I am working on a quadrotor 3dof ,and I have a problem with block nmpc can you help me?

Zero weights are applied to one or more OVs because there are fewer MVs than OVs.

Slack variable unused or zero-weighted in your custom cost function.

All constraints will be hard.

Model.StateFcn is OK.

Jacobian.StateFcn is OK.

Warning: Jacobian matrix with respect to "x" has maximum error = 3.9315 occurring at "(4,4)".

> In ctrlMsgUtils.warning (line 25)

In nlmpc/validateFcns>compareJac (line 378)

In nlmpc/validateFcns (line 175)

In Quadrotor6dof (line 60)

Warning: Jacobian matrix with respect to "umv" has maximum error = 0.45637 occurring at "(4,1)".

> In ctrlMsgUtils.warning (line 25)

In nlmpc/validateFcns>compareJac (line 378)

In nlmpc/validateFcns (line 176)

In Quadrotor6dof (line 60)

No output function specified. Assuming "y = x" in the prediction model.

Optimization.CustomCostFcn is OK.

Analysis of user-provided model, cost, and constraint functions complete.

Error using Quadrotor6dof (line 67)

Expected "ref" to be an array with number of columns equal to 6.

Error in nmpcblock_interface.m (line 165)

throw(ME)

Error in Quadrotor6dof.m (line 67)

Error in 's_hover_nmpc/Nonlinear MPC Controller/MPC/NLMPC' (line 24)

[Ibrahim]

nx      = 6;
ny      = 6;
nu      = 4;
nlobj   = nlmpc(nx, ny, nu);

%nlobj = nlmpc(6,6,'MV',4,'MD',1,'UD',4)

nlobj.Model.StateFcn    = "QuadrotorStateFcn";
nlobj.Jacobian.StateFcn = @QuadrotorStateJacobianFcn;

% nlobj.Model.OutputFcn = 'QuadrotorOutputFcn';
% nlobj.Model.OutputFcn = @(x,u,Ts) [x(1); x(3);x(5)];

rng(0)

% u=[1 1 1 1];
% validateFcns(nlobj,rand(nx,1),rand(nu,1));

Ts  = 25;
p   = 18;
m   = 18;

% nlobj.Ts = Ts;
% nlobj.PredictionHorizon = p;
% nlobj.ControlHorizon = m;
% nlobj.MV = struct('Min',{0;0;0;0},'Max',{6;6;6;6});
% nlobj.MV = struct( ...
%     Min={0;0;0;0}, ...
%     Max={6;6;6;6}, ...
%     RateMin={-2;-2;-2;-2}, ...
%     RateMax={2;2;2;2} ...
%     );
% nlobj.Model.NumberOfParameters = 1;
% nlobj.Weights.OutputVariables = [1 1 1 0 0 0];
% nlobj.Weights.ManipulatedVariables = [0.1 0.1 0.1 0.1];
% nlobj.Weights.ManipulatedVariablesRate = [0.1 0.1 0.1 0.1];
%%%%%%%%%%

nlobj.Ts                = Ts;
nlobj.PredictionHorizon = p;
nlobj.ControlHorizon    = m;
nlobj.MV                = struct('Min', {0; 0; 0; 0}, 'Max', {6; 6; 6; 6});
nlobj.MV                = struct( ...
                        Min     = { 0;  0;  0;  0}, ...
                        Max     = { 6;  6;  6;  6}, ...
                        RateMin = {-2; -2; -2; -2}, ...
                        RateMax = { 2;  2;  2;  2} ...
                        );

%nlobj.Model.NumberOfParameters = 1;

nlobj.Weights.OutputVariables           = [0.1 0.1 0.1 0 0 0];
nlobj.Weights.ManipulatedVariables      = [0.1 0.1 0.1 0.1];
nlobj.Weights.ManipulatedVariablesRate  = [0.1 0.1 0.1 0.1];

% Specify the initial conditions
x                                       = [0;0;0;0;0;0];
nlobj.Optimization.CustomCostFcn        = @(X, U, e, data) Ts*sum(sum(U(1:p, :)));
nlobj.Optimization.ReplaceStandardCost  = true;

% Nominal control target (average to keep quadrotor floating)
nloptions           = nlmpcmoveopt;
nloptions.MVTarget  = [4.9 4.9 4.9 4.9]; 
mv                  = nloptions.MVTarget;

% mv = nlmpcmove(nlobj,x,lastmv,ref);

lastmv  = mv;
md      = 0;

% ref=[1 0 0; 0 1 0; 0 0 1; 0 0 0; 0 0 0; 0 0 0];
% nlmpcmoveopt.options.Parameters={Ts x};

ref             = zeros(1, nlobj.ny);
[mv, ~, Info]   = nlmpcmove(nlobj, x, lastmv,ref);
validateFcns(nlobj, rand(nx, 1), rand(nu, 1));