Thanks for developing such a great tool.
I’m trying to control the position of the pendulum on a cart system maintaining “-pi/2<theta<pi/2”. I’m trying to do a similar implementation to “https://www.sciencedirect.com/science/article/pii/S2405896318327204”. As the paper comments, the approach consists of setting the cost function in terms of only the control signal and imposing a terminal condition to reach the desired state.
In this sense, I should solve the NLP with:
-nlp_solver = ‘sqp’;
-nlp_solver_exact_hessian = ‘true’;
-regularize_method = ‘convexify’;
-qp_solver = ‘partial_condensing_hpipm’;
-and imposing the terminal constraint as a quadratic constraint.
How can I implement this quadratic constraint in acados?
Is there any example that I can follow?
If the cost function is only in terms of the control signal, the weight matrix in Mayer term should be W_e=zeros(nx,nx)?
Is there any disadvantage of only using the square of the control signal as a cost function in terms of the Hessian Matrix computation and convergence of the algorithm(eg. it may not get any solution or will have a slow convergence)?
Thanks in advance for your help.