Runge-Kutta Type Discrete Circadian RNN for Resolving Tri-Criteria Optimization Scheme of Noises Perturbed Redundant Robot Manipulators

Authors：Zhang, Zhijun^*; Deng, Xianzhi; He, Mingzhen; Chen, Tao; Liang, Junjie

Source：IEEE Transactions on Systems Man Cybernetics-Systems, 2022, 52(3): 1405-1416.

DOI：10.1109/TSMC.2020.3020145

• Mathematical model
• Manipulators
• Neural networks
• Acceleration
• Planning
• Optimization
• Motion planning
• neural networks
• periodic noises
• redundant robot manipulators
• Runge-Kuttad numerical differential method

In order to resist periodic interfere in robot hardware or environment, a Runge-Kutta type discrete-time circadian rhythms neural network (RK-DCRNN) model is proposed, and investigated to plan the motion of redundant robot manipulators. To achieve the optimal control, a quadratic programming-based acceleration-level hybrid tri-criteria (ALHT) scheme is first designed, which simultaneously minimize the acceleration norm, torque norm, and joint-angle shift-free indices. Second, according to the neural dynamic design method, a continuous-time circadian rhythms neural network model is exploited, and then based on the Runge-Kutta numerical differential method, a discrete-time circadian rhythms neural network model is obtained. Third, the convergence of the proposed RK-DCRNN model is proved by detailed mathematical derivation. Fourth, comparative simulations and physical experiments verify that the proposed RK-DCRNN model can suppress the accumulation of position error in the motion planning of manipulators.