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State-Feedback Adaptive Stabilizing Control Design for a Class of High-Order Nonlinear Systems with Unknown Control Coefficients | IEEE Conference Publication | IEEE Xplore

State-Feedback Adaptive Stabilizing Control Design for a Class of High-Order Nonlinear Systems with Unknown Control Coefficients


Abstract:

In this paper, a new approach is successfully addressed to design the state-feedback adaptive stabilizing control law for a class of high-order nonlinear systems in trian...Show More

Abstract:

In this paper, a new approach is successfully addressed to design the state-feedback adaptive stabilizing control law for a class of high-order nonlinear systems in triangular form and with unknown and nonidentical control coefficients, whose stabilizing control has been investigated recently under the knowledge that the lower bounds of the control coefficients are exactly known. In the present paper, without any knowledge of the lower bounds of the control coefficients, based on the adaptive technique and appropriately choosing design parameters, we give the recursive design procedure of the stabilizing control law by utilizing the approach of adding a power integrator together with tuning functions. The state-feedback adaptive control law designed not only preserves the equilibrium at the origin, but also guarantees the globally asymptotic stability of the closed-loop states and the uniform boundedness of all the other closed-loop signals.
Date of Conference: 07-11 August 2006
Date Added to IEEE Xplore: 15 January 2007
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ISSN Information:

Conference Location: Harbin, China

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.

References

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