How to code up an objective function that has summations in it and then use it with fminunc?

I am trying to code up a Model Predictive Controller (MPC) using the fminunc function to give me values of u that would minimize

J = Sum[{N1:N1+10} (reference_state-predicted_state(N1:N1+10))^2] + Sum[{N1:N1+3} u(N1:N1+3)^2]

where N1 is current time, or for simplicity J= Sum(term1)^2 + Sum(term2)^2. So, basically, I need to find the vector of u's in term 2 based on the vector of predicted errors in term 1 that would minimize J. To begin with, I have considered a prediction horizon of 10 and a control horizon of 3.

My objective function looks like this

global setpoint

global x_opt

[n,m] = size(x_opt);

term1 = 0; term2 = 0;

for i = 1:n

    sum1 = (x_opt(i,1)-setpoint)^2;

    term1 = term1 + sum1;

    sum2 = u(i)^2;

    term2 = term2 + sum2

end

J = term1 + term2

here x_opt contains the predicted_state values for the length of the prediction horizon and setpoint is my reference_state which is a constant for all time. I am calling fminunc in the main function after computing all the predicted_state values for the length of the prediction horizon.

So, when I run this code, I get the following error

Attempted to access x(2); index out of bounds because numel(x)=1.

which leads me to believe that there is something wrong with my objective function. I have never used fminunc or fmincon with summations in the objective function and this is my first time coding up a MPC, therefore, any help/suggestions would be highly appreciated.