Abstract
This paper presents a constructive design of a continuous finite-time controller for a class of mechanical systems known as underactuated systems that satisfy the symmetry properties. An adaptive radial basis function neural network (RBFNN) finite-time control scheme is proposed to stabilize the underactuated system at a given equilibrium, regardless of the various uncertainties and disturbances that the system contains. First, a coordinate transformation is introduced to decouple the control input so that an n-th order underactuated system can be represented into a special cascade form. Next, an adaptive robust finite-time controller is derived from adding a power integrator technique and the RBFNN to approximate the nonlinear unknown dynamics in the new space, whose bounds are supposedly unknown. The stability and finite-time convergence of the closed-loop system are established by using Lyapunov theory. To show the effectiveness of the proposed method, simulations are carried out on the rotary inverted pendulum, a typical example of an underactuated mechanical system.
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28 December 2017
The list of authors in the original publication was incomplete. The complete list of authors is shown here, including the third author, Faiçal Mnif.
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A correction to this article is available online at https://doi.org/10.1007/s11071-017-4024-x.
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Ghommam, J., Chemori, A. Adaptive RBFNN finite-time control of normal forms for underactuated mechanical systems. Nonlinear Dyn 90, 301–315 (2017). https://doi.org/10.1007/s11071-017-3662-3
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DOI: https://doi.org/10.1007/s11071-017-3662-3