Adaptive non-singular terminal sliding mode control with high-gain observers for robotic manipulators

Predictive current control (PCC) is an advanced control strategy for permanent magnet synchronous motors (PMSM). When the motor drive system is undisturbed, predictive current control exhibits a good dynamic response speed and steady−state performance, but the conventional PCC control performance of PMSM that depends on the motor body model is vulnerable to parameter perturbation. Aiming at this problem, an improved model−free predictive current control (IMFPCC) strategy based on a high−gain disturbance observer (HGDO) is proposed in this paper. The proposed strategy is introduced with the idea of model−free control, relying only on the system input and output to build an ultra−local current prediction model, which gets rid of the constraints of the motor body parameters. In the paper, the ultra−local structure is optimized by comparing and analyzing the equation of the state of the classical ultra−local structure and PMSM system. The system’s current state variables are incorporated into the ultra−local system modeling, as a result, the current estimation errors existing in the classical ultra−local structure are eliminated. For the unmodeled and parametric perturbation part of the ultra−local system, a high−gain disturbance observer is designed to estimate it in real time. Finally, the proposed IMFPCC strategy is compared with the conventional model−based predictive current control (MPCC) and the conventional model−free predictive current control (CMFPCC) in simulation and experiment. The results show that the current steady−state error of the IMFPCC strategy in the case of parameter variation is only 50% of the MPCC method, which proves the effectiveness and correctness of the proposed strategy.

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Adaptive non-singular terminal sliding mode control with high-gain observers for robotic manipulators

Cited by 2 publications

References 15 publications

A Novel Adaptive Sliding Mode Control of Robot Manipulator Based on RBF Neural Network and Exponential Convergence Observer

A Novel Adaptive Sliding Mode Control of Robot Manipulator Based on RBF Neural Network and Exponential Convergence Observer

An Improved Model−Free Current Predictive Control of Permanent Magnet Synchronous Motor Based on High−Gain Disturbance Observer

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