This note presents a modification of the integrated friction model structure proposed by Swevers et al. (2000), called the Leuven model. The Leuven model structure allows accurate modeling both in the presliding and the sliding regimes without the use of a switching function. The model incorporates a hysteresis function with nonlocal memory and arbitrary transition curves. This note presents two modifications of the Leuven model. A first modification overcomes a recently detected shortcoming of the original Leuven model: a discontinuity in the friction force which occurs during certain transitions in presliding. A second modification, using the general Maxwell slip model to implement the hysteresis force, eliminates the problem of stack overflow, which can occur with the implementation of the hysteresis force.

Technical Note: modification of the Leuven integrated friction model structure

This paper concerns high-accuracy tracking control for hydraulic actuators with nonlinear friction compensation Typically, LuGre model-based friction compensation has been widely employed in sundry industrial servomechanisms However, due to the piecewise continuous property, it is difficult to be integrated with backstepping design, which needs the time derivation of the employed friction model Hence, nonlinear model-based hydraulic control rarely sets foot in friction compensation with nondifferentiable friction models, such as LuGre model, Stribeck effects, although they can give excellent friction description and prediction In this paper, a novel continuously differentiable nonlinear friction model is first derived by modifying the traditional piecewise continuous LuGre model, then an adaptive backstepping controller is proposed for precise tracking control of hydraulic systems to handle parametric uncertainties along with nonlinear friction compensation In the formulated nonlinear hydraulic system model, friction parameters, servovalve null shift, and orifice-type internal leakage are all uniformly considered in the proposed controller The controller theoretically guarantees asymptotic tracking performance in the presence of parametric uncertainties, and the robustness against unconsidered dynamics, as well as external disturbances, is also ensured via Lyapunov analysis The effectiveness of the proposed controller is demonstrated via comparative experimental results

Adaptive Control of Hydraulic Actuators With LuGre Model-Based Friction Compensation

Quality and productivity methods for evaluating quality parameter design methods for specifying tolerance quality management for production processes

Taguchi on robust technology development : bringing quality engineering upstream

© 2014 The Science and Information (SAI) Organization.In this paper we present a intrusion detection module capable of detecting malicious network traffic in a SCADA (Supervisory Control and Data Acquisition) system. Malicious data in a SCADA system disrupt its correct functioning and tamper with its normal operation. OCSVM (One-Class Support Vector Machine) is an intrusion detection mechanism that does not need any labeled data for training or any information about the kind of anomaly is expecting for the detection process. This feature makes it ideal for processing SCADA environment data and automate SCADA performance monitoring. The OCSVM module developed is trained by network traces off line and detect anomalies in the system real time. The module is part of an IDS (Intrusion Detection System) system developed under CockpitCI project and communicates with the other parts of the system by the exchange of IDMEF (Intrusion Detection Message Exchange Format) messages that carry information about the source of the incident, the time and a classification of the alarm.

/pdf/intrusion-detection-in-scada-systems-using-machine-learning-257fqp777o.pdf

Intrusion detection in SCADA systems using machine learning techniques

The three previous industrial revolutions profoundly transformed agriculture industry from indigenous farming to mechanized farming and recent precision agriculture. Industrial farming paradigm greatly improves productivity, but a number of challenges have gradually emerged, which have exacerbated in recent years. Industry 4.0 is expected to reshape the agriculture industry once again and promote the fourth agricultural revolution. In this paper, first, we review the current status of industrial agriculture along with lessons learned from industrialized agricultural production patterns, industrialized agricultural production processes, and the industrialized agri-food supply chain. Furthermore, five emerging technologies, namely, the Internet of Things, robotics, Artificial Intelligence, big data analytics, and blockchain, toward Agriculture 4.0 are discussed. Specifically, we focus on the key applications of these emerging technologies in the agricultural sector and corresponding research challenges. This paper aims to open up new research opportunities for readers, particularly industrial practitioners.

