Sensors: Sensor Malfunctions in Process Control: Analysis, Design and Applications

摘要：Panagiotis Christofides和James F.Davis研究所：加州大学洛杉矶分校建议编号：0529295标题：过程控制中的传感器故障：分析、设计和应用智力价值。化学工业是美国经济的重要组成部分。面对越来越多的安全和盈利要求，化工过程操作越来越依赖于自动化控制系统，涉及大量的传感器。然而，对传感器的依赖往往会增加过程对传感器故障(例如，传感器故障、间歇性传感器数据丢失、测量偏差等)的脆弱性，导致控制系统故障，并可能导致一系列经济、环境和安全问题，这些问题可能会严重降低过程的运行效率。对传感器故障导致的异常情况的管理在化工行业是一项挑战，因为异常情况仅在美国就造成了100亿美元的年度收入损失。本项目的目的是通过明确处理控制系统设计和实施中的传感器数据丢失和故障，开发一个通用和实用的框架来处理化工过程反馈控制中的传感器故障。非线性和预测控制理论将被用来产生实际可实现的反馈控制系统，这些系统明确地解释传感器故障的发生，并在闭环系统中强制执行期望的稳定性、性能和鲁棒性指标。混合系统和控制理论随后将被用于：a)建模和分析传感器故障情况，以及b)构建新的监控方案，以确保本地控制系统在过程中的及时和协调响应，以实现故障恢复和最大限度地减少性能恶化。其动机是：a)化工过程操作中常见的传感器故障，b)由于过程非线性、模型不确定性和约束而导致的化工过程中丰富的复杂动力学，c)缺乏能够明确和同时处理复杂动力学、传感器数据丢失和传感器故障的非线性化工过程的实用控制策略，d)通信和计算技术的进步，以及e)继续需要改善化工过程操作，减少产品的变异性，提高能源效率和最大限度地减少环境和安全危害。具体研究内容包括：1.传感器数据丢失情况下的控制与估计系统的分析与设计，包括状态反馈控制问题和输出反馈控制问题。传感器完全故障下的集成容错控制与估计系统设计研究了传感器故障检测与辨识问题和传感器故障诱导控制重构问题。控制传感器出现故障的多个相互连接的单元。研究还将提供对传感器故障对过程控制造成的问题和限制的基本洞察，开发明确对传感器故障进行解释的实际可实施的控制算法，解决传感器故障检测和重构方法与工业决策支持技术的集成，并说明这些方法在化工过程中的应用。更广泛的影响。这些针对传感器故障的过程控制方法有望显著改善化工过程的操作和性能，提高过程的安全性和可靠性，并将故障对整个过程操作的负面经济影响降至最低。这项研究解决了反馈控制和估计系统的设计，明确地考虑了传感器故障的发生，并独特地集成了控制器设计、传感器故障检测和隔离以及决策支持技术，并为在实际实施中对这些之间存在的平衡提供了重要的见解。将这项研究整合到教育中将使教授过程控制和操作高级课程的教育工作者受益。开发软件、举办短期课程和讲习班，以及与工业联盟成员合作，将是将这项研究成果转化为工业部门的手段。

ABSTRACTPI: Panagiotis Christofides and James F. Davis Institution: University of California - Los AngelesProposal Number: 0529295Title: Sensor Malfunctions in Process Control: Analysis, Design and ApplicationsIntellectual merit. The chemical industry is a vital sector of the US economy. Increasingly faced with the requirements of safety and profitability, chemical process operation is relying extensively on automated control systems, involving a large number of sensors. The reliance on sensors, however, tends to increase vulnerability of the process to sensor malfunctions (e.g., sensor failure, intermittent sensor data losses, biased measurements, etc.,), leading to the failure of the control system and potentially causing a host of economic, environmental, and safety problems that can seriously degrade the operating efficiency of the process. Management of abnormal situations resulting from sensor malfunctions is a challenge in the chemical industry since abnormal situations account for $10 billion in annual lost revenue in the US alone. The objective of this project is to develop a general and practical framework for handling sensor malfunctions in feedback control of chemical processes by explicitly dealing with sensor data losses and failures in the control system design and implementation. Nonlinear and predictive control theory will be used to produce practically-implementable, feedback control systems that account explicitly for the occurrence of sensor faults and enforce the desired stability, performance and robustness specifications in the closed-loop system. Hybrid systems and control theory will subsequently be used to: a) model and analyze sensor failure situations, and b) construct novel supervisory control schemes that ensure the timely and coordinated response of the local control systems in the process, in a way that achieves fault recovery and minimizes performance deterioration. The motivation is provided by: a) the common occurrence of sensor malfunctions in chemical process operation, b) the abundance of complex dynamics in chemical processes due to process nonlinearities, model uncertainties and constraints, c) the lack of practical control strategies for nonlinear chemical processes that can deal explicitly and simultaneously with complex dynamics, sensor data losses and sensor failures, d) advances in communication and computation technologies, and e) the continuing need to improve chemical process operation, reduce product variability, improve energy efficiency and minimize environmental and safety hazards. Specifically, the research will focus on:1. Analysis and design of control and estimation systems subject to sensor data losses; both the state and output feedback control problems will be studied.2. Design of integrated fault-tolerant control and estimation systems subject to complete sensor failures; both the sensor fault-detection and identification problem and the problem of sensor fault-induced control reconfiguration will be studied.3. Control of multiple interconnected units subject to sensor malfunctions.4. Applications to chemical processes where control is critical in achieving the desired stability and performance objectives.The research will also provide fundamental insight into the problems and limitations that sensor malfunctions cause on process control, develop practically-implementable control algorithms accounting explicitly for sensor malfunctions, address the integration of sensor fault-detection and reconfiguration methods with industrial decision support technologies, and illustrate the application of these methods to chemical processes. Broader impact. These control methods for processes subject to sensor malfunctions are expected to significantly improve the operation and performance of chemical processes, increase process safety and reliability, and minimize the negative economic impact of failures on overall process operation. This research addresses the design of feedback control and estimation systems accounting explicitly for the occurrence of sensor faults and uniquely integrates controller design, sensor fault-detection and isolation, and decision support technologies and provides the potential for significant insight on the balance that can exist between these in practical implementation. The integration of the research into education would benefit educators teaching advanced-level classes in process control and operations. The development of software, short courses and workshops, and the collaboration with the members of an industrial consortium will be the means for transferring the results of this research into the industrial sector.