A Mutation Co-evolution Clone Algorithm-Based Dynamic Recurrent Neural Network for Decoupling Control of Parallel Manipulator
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Parallel manipulator is very complicate nonlinear, Strong coupling system. In this work, a decoupling controller was presented for parallel manipulator based on an improved dynamic recurrent neural network (IDRNN). IDRNN was trained by a mutation co-evolution clone algorithm. Finally, the control performance of the proposed control approach is illustrated through the comparison studies with robot tracking control approach.
[1] Dervis Karaboga,et al. Training recurrent neural networks by using parallel tabu search algorithm based on crossover operation , 2004, Eng. Appl. Artif. Intell..
[2] Seul Jung,et al. A study of neural network control of robot manipulators , 1996, Robotica.
[3] Charles C. Nguyen,et al. Adaptive control of a stewart platform-based manipulator , 1993, J. Field Robotics.
[4] Dong Hwan Kim,et al. Robust tracking control design for a 6 DOF parallel manipulator , 2000 .