Abstract
A comparative study of various control systems using neural networks is done. The paper proposes to use a Recurrent Trainable Neural Network (RTNN) identifier with backpropagation method of learning. Two methods of adaptive neural control with integral plus state action are applied - an indirect and a direct trajectory tracking control. The first one is the indirect Sliding Mode Control (SMC) with I-term where the SMC is resolved using states and parameters identified by RTNN. The second one is the direct adaptive control with I-term where the adaptive control is resolved by a RTNN controller. The good tracking abilities of both methods are confirmed by simulation results obtained using a MIMO mechanical plant and a 1-DOF mechanical system with friction plant model. The results show that both control schemes could compensate constant offsets and that - without I- term did not.
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Baruch, I. (2007). Direct and Indirect Adaptive Neural Control of Nonlinear Systems. In: Castillo, O., Melin, P., Kacprzyk, J., Pedrycz, W. (eds) Hybrid Intelligent Systems. Studies in Fuzziness and Soft Computing, vol 208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37421-3_6
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DOI: https://doi.org/10.1007/978-3-540-37421-3_6
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