A Hierarchical Support Vector Machine based Trajectory Tracking Methodology for Modeling Robot Manipulators Inverse Dynamics

摘要

A novel approach is presented for modeling continuous multivariable functions using a three-stage hierarchical neural network model involving Support Vector Machines (SVM) and an adaptive unsupervised Neural Network. It involves an adaptive Kohonen feature map (SOFM) in the first stage which aims at clustering the input variable space into smaller sub-spaces representative of the input space probability distribution and preserving its original topology, while rapidly increasing, on the other hand, cluster distances. During convergence phase of the map a group of Support Vector Machines, associated with its codebook vectors, is simultaneously trained in an online fashion so that each SVM learns to positively respond when the input data belong to the topological sub-space represented by its corresponding codebook vector. Moreover, it learns to negatively respond to input data not belonging to such a previously mentioned corresponding topological subspace. The proposed methodology is applied, with promising results, to the design of a neural-adaptive trajectory tracking controller, by involving the computer-torque approach, which combines the proposed three-stage neural network model with a classical servo PD feedback controller. The results achieved by the suggested hierarchical SVM approach are favorably compared to the ones obtained by traditional (PD) and non-hierarchical neural network based controllers.