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Stabilization of Interconnected Systems with Backlash Nonlinearity

  • Jing Zhou
  • Changyun Wen
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 372)

Contents

Due to the difficulty of handling both hysteresis and interactions between subsystems, there are limited results available on decentralized stabilization of unknown interconnected systems with hysteresis, even though the problem is practical and important. In this chapter, we provide solutions to this challenging problem by proposing two new schemes to design decentralized output feedback adaptive controllers using backstepping approach. For each subsystem, a general transfer function with arbitrary relative degree is considered. The interactions between subsystems are allowed to satisfy a nonlinear bound with certain structural conditions. In the first scheme, no knowledge is assumed on the bounds of unknown system parameters. In case that the uncertain parameters are inside known compact sets, we propose an alternative scheme where a projection operation is employed in the adaptive laws. In both schemes, the effect of the hysteresis and the effects due to interactions are taken into consideration in devising local control laws. It is shown that the designed local adaptive controllers can ensure all the signals in the closed loop system bounded. A root mean square type of bound is obtained for the system states as a function of design parameters. This implies that the transient system performance can be adjusted by choosing suitable design parameters. With Scheme II, the proposed control laws allow arbitrarily strong interactions provided their upper bounds are available. In the absence of hysteresis, perfect stabilization is ensured and the L2 norm of the system states is also shown to be bounded by a function of design parameters when the second scheme is applied.

Keywords

Uncertain Parameter Adaptive Controller Interconnected System Projection Operation Hysteresis Nonlinearity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jing Zhou
    • 1
  • Changyun Wen
    • 2
  1. 1.Norwegian University of Science and Technology, TrondheimNorway
  2. 2.Nanyang Technological UniversitySingapore

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