Decentralized Neural Control: Application to Robotics by Ramon Garcia-Hernandez, Michel Lopez-Franco, Edgar N.

By Ramon Garcia-Hernandez, Michel Lopez-Franco, Edgar N. Sanchez, Alma y. Alanis, Jose A. Ruz-Hernandez

This publication offers a decentralized procedure for the id and keep watch over of robotics structures. It additionally offers fresh learn in decentralized neural regulate and comprises purposes to robotics. Decentralized regulate is unfastened from problems because of complexity in layout, debugging, info collecting and garage standards, making it most advantageous for interconnected structures. in addition, rather than the centralized technique, it may be carried out with parallel processors.

This procedure bargains with 4 decentralized keep watch over schemes, that are in a position to establish the robotic dynamics. the educational of every neural community is played online utilizing a longer Kalman filter out (EKF).

The first oblique decentralized regulate scheme applies the discrete-time block keep an eye on procedure, to formulate a nonlinear sliding manifold.

The moment direct decentralized neural regulate scheme relies at the backstepping procedure, approximated through a excessive order neural network.

The 3rd regulate scheme applies a decentralized neural inverse optimum regulate for stabilization.

The fourth decentralized neural inverse optimum keep an eye on is designed for trajectory tracking.

This finished paintings on decentralized regulate of robotic manipulators and cellular robots is meant for professors, scholars and execs eager to comprehend and follow complex wisdom of their box of labor.

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6). 10) and optimal value function Ji (χi,k ) = Vi (χ0 ). This lemma is adapted from [10] for each isolated subsystem, which allows to establish the following theorem. 2) with i = 1, . . , γ ; γ the number of subsystems. 6). 13) i=0 = γ i=0 [Vi (χ0 )]. γ Proof First, we analyze stability. 4) is satisfied. 14) 1 P (χi,k )(Ri (χi,k ) + Pi,2 (χi,k ))−1 Pi,1 (χi,k ) . 4 i,1 γ γ Selecting Pi such that i=0 [V i ] ≤ 0, stability of i=0 [χi,k ] = 0 where χi,k = 0 is guaranteed. 14). 11). 15), if i=0 [V i ] γ γ = i=0 [Vi (χi,k+1 ) − Vi (χi,k ) + αi (χi,k )Ri (χi,k )αi (χi,k )] ≤ − i=0 [ζi,Q χi,k 2 ], γ γ γ then i=0 [ Vi ] = i=0 [Vi (χi,k+1 ) − Vi (χi,k )] ≤ i=0 [−ζi,Q χi,k 2 ].

IEEE Trans. Syst. Man Cybern. Part B Cybern. 34(4), 1630–1645 (2004) 6. : Introduction to Random Signals and Applied Kalman Filtering. Wiley, New York (1992) 7. : Identification and control of dynamical systems using neural networks. J. Frankl. Inst. 335(5), 827–839 (1998) 8. : Nonlinear Systems. , New Jersey (1996) 9. : Optimal Control Theory: An Introduction. , New Jersey (2004) 10. : Dual extended Kalman filtering in recurrent neural networks. Neural Netw. 16(2), 223–239 (2003) 11. : Optimal Control.

122(1), 1–10 (2000) 17. : Sliding Mode Control in Electromechanical Systems. 1 Decentralized Systems in Block Strict Feedback Form The model of many practical nonlinear systems can be expressed in (or transformed into) a special state form named the block strict feedback form (BSFF) [4] as follows 1 2 1 = f i1 xi1 + gi1 xi1 xi,k + di,k xi,k+1 2 3 2 xi,k+1 = f i2 xi1 , xi2 + gi2 xi1 , xi2 xi,k + di,k .. j j j j j j+1 j xi,k+1 = f i xi1 , xi2 , . . , xi + gi xi1 , xi2 , . . , xi xi,k + di,k ..

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