comments moved

Author: franckgaga

example/juMPC.jl

@@ -64,7 +64,7 @@ initstate!(ssKalmanFilter1,[0,0],[2,1])
 nx = linModel4.nx
 kf = KalmanFilter(linModel4, σP0=10*ones(nx), σQ=0.01*ones(nx), σR=[0.1, 0.1], σQ_int=0.05*ones(2), σP0_int=10*ones(2))
 
-mpc = LinMPC(kf, Hp=15, Hc=1, Mwt=[1, 1] , Nwt=[0.1, 0.1], Cwt=1e6)
+mpc = LinMPC(kf, Hp=15, Hc=1, Mwt=[1, 1] , Nwt=[0.1, 0.1], Cwt=1e5)
 
 setconstraint!(mpc, c_umin=[0,0], c_umax=[0,0])
 setconstraint!(mpc, c_ŷmin=[1,1], c_ŷmax=[1,1])

src/predictive_control.jl

@@ -577,9 +577,9 @@ function optim_objective!(mpc::LinMPC, b, q̃, p)
     J = objective_value(mpc.optim.model) + p # optimal objective value by adding constant p
     status = termination_status(mpc.optim.model)
     status ≠ OPTIMAL && @warn "MPC termination status not optimal ($status)"
-    # if error, we take last value :
     if any(status .== [INFEASIBLE, DUAL_INFEASIBLE, SLOW_PROGRESS, INVALID_MODEL, 
                        INVALID_OPTION, INTERRUPTED, OTHER_ERROR])
+        # if error, we take last value :
         ΔŨ = ΔŨ0
     end
     return ΔŨ, ϵ, J