LAUSR

Due to the uncertain dynamic characteristics, the requirements for robotic manipulator control are increasingly complex. The traditional radial basis function (RBF) neural network has a good generalization ability, but its redundant and tedious training process cannot meet the “Intelligent” control requirement of robotic manipulator. This study designs a new valve-regulated memristive RBF neural network, which adopts the model block approximation control strategy to estimate the three coefficient matrices of the robotic manipulator and uses the memristor with voltage threshold (MVT) as an electronic synapse to provide connections between neurons for the neural network and store information. This study adopts the design idea of software hardening and replaces the updated neural network weight with the change of the memristance value in the MVT network (crossed array), which can effectively improve the control performance of the traditional RBF neural network and can also provide analytical data for the fault detection of the subsequent control system. A simulation analysis is conducted with a single-joint robotic manipulator as the control object, and the results verify the rationality and feasibility of the proposed control algorithm.