Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume -, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2023.3253801
Keywords
Manipulators; Robots; Planning; Field programmable gate arrays; Convergence; Recurrent neural networks; Artificial neural networks; Field programmable gate array (FPGA); fuzzy control; recurrent neural network (RNN); robot manipulator; time-varying problem
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A novel fuzzy recurrent neural network (FRNN) is proposed to resist internal error noises of robots, and it is designed and implemented on field-programmable gated array (FPGA).
When a robot completes end-effector tasks, internal error noises always exist. To resist internal error noises of robots, a novel fuzzy recurrent neural network (FRNN) is proposed, designed, and implemented on field-programmable gated array (FPGA). The implementation is pipeline-based, which guarantees the order of overall operations. The data processing is based on across-clock domain, which is beneficial for computing units' acceleration. Compared with traditional gradient-based neural networks (NNs) and zeroing neural networks (ZNNs), the proposed FRNN has faster convergence rate and higher correctness. Practical experiments on a 3 degree-of-freedom (DOs) planar robot manipulator show that the proposed fuzzy RNN coprocessor needs 496 lookup table random access memories (LUTRAMs), 205.5 block random access memories (BRAMs), 41 384 lookup tables (LUTs), and 16 743 flip-flops (FFs) of the Xilinx XCZU9EG chip.
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