An adaptive discretized RNN algorithm for posture collaboration motion control of constrained dual-arm robots - Zhang, Yichen; Han, Yu; Qiu, Binbin (Frontiers in Neurorobotics, 2024)
Although there are many studies on repetitive motion control of robots, few schemes and algorithms involve posture collaboration motion control of constrained dual-arm robots in threedimensional scenes, which can meet more complex work requirements. Therefore, this paper establishes the minimum displacement repetitive motion control scheme for the left and right robotic arms separately. On the basis of this, the design mentality of the proposed dual-arm posture collaboration motion control (DAPCMC) scheme, which is combined with a new joint-limit conversion strategy, is described, and the scheme is transformed into a time-variant equation system (TVES) problem form subsequently. To deal with the TVES problem, a novel adaptive Taylor-type discretized recurrent neural network (ATT-DRNN) algorithm is devised, which fundamentally remedies the problem that the calculation accuracy cannot be balanced well with the fast convergence speed. Then, stringent theoretical analysis confirms the dependability of the ATT-DRNN algorithm in terms of calculation precision and convergence rate. Ultimately, the effectiveness of the DAPCMC scheme and the excellent convergence competence of the ATT-DRNN algorithm are verified by a numerical simulation analysis and two control cases of dual-arm robots.