Min Ji , Hai sheng Deng , Weiming Zhang , Hasan Rastgoo
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引用次数: 0
Abstract
The Mixed Logical Dynamical (MLD) model framework is used in this paper to develop a novel algorithm for state estimation and fault diagnosis in hybrid systems. These systems, with both continuous and discrete dynamics, present challenges for accurate state estimation and timely fault detection. The proposed method integrates the constrained Kalman filter, MLD modeling, and mixed integer programming for robust state monitoring and fault diagnosis. It leverages the MLD model to represent system dynamics while handling discrete and continuous states, offering a flexible framework for hybrid system analysis. The constrained Kalman filter estimates the system state in real time, ensuring the estimation stays within constraints that reflect physical or operational limits. This enhances robustness, especially in noisy environments. Mixed integer programming efficiently manages discrete events and logical decisions, capturing the hybrid system's nature. The technique, called the Hybrid Kalman Filter (HKF), combines Kalman filtering with MLD models to detect and isolate sensor faults. A bank of HKFs monitors specific sensors or subsystems for precise fault isolation. When a fault occurs, the corresponding HKF detects it, providing critical information about its location and nature. The proposed method is tested on hybrid systems, both simulated and real-world, demonstrating its effectiveness in estimating system states and detecting sensor faults, even in complex environments. The results show its potential to improve hybrid system reliability and performance in industries such as automotive, aerospace, and industrial automation.
期刊介绍:
This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. In addition to the traditional chemical processing and manufacturing applications, the scope of process control problems involves a wide range of applications that includes energy processes, nano-technology, systems biology, bio-medical engineering, pharmaceutical processing technology, energy storage and conversion, smart grid, and data analytics among others.
Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at the application and the development of process control techniques.
Topics covered include:
• Control applications• Process monitoring• Plant-wide control• Process control systems• Control techniques and algorithms• Process modelling and simulation• Design methods
Advanced design methods exclude well established and widely studied traditional design techniques such as PID tuning and its many variants. Applications in fields such as control of automotive engines, machinery and robotics are not deemed suitable unless a clear motivation for the relevance to process control is provided.