A Q-modification neuroadaptive control architecture for discrete-time systems
IEEE Transactions on Neural Networks
Dynamic structure neural network for stable adaptive control of nonlinear systems
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Adaptive control based on IF-THEN rules for grasping force regulation with unknown contact mechanism
Robotics and Computer-Integrated Manufacturing
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A new feedback-linearization-based neural network (NN) adaptive control is proposed for unknown nonaffine nonlinear discrete-time systems. An equivalent model in afflne-like form is first derived for the original nonaffine discrete-time systems as feedback linearization methods cannot be implemented for such systems. Then, feedback linearization adaptive control is implemented based on the affine-like equivalent model identified with neural networks. Pretraining is not required and the weights of the neural networks used in adaptive control are directly updated online based on the input-output measurement. The dead-zone technique is used to remove the requirement of persistence excitation during the adaptation. With the proposed neural network adaptive control, stability and performance of the closed-loop system are rigorously established. Illustrated examples are provided to validate the theoretical findings.