论文标题
使用神经网络对不确定非线性过程的基于自适应收缩的控制
Adaptive Contraction-based Control of Uncertain Nonlinear Processes using Neural Networks
论文作者
论文摘要
在过程控制行业的灵活制造趋势以及化学过程模型的不确定性质的驱动下，本文旨在实现不确定的非线性系统（例如，使用具有参数性不确定性的过程模型）具有适应性的性能。所提出的自适应控制方法将基于自适应的神经网络嵌入基于收缩的控制器（以确保融合到时变参数）和在线参数识别模块与参考生成（确保建模参数收敛于物理系统的那些）。综合学习和控制方法涉及培训状态和参数依赖性神经网络，以学习通过不确定参数和差异反馈增益来参数的收缩度量。然后将该神经网络嵌入基于自适应收缩的控制法中，该法律通过在线参数估算更新。随着不确定的参数估计收敛到相应的物理值，可以达到无抵消的跟踪，同时可以提高收敛速率，从而导致一种灵活，有效且保守的方法，用于对不确定非线性过程的参考跟踪控制。包括一个说明性示例以证明整体方法。包括一个说明性示例以证明整体方法。
Driven by the flexible manufacturing trend in the process control industry and the uncertain nature of chemical process models, this article aims to achieve offset-free tracking for a family of uncertain nonlinear systems (e.g., using process models with parametric uncertainties) with adaptable performance. The proposed adaptive control approach incorporates into the control loop an adaptive neural network embedded contraction-based controller (to ensure convergence to time-varying references) and an online parameter identification module coupled with reference generation (to ensure modelled parameters converge those of the physical system). The integrated learning and control approach involves training a state and parameter dependent neural network to learn a contraction metric parameterized by the uncertain parameter and a differential feedback gain. This neural network is then embedded in an adaptive contraction-based control law which is updated by parameter estimates online. As uncertain parameter estimates converge to the corresponding physical values, offset-free tracking, simultaneously with improved convergence rates, can be achieved, resulting in a flexible, efficient and less conservative approach to the reference tracking control of uncertain nonlinear processes. An illustrative example is included to demonstrate the overall approach. An illustrative example is included to demonstrate the overall approach.
