Motion control strategy for robotic arm using deep cascaded feature-enhancement Bayesian broad learning system with motion constraints

Intelligent control strategies can significantly enhance the efficiency of model parameter adjustment. However, existing intelligent motion control strategies for robotic arms based on the broad learning system lack sufficient accuracy and fail to account for the effects of joint motion limitations on overall control performance. To address the aforementioned challenges, this paper proposes a robotic arm motion control strategy based on a deep cascaded feature-enhanced Bayesian broad learning system with motion constraints (MC-DCBLS). Firstly, the motion control strategy based on a deep cascaded feature-enhanced Bayesian broad learning system (DCBBLS) is designed, which simplifies the modeling process and significantly improves control accuracy. Secondly, the motion constraint mechanism is introduced to optimize the control strategy to ensure that the robotic arm motion does not break through the physical limit. Finally, the parameter constraints of the control strategy network were obtained by introducing the Lyapunov theory to ensure the stability of the robotic arm motion control. The effectiveness of the proposed control strategy was validated through both simulations and physical experiments. The results demonstrated that the strategy significantly improved the accuracy of robotic arm motion control, with the root mean square error (RMSE) in position tracking reduced to 0.038 rad. This represents a 61.26% reduction in error compared to existing techniques.