Linear Constraints as Function in Fmincon

I want to find minimum of a function with constraints. My aim is to find Beta^b values.

Objective Function:

Constraints:

B^(a) in the constraint formula is given --> In the code it is written as betas_a

B^(b) in the formula is desired solution --> In the code it is written as betas_b

My Main function:

function [betas_b,fval,exitflag,output,lambda, CO_coeffs] = ConstrainedOptimization(series, betas_a, lag, Epsilon, p)

Calling fmincon in the function:

[betas_b,fval,exitflag,output,lambda] = fmincon(fun,x0,A,b,[],[],[],[],[],options);

My Objective Function:

function [f] = ObjectiveNormal (beta_b, matrix, vector)
f = sum((vector - matrix*beta_b).^2);  
end

I think b in fmincon should be:

b = Epsilon / p;

and I want to define A as a function:

function [c, ceq] = ConstraintNormal(betas_b, betas_a)    
    c(:,1) = betas_a -  betas_b;
    c(:,2) = betas_b  - betas_a;
end    

How can I define my Linear inequality constraints as a function like nonlcon?

Best regards