This paper presents a design scheme of least squares support vector machines (LS-SVM) based direct adaptive
??∞ output feedback control for a class of nonlinear uncertain systems whose state variables are not available. Firstly, a
class of error observer is designed to estimate the system states indirectly. Then the adaptive outout feedback controller is
constructed by using LS-SVM, and an on-line learning rule for the weigh vector and bias is derived. ??H∞ control is used to
attentuate the effect on the tracking error caused by LS-SVM approximation errors and external disturbance. Lyapunov theory
is used to prove the stability of the close-loop system. Finally, numerical simulations show the feasibility and effectiveness
of the proposed method.