Robust dynamic sliding-mode control using adaptive RENN for magnetic levitation system
IEEE Transactions on Neural Networks
Adaptive neural control for strict-feedback nonlinear systems without backstepping
IEEE Transactions on Neural Networks
Projection-based adaptive neurocontrol with switching logic deadzone tuning
IEEE Transactions on Neural Networks
Adaptive fuzzy output-feedback controller for SISO affine nonlinear systems without state observer
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
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Zhang et al. presented an excellent neural-network (NN) controller for a class of nonlinear control designs. The singularity issue is completely avoided. Based on a modified Lyapunov function, their lemma illustrates the existence of an ideal control which is important in establishing the NN approximator. In this paper, we provide a Lyapunov function to realize an alternative ideal control which is more direct and simpler. The major contributions of this paper are divided into two parts. First, it proposes a control scheme which results in a smaller dimensionality of NN than that of Zhang et al. In this way, the proposed NN controller is easier to implement and more reliable for practical purposes. Second, by removing certain restrictions from the design reported by Zhang et al., we further develop a new NN controller, which can be applied to a wider class of systems.