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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.

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There exists strong coupling and a complex multivariable relation among the strip gauge, looper angle and strip tension in tandem hot rolling. To improve the gauge precision of the hot strip, the dynamic relationship among these three variables must be determined. In this paper, a new coordinated optimal control scheme based on discrete model predictive control (DMPC) theory was presented for an automatic gauge control and looper control (AGC-LP) integrated system. First, a state-space model of the AGC-LP integrated system was established based on a 1700 mm tandem hot strip mill. Then, to achieve the desired dynamic performance with the AGC-LP integrated system, a performance index function and an optimal control sequence were designed for the DMPC controller. The simulation results showed that the proposed DMPC controller had better robustness and adaptability to modeling uncertainties and external disturbances than a traditional proportional-integral (PI) controller.