In this paper, position control of servomotors is addressed. A radial basis function neural network is employed to identify the unknown nonlinear function of the plant model, and then a robust adaptive law is developed to train the parameters of the neural network, which does not require any preliminary off-line weight learning. Moreover, base on the identified model, we propose a new dynamic sliding mode control (DSMC) for a general class of nonaffine nonlinear systems by defining a new adaptive proportional-integral sliding surface and employing a linear state feedback. The main property of proposed controller is that it does not need an upper bound for the uncertainty and identified model; moreover, the switching gain increases and decreases according to the system circumstance by employing an adaptive procedure. Then, chattering is removed completely by using the DSMC with a small switching gain.
Issue Section:
Research Papers
Keywords:
adaptive control,
learning (artificial intelligence),
machine control,
neurocontrollers,
nonlinear control systems,
PI control,
position control,
radial basis function networks,
robust control,
servomotors,
state feedback,
variable structure systems,
servomotors,
sliding mode control,
chattering,
adaptive control,
neural networks
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.Copyright © 2011
by American Society of Mechanical Engineers
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