A Spintronic Memristor-Based Neural Network With Radial Basis Function for Robotic Manipulator Control Implementation
提出一种基于自旋忆阻器的径向基函数神经网络控制算法，利用忆阻器记忆最优权重以避免启动波动，并通过李雅普诺夫方法保证系统稳定，仿真验证了其在双连杆机器人操作臂控制中的有效性。
A radial basis function (RBF) neural network control algorithm can effectively improve the robotic manipulators' performance against a large amount of uncertainty. The adaptive law can be derived by using the Lyapunov method so that the stability of robotic manipulator control system and the weight self-adaptive convergence of RBF neural networks will be guaranteed. Meanwhile, system fluctuations and even overshot phenomenon under every start-up process, which are caused by the system's convergence from the given nonoptimal initial weight value to the optimal weight value, can be avoided by using memristors to remember the optimal weight after the system's first operation. According to the above analysis, this correspondence paper designs a kind of RBF neural network control algorithm based on spintronic memristors, and then analyzes its theoretical derivation process and core design idea. Finally, the system simulation model, which uses a two-link robotic manipulator as control object, is built to prove the algorithm's validity and feasibility. Simulation results show that the proposed algorithm can satisfy the effect of presupposition.