(t_data,u_data[2,:],label="hot", linetype=:steppost); xlabel!("time (s)")
-    return plot(p1, p2, p3, layout=(3,1), fmt=:svg)
+    return plot(p1, p2, p3, layout=(3,1))
 end
 plot_data(t_data, u_data, y_data, ry_data)
+savefig(ans, "plot1_LinMPC.svg"); nothing # hide
 ```
 
+![plot1_LinMPC](plot1_LinMPC.svg)
+
 For some situations, when [`LinMPC`](@ref) matrices are small/medium and dense, [`DAQP`](https://darnstrom.github.io/daqp/)
 optimizer may be more efficient. To install it, run:
 
@@ -178,8 +181,11 @@ u, y = model.uop, model()
 initstate!(mpc2, u, y)
 u_data, y_data, ry_data = test_mpc(mpc2, model)
 plot_data(t_data, u_data, y_data, ry_data)
+savefig(ans, "plot2_LinMPC.svg"); nothing # hide
 ```
 
+![plot2_LinMPC](plot2_LinMPC.svg)
+
 ## Adding Feedforward Compensation
 
 Suppose that the load disturbance ``u_l`` of the last section is in fact caused by a
@@ -251,4 +257,7 @@ u, y, d = model.uop, model(), mpc_ff.estim.model.dop
 initstate!(mpc_ff, u, y, d)
 u_data, y_data, ry_data = test_mpc_ff(mpc_ff, model)
 plot_data(t_data, u_data, y_data, ry_data)
+savefig(ans, "plot3_LinMPC.svg"); nothing # hide
 ```
+
+![plot3_LinMPC](plot3_LinMPC.svg)

docs/src/manual/nonlinmpc.md

@@ -56,8 +56,11 @@ to first simulate `model` using [`sim!`](@ref) as a quick sanity check:
 using Plots
 u = [0.5]
 plot(sim!(model, 60, u), plotu=false)
+savefig(ans, "plot1_NonLinMPC.svg"); nothing # hide
 ```
 
+![plot1_NonLinMPC](plot1_NonLinMPC.svg)
+
 ## Nonlinear Model Predictive Controller
 
 An [`UnscentedKalmanFilter`](@ref) estimates the plant state :
@@ -76,8 +79,11 @@ f_plant(x, u, _) = x + Ts*pendulum(par_plant, x, u)
 plant = NonLinModel(f_plant, h, Ts, nu, nx, ny)
 res = sim!(estim, 60, [0.5], plant=plant, y_noise=[0.5])
 plot(res, plotu=false, plotxwithx̂=true)
+savefig(ans, "plot2_NonLinMPC.svg"); nothing # hide
 ```
 
+![plot2_NonLinMPC](plot2_NonLinMPC.svg)
+
 The estimate ``x̂_3`` is the integrator state that compensates for static errors (`nint_ym`
 and `σQint_ym` parameters of [`UnscentedKalmanFilter`](@ref)). The Kalman filter performance
 seems sufficient for control. As the motor torque is limited to -1.5 to 1.5 N m, we
@@ -94,13 +100,19 @@ position):
 ```@example 1
 res = sim!(mpc, 60, [180.0], plant=plant, x0=zeros(plant.nx), x̂0=zeros(mpc.estim.nx̂))
 plot(res)
+savefig(ans, "plot3_NonLinMPC.svg"); nothing # hide
 ```
 
+![plot3_NonLinMPC](plot3_NonLinMPC.svg)
+
 The controller seems robust enough to variations on ``K`` coefficient. Starting from this
 inverted position, the closed-loop response to a step disturbances of 10° is also
 satisfactory:
 
 ```@example 1
 res = sim!(mpc, 60, [180.0], plant=plant, x0=[π, 0], x̂0=[π, 0, 0], y_step=[10])
 plot(res)
+savefig(ans, "plot4_NonLinMPC.svg"); nothing # hide
 ```
+
+![plot4_NonLinMPC](plot4_NonLinMPC.svg)