Nonlinear MPC Controller Toolbox error

When using the Nonlinear MPC Controller toolbox, the internal MV port width is always reported error , but no errors were found after several checks. Please help me to look at it!

The code and error are as follows:

%% Lane Following Using Nonlinear Model Predictive Control
 
%% Overview of Simulink Model
% Open the Simulink model:
mdl = 'LaneFollowingNMPC';
open_system(mdl)
 
%% Parameters of Vehicle Dynamics and Road Curvature. 
% Specify the vehicle dynamics parameters
m = 1575;   % Mass of car
Iz = 2875;  % Moment of inertia about Z axis
lf = 1.2;   % Distance between Center of Gravity and Front axle 
lr = 1.6;   % Distance between Center of Gravity and Rear axle
Cf = 19000; % Cornering stiffness of the front tires (N/rad)
Cr = 33000; % Cornering stiffness of the rear tires (N/rad).

%%
% Set the initial and driver-set velocities.
v0 = 20;    % Initial velocity
v_set = 20; % Driver set velocity

%%
% Set the controller sample time.
Ts = 0.1;

%%
% Obtain the lane curvature information.
% seconds.
Duration = 15;                              % Simulation duration
t = 0:Ts:Duration;                          % Time vector
rho = LaneFollowingGetCurvature(v_set,t);   % Signal containing curvature information

%% 
nlobj = nlmpc(4,2,'MV',1,'MD',2);

%%
% Specify the controller sample time, prediction horizon, and control horizon. 
nlobj.Ts = Ts;
nlobj.PredictionHorizon = 10;
nlobj.ControlHorizon = 2;

%% 
% Specify the state function for the nonlinear plant model and its
% Jacobian.
nlobj.Model.StateFcn = @(x,u) LaneFollowingStateFcn(x,u);
nlobj.Jacobian.StateFcn = @(x,u) LaneFollowingStateJacFcn(x,u);

%% 
% Specify the output function for the nonlinear plant model and its
% Jacobian. The output variables are:
%
% * Lateral deviation
% * the yaw angle 
%
nlobj.Model.OutputFcn = @(x,u) [x(3);x(4)];
nlobj.Jacobian.OutputFcn = @(x,u) [0 0 1 0 ;0 0 0 1];

%% 
% Set the constraints for manipulated variables.
nlobj.MV(1).Min = -1.13;   % Minimum steering angle -65 
nlobj.MV(1).Max = 1.13;    % Maximum steering angle 65

%% 
nlobj.OV(1).ScaleFactor = 0.5;  % Range for lateral deviation
nlobj.OV(2).ScaleFactor = 0.5;  % Range for relative yaw angle
nlobj.MV(1).ScaleFactor = 2.26; % Range of  steering angle
nlobj.MD(1).ScaleFactor = 0.2;  % Range of Curvature

%% 
% Specify the weights in the standard MPC cost function. The third output,
% yaw angle, is allowed to float because there are only two manipulated
% variables to make it a square system. In this example, there is no
% steady-state error in the yaw angle as long as the second output, lateral
% deviation, reaches 0 at steady state.
nlobj.Weights.OutputVariables = [1 0];

%%
% Penalize acceleration change more for smooth driving experience.
nlobj.Weights.ManipulatedVariablesRate = 0.1;

%% 
% Validate prediction model functions at an arbitrary operating point using
% the |validateFcns| command. At this operating point:
%
% * |x0| contains the state values
% * |u0| contains the input values
% * |ref0| contains the output reference values
% * |md0| contains the measured disturbance value
%
x0 = [0.1 0.5 0.1 0.001];
u0 = 0.4;
ref0 = [0 0];
md0 = 0.1;
validateFcns(nlobj,x0,u0,md0,{},ref0);

%% Compare Controller Performance
% To compare the results of NLMPC and AMPC, simulate the model and
% save the logged data. 
%% 
% First, simulate the model using nonlinear MPC. 
controller_type = 1; % Set the variant for nonlinear MPC.
sim(mdl)
logsout1 = logsout;

%%
% Second, simulate the model using adaptive MPC.  
% controller_type = 2; % Set the variant for adaptive MPC. 
% sim(mdl)
logsout2 = logsout;

%%
% Plot and compare simulation results.
LaneFollowingCompareResults(logsout1,logsout2)
 
%%
% Copyright 2018 The MathWorks, Inc.