Micropositioning systems are widely employed in industrial applications. Nonminimum-phase (NMP) is a normal phenomenon in micropositioning system, which leads to a great challenge for control system design. Model predictive control (MPC) is effective in handling the NMP problem. However, the parameter tuning of MPC is quite complicated and time-consuming using traditional methods for motion tracking control implementation. In this paper, an efficient neural networks (NN) model is established to optimize the MPC controller parameters including the prediction horizon, control horizon, and weighting factor. With the developed NN model, the motion tracking process of the micropositioning system is more intelligent and adaptive. The effectiveness of the presented novel NN-MPC control strategy has been verified by conducting extensive simulation studies. Furthermore, the results demonstrate that the NN-MPC scheme has good robustness under model parameter variation and noise condition.

中文翻译：

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基于神经网络的模型预测控制，用于微定位系统的精确运动跟踪

微定位系统广泛用于工业应用。非最小相位（NMP）是微定位系统中的正常现象，这给控制系统设计带来了巨大挑战。模型预测控制（MPC）可有效处理NMP问题。但是，使用传统方法进行运动跟踪控制时，MPC的参数调整非常复杂且耗时。本文建立了一个有效的神经网络（NN）模型，以优化MPC控制器参数，包括预测范围，控制范围和加权因子。利用已开发的NN模型，微定位系统的运动跟踪过程更加智能和自适应。通过进行广泛的仿真研究，已验证了所提出的新型NN-MPC控制策略的有效性。