Flexible manufacturing in the process industry requires control systems to achieve time-varying setpoints (e.g., product specifications) based on market demand. Contraction theory provides a useful framework for reference-independent system analysis and tracking control for nonlinear systems. However, determination of the control contraction metrics and control laws can be very difficult for general nonlinear systems. This work develops an approach to discrete-time contraction analysis and control using neural networks. The methodology involves training a neural network to learn a contraction metric and feedback gain. The resulting contraction-based controller embeds the trained neural network and is capable of achieving efficient tracking of time-varying references, with a full range of model uncertainty, without the need for controller structure redesign. This is a robust approach that can deal with bounded parametric uncertainties in the process model, which are commonly encountered in industrial (chemical) processes. Simulation examples are provided to illustrate the above approach.
翻译:在工序工业中,灵活制造要求根据市场需求建立控制系统,以实现时间变化的定点(如产品规格),合同理论为非线性系统的参考独立系统分析和跟踪控制提供了一个有用的框架,然而,对于一般的非线性系统来说,确定控制收缩指标和控制法可能非常困难。这项工作开发了使用神经网络进行离散时间收缩分析和控制的方法。方法包括培训神经网络学习收缩度和反馈收益。由此产生的收缩控制器嵌入了经过训练的神经网络,能够有效地跟踪时间变化参考,并有各种模型不确定性,而无需对控制器结构进行重新设计。这是一个强有力的方法,可以处理工艺模型中常见的、在工业(化学)过程中常见的、受约束的参数不确定性。提供了一些实例来说明上述方法。