Robust adaptive neural network control for a class of uncertain MIMO nonlinear systems with input nonlinearities

  • Authors:

  • Mou Chen;Shuzhi Sam Ge;Bernard Voon Ee How

  • Affiliations:

  • College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China and Department of Electrical and Computer Engineering, National University of Singapore, Singa ...;Social Robotics Lab, Interactive Digital Media Institute and Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

  • Venue:
  • IEEE Transactions on Neural Networks
  • Year:
  • 2010

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Abstract

In this paper, robust adaptive neural network (NN) control is investigated for a general class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems with unknown control coefficient matrices and input nonlinearities. For nonsymmetric input nonlinearities of saturation and deadzone, variable structure control (VSC) in combination with backstepping and Lyapunov synthesis is proposed for adaptive NN control design with guaranteed stability. In the proposed adaptive NN control, the usual assumption on nonsingularity of NN approximation for unknown control coefficient matrices and boundary assumption between NN approximation error and control input have been eliminated. Command filters are presented to implement physical constraints on the virtual control laws, then the tedious analytic computations of time derivatives of virtual control laws are canceled. It is proved that the proposed robust backstepping control is able to guarantee semiglobal uniform ultimate boundedness of all signals in the closed-loop system. Finally, simulation results are presented to illustrate the effectiveness of the proposed adaptive NN control.