The machining robotic manipulators (MRMs) find a broad range of applications due to their high efficiency, wide range of processing, and strong flexibility. High tracking accuracy and strong anti-interference ability are required to trajectory tracking control of machining robot during processing. In view of the above characteristics, this paper proposes a compensation sliding mode control (CSMC) based on nonlinear disturbance observer (NBO-CSMC) for MRM. First, we deduced the dynamic model of MRM considering the uncertainties and disturbances. Then, for improving the trajectory tracking accuracy, a compensation sliding mode controller is designed based on the traditional sliding mode control (TSMC) strategy. Finally, in order to reduce the chattering in sliding mode control, the NBO-CSMC is designed for MRM, NBO is used to estimate the external composite uncertain interference existing in the system, and compensate the system control input in real time. The Lyapunov’s theory proved the stability of the proposed algorithm, and simulation experiments verified the effectiveness of the proposed control strategy.

Milling force prediction is one of the most important ways to improve the quality of products and stability in robot stone machining. In this paper, support vector machines (SVMs) are introduced to model the milling force of white marble, and the model parameters in the SVMs are optimized by the improved quantum-behaved particle swarm optimization (IQPSO) algorithm. A set of online inspection data from stone-machining robotic manipulators is adopted to train and test the model. The overall performance of the model is evaluated based on the decision coefficient (R2), mean absolute percentage error (MAPE) and root mean square error (RMSE), and the results obtained by IQPSO-SVM are superior to those of the PSO-SVM model. On this basis, the relationship between the milling force of white marble and various machining parameters is explored to obtain optimal machining parameters. The proposed model provides a tool for the adjustment of machining parameters to ensure stable machining quality. This approach is a new method and concept for milling force control and optimization research in the robotic stone milling process.