Recurrent-Neural-Network-Based Predictive Control of Piezo Actuators for Trajectory Tracking
Recurrent-Neural-Network-Based Predictive Control of Piezo Actuators for Trajectory Tracking
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DOI：
10.1109/tmech.2019.2946344
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发表时间：
2019-12-01
影响因子：
6.4
通讯作者：
Ren, Juan
中科院分区：
文献类型：
--
作者：
Xie, Shengwen;Ren, Juan
压电致动器的实时精确轨迹跟踪对高精度系统和应用至关重要。然而，由于PEA的非线性特性，无法在大带宽和大位移范围内精确建模，因此实时跟踪精度受到限制。在本文中，我们提出使用递归神经网络（RNN）对PEA系统建模，并开发用于PEA轨迹跟踪的非线性预测控制器。考虑到计算效率，首先训练RNN对PEA系统在高频范围内的非线性动力学进行建模。然后，提出了一个二阶线性模型来解释PEA的低频动态。因此，采用由RNN和线性模型组成的非线性模型对PEA动力学进行建模，并将其进一步用于位移的非线性预测控制。为了提高预测精度，设计了无气味卡尔曼滤波器来估计非线性模型的状态。基于梯度下降算法求解非线性预测控制问题，提出了一种解析计算代价函数梯度的方法。在纳米压电平台上进行了实验验证，并与PID控制器进行了性能比较。迭代学习控制方法的准确性也被用作比较的基准。结果表明，该方法可以实现高精度的实时轨迹跟踪。
Precise trajectory tracking of piezo actuators (PEAs) in real time is essential to high-precision systems and applications. However, the real-time tracking accuracy is rather limited as the PEA cannot be accurately modeled over large bandwidth and displacement range due to its nonlinearities. In this article, we propose to use recurrent-neural-network (RNN) to model the PEA system and develop a nonlinear predictive controller for PEA trajectory tracking. Considering the computation efficiency, first, an RNN is trained to model the nonlinear dynamics of the PEA system at high-frequency range. Then, a second-order linear model is proposed to account for the PEA low-frequency dynamics. Therefore, the PEA dynamics is modeled by the nonlinear model consisting of the RNN and the linear model, which is further used for nonlinear predictive control of the displacement. To increase the prediction accuracy, an unscented Kalman filter is designed to estimate the states of the nonlinear model. The nonlinear predictive control problem is solved based on a gradient descent algorithm, in which a method for analytically calculating the gradient of the cost function is developed. The proposed technique was experimentally implemented on a nano piezo stage for demonstration and its performance was compared with that of a PID controller. The accuracy of an iterative learning control approach was used as a benchmark for comparison as well. The results showed that high precision trajectory tracking of PEAs in real time can be achieved using the proposed technique.