A novel excitation controller using support vector machines and approximate models

Key words:  Support vector machines  Nonlinear control  Approximate model  Neural networks  Identification

摘要:

Abstract:

This paper proposes a novel excitation controller using support vector machines (SVM) and approximate models. The nonlinear control law is derived directly based on an input-output approximation method via Taylor expansion, which not only avoids complex control development and intensive computation, but also avoids online learning or adjustment. Only a general SVM modelling technique is involved in both model identification and controller implementation. The robustness of the stability is rigorously established using the Lyapunov method. Several simulations demonstrate the effectiveness of the proposed excitation controller.