Unsupervised learning of control surfaces based on B-spline models

Based on our earlier work on construction of fuzzy controllers with B-spline models, we propose an automatic learning approach for generating control vertices of such a type of fuzzy controller. For supervised learning, we point out that rapid convergence of this learning procedure can be guaranteed, which is confirmed by diverse examples of approximating nonlinear functions and interpolating training data. For unsupervised learning, we employ a type of state evaluation functions which can be found for a large amount of control problems. Using such an evaluation function, a learning algorithm is devised which modifies the local control action efficiently to guide the system to the desired state. Implementations with the cart-pole balancing and a sensor-based mobile robot validate this learning approach