IEEE transactions on industrial informatics

The least-squares support vector machine (LS-SVM) has been successfully used to model nonlinear time dynamics; however, it does not have the capability to handle space information and is, therefore, unable to model the complex nonlinear distributed parameter systems (DPS). Here, we propose a spatiotemporal LS-SVM modeling method for complex nonlinear DPS. The space kernel function is developed to describe the nonlinear correlation between space locations. The time Lagrange multiplier represents the time dynamics. The integration of the space kernel function and the time Lagrange multiplier can reconstruct the nonlinear spatiotemporal dynamics of the DPS. The spatiotemporal LS-SVM method accounts for the time dynamics and the space distribution nature of the DPS, enabling it to adapt well to real-time spatiotemporal variation. The successful application of this spatiotemporal LS-SVM method to a practical curing thermal process and its comparison with several common DPS modeling methods demonstrate its superiority in the modeling of the unknown nonlinear distributed parameter process.

A Novel Spatiotemporal LS-SVM Method for Complex Distributed Parameter Systems With Applications to Curing Thermal Process

Improvement of aluminum lithium alloy adhesion performance based on sandblasting techniques

In this paper, a novel system-decomposition-based multilevel control method is proposed to control the complex hydraulic press machine system. The key idea in this proposed method is to decompose the system complexity into a group of simple subsystems, and the control task is shared by a group of simple subcontrollers. First, the complex nonlinear system is decomposed into a group of simple subsystems according to the process knowledge, upon which every subsystem is easily controlled by a simple subcontroller. Then, a sequence control strategy is developed to help these subcontrollers to handle the coupling between subsystems. Finally, the proposed method is applied to control a practical hydraulic press machine and compared with the traditional proportional-integral-derivative control.

System-Decomposition-Based Multilevel Control for Hydraulic Press Machine

Many industrial processes are complex nonlinear distributed parameter systems (DPSs) with time-varying spatiotemporal dynamics. However, time-varying spatiotemporal dynamics and the nonlinear relationships between spatial points are currently not given much consideration in the existing data-driven modeling methods. Thus, accurately modeling a nonlinear DPS with time-varying spatiotemporal dynamics using these current methods is challenging. Here, we propose a spatiotemporal extreme learning machine (ELM) to accurately model time-varying and nonlinear DPSs. First, we develop the nonlinear spatial activation function to describe the nonlinear relationships between spatial points. As a result, in contrast to the traditional ELM method which is only used to model the temporal dynamics, the spatiotemporal ELM inherently takes the spatial information into consideration. Next, an online time coefficient model is developed, which accounts for the time-varying temporal dynamics of the DPS. After the integration of the spatial activation function with the time coefficient model, this modeling method is able to adapt to real-time spatiotemporal variation. Unlike the existing data-driven DPS modeling approaches, the proposed method has the capability to accurately represent the nonlinear relationships between spatial points and has the adaptive ability for modeling time-varying dynamics. Finally, through application on practical curing experiments, the proposed method can improve the modeling precision for an unknown, time-varying, and nonlinear DPS due to smaller modeling error as compared to the several commonly used DPS modeling methods.

Online Spatiotemporal Extreme Learning Machine for Complex Time-Varying Distributed Parameter Systems

An effective model of batch forging processes is crucial to ensure the quality conformance control of batch productions. However, obtaining this model has proven difficult due to a variety of the raw forgings produced by manufacturing error, material variation, geometric defects, etc. In this paper, a novel online probabilistic extreme learning machine (ELM) is proposed to model batch forging processes. A probabilistic ELM is first developed to extract the distribution information of the batch forging processes from the data. Due to the highly linear structure of the ELM, the stochastic property of the forging process is easily derived and processed. By using the characteristics of the online ELM, a strategy is then developed to update the distribution model as new forging process data are collected. Finally, case studies on complex batch forging processes demonstrate the effectiveness of the proposed online probabilistic ELM.

Minghui Huang

Papers

A Novel Spatiotemporal LS-SVM Method for Complex Distributed Parameter Systems With Applications to Curing Thermal Process

Improvement of aluminum lithium alloy adhesion performance based on sandblasting techniques

System-Decomposition-Based Multilevel Control for Hydraulic Press Machine

Online Spatiotemporal Extreme Learning Machine for Complex Time-Varying Distributed Parameter Systems

Online Probabilistic Extreme Learning Machine for Distribution Modeling of Complex Batch Forging Processes