Decentralized neural identification and control for uncertain nonlinear systems: Application to planar robot |
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| Authors: | Fernando Ornelas Tellez Alexander G Loukianov Eduardo Jose Bayro Corrochano |
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| Institution: | CINVESTAV, Unidad Guadalajara, Jalisco 45015, Mexico |
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| Abstract: | This paper presents a discrete-time decentralized neural identification and control for large-scale uncertain nonlinear systems, which is developed using recurrent high order neural networks (RHONN); the neural network learning algorithm uses an extended Kalman filter (EKF). The discrete-time control law proposed is based on block control and sliding mode techniques. The control algorithm is first simulated, and then implemented in real time for a two degree of freedom (DOF) planar robot. |
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| Keywords: | Neural networks Identification Decentralized systems EKF Sliding modes |
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