1 INTRODUCTION
Adaptive stabilizing control design of nonlinear systems has been widely addressed in the recent literature ([1], [2], [3], [4], [5]). Up to now, one of the most popular approaches is the backstepping approach, which was first introduced in the celebrated work [2]. Compared with feedback linearization methods [3] which require precise models and may cancel some useful nonlinearities, such approach offers much more advantages and great freedom in control design, and hence allows one to address many other interesting issues, such as robustness and adaptiveness, rather than stabilization. Since the early 1990s, a series of research results on strict-feedback systems have been obtained in [2], [4], [6], [7], [8], [9], [10], [11], [12], [13] for deterministic systems and in [14], [15], [16], [17] for stochastic systems, incorporated with nonlinear damping ([6]), tuning functions ([4]), MT filters ([4]), neural networks ([12]), Nussbaum gain function ([10], [13]) and other methods.