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.
Position Control of Servomotors Using Neural Dynamic Sliding Mode
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Karami-Mollaee, A., Pariz, N., and Shanechi, H. M. (November 11, 2011). "Position Control of Servomotors Using Neural Dynamic Sliding Mode." ASME. J. Dyn. Sys., Meas., Control. November 2011; 133(6): 061014. https://doi.org/10.1115/1.4004782
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