Model predictive control design for multivariable processes in the presence of valve stiction

This paper presents different formulations of Model Predictive Control (MPC) to handle static friction in control valves for industrial processes. A fully unaware formulation, a stiction embedding structure, and a stiction inversion controller are considered. These controllers are applied to multivariable systems, with linear and nonlinear process dynamics. A semiphysical model is used for valve stiction dynamics and the corresponding inverse model is derived and used within the stiction inversion controller. The two-move stiction compensation method is revised and used as warm-start to build a feasible trajectory for the MPC optimal control problem. Some appropriate choices of objective functions and constraints are used with the aim of improving performance in set-points tracking. The different MPC formulations are reviewed, compared, and tested on several simulation examples. Stiction embedding MPC proves to guarantee good performance in set-points tracking and also stiction compensation, at the expense of a lower robustness with respect to other two formulations

Fault detection and root cause diagnosis using dynamic Bayesian network

This thesis presents two real time process fault detection and diagnosis (FDD) techniques incorporating process data and prior knowledge. Unlike supervised monitoring techniques, both these methods can perform without having any prior information of a fault. In the first part of this research, a hybrid methodology is developed combining principal component analysis (PCA), Bayesian network (BN) and multiple uncertain (likelihood) evidence to improve the diagnostic capacity of PCA and existing PCA-BN schemes with hard evidence based updating. A dynamic BN (DBN) based FDD methodology is proposed in the later part of this work which provides detection and accurate diagnosis by a single tool. Furthermore, fault propagation pathway is analyzed using the predictive feature of a BN and cause-effect relationships among the process variables. Proposed frameworks are successfully validated by applying to several process models

A data-based approach for multivariate model predictive control performance monitoring

An intelligent statistical approach is proposed for monitoring the performance of multivariate model predictive control (MPC) controller, which systematically integrates both the assessment and diagnosis procedures. Model predictive error is included into the monitored variable set and a 2-norm based covariance benchmark is presented. By comparing the data of a monitored operational period with the "golden" user-predefined one, this method can properly evaluate the performance of an MPC controller at the monitored operational stage. Characteristic direction information is mined from the operating data and the corresponding classes are built. The eigenvector angle is defined to describe the similarity between the current data set and the established classes, and an angle-based classifier is introduced to identify the root cause of MPC performance degradation when a poor performance is detected. The effectiveness of the proposed methodology is demonstrated in a case study of the Wood–Berry distillation column system

Digital flight control actuation system study

Flight control actuators and feedback sensors suitable for use in a redundant digital flight control system were examined. The most appropriate design approach for an advanced digital flight control actuation system for development and use in a fly-by-wire system was selected. The concept which was selected consisted of a PM torque motor direct drive. The selected system is compatible with concurrent and independent development efforts on the computer system and the control law mechanizations

Design and control of components-based integrated servo pneumatic drives

On-off traditional pneumatic drives are most widely used in industry offering low-cost, simple but flexible mechanical operation and relatively high power to weight ratio. For a period of decade from mid 1980's to 1990's, some initiatives were made to develop servo pneumatic drives for most sophisticated applications, employing purpose-designed control valves for pneumatic drives and low friction cylinders. However, it is found that the high cost and complex installation have discouraged the manufacturer from applying servo pneumatic drives to industrial usage, making them less favourable in comparison to their electric counterpart. This research aims to develop low-cost servo pneumatic drives which are capable of point-to-point positioning tasks, suitable for applications requiring intermediate performance characteristics. In achieving this objective, a strategy that involves the use of traditional on-off valve, simple control algorithm and distributed field-bus control networks has been adopted, namely, the design and control of Components-based Integrated Pneumatic Drives (CIPDs). Firstly, a new pneumatic actuator servo motion control strategy has been developed. With the new motion control strategy, the processes of positioning a payload can be achieved by opening the control valve only once. Hence, lowspeed on-off pneumatic control valves can be employed in keeping the cost low, a key attraction for employing pneumatic drives. The new servo motion control strategy also provides a way of controlling the load motion speed mechanically. Meanwhile, a new PD-based three-state closed-loop control algorithm also has been developed for the new control scheme. This control algorithm provides a way of adapting traditional PID (Proportional Integral Derivative) control theories for regulating pneumatic drives. Moreover, a deceleration control strategy has been developed so that both high-speed and accurate positioning control can be realised with low cost pneumatic drives. Secondly, the effects of system parameters on the transient response are studied. In assisting the analysis, a second order model is developed to encapsulate the velocity response characteristics of pneumatic drives to a step input signal. Stability analyses for both open loop and closed-loop control have also been carried out for the CIPDs with the newly developed motion control strategy. Thirdly, a distributed control strategy employing Lon Works has been devised and implemented, offering desirable attributes, high re-configurability, low cost and easy in installation and maintenance, etc to keep with the traditional strength for using pneumatic drives. By applying this technology, the CIPDs become standard components in "real" and "virtual" design environments. A remote service strategy for CIPDs using TCP/IP communication protocol has also been developed. Subsequently a range of experimental verifications has been carried out in the research. The experimental study of high-speed motion control indicates that the deceleration control strategy developed in the research can be an effective method in improving the behaviour of high speed CIPDs. The verification of open loop system behaviour of CIPDs shows that the model derived is largely indicative of the likely behaviour for the system considered, and the steady state velocity can be estimated using the Velocity Gain Kv. The evaluation made on a pneumatically driven pick-and-place machine has also confirmed that the system setup, including wiring, tuning, and system reconfiguration can be achieved in relative ease. This pilot study reveals the potential for employing CIPDs in building highly flexible cost effective manufacturing machines. It can thus be concluded that this research has developed successfully a new dimension and knowledge in both theoretical and practical terms in building low-cost servo pneumatic drives, which are capable of point-to-point positioning through employing traditional on-off pneumatic valves and actuators and through their integration with distributed control technology (LonWorks) by adopting a component-based design paradigm

Automation of garment assembly processes

Robotic automation in apparel manufacturing is reviewed and investigated. Gripper design for separation and de-stacking of batch cut fabric components is identified as an important factor in implementing such automation and a study of existing gripper mechanisms is presented. New de-stacking gripper designs and processes are described together with experimental results. Single fabric component handling, alignment and registration techniques are investigated. Some of these techniques are integrated within a demonstrator robotic garment assembly cell automating the common edge binding process. Performance results are reported

Deep Recurrent Neural Networks for Fault Detection and Classification

Deep Learning is one of the fastest growing research topics in process systems engineering due to the ability of deep learning models to represent and predict non-linear behavior in many applications. However, the application of these models in chemical engineering is still in its infancy. Thus, a key goal of this work is assessing the capabilities of deep-learning based models in a chemical engineering applications. The specific focus in the current work is detection and classification of faults in a large industrial plant involving several chemical unit operations. Towards this goal we compare the efficacy of a deep learning based algorithm to other state-of-the-art multivariate statistical based techniques for fault detection and classification. The comparison is conducted using simulated data from a chemical benchmark case study that has been often used to test fault detection algorithms, the Tennessee Eastman Process (TEP). A real time online scheme is proposed in the current work that enhances the detection and classifications of all the faults occurring in the simulation. This is accomplished by formulating a fault-detection model capable of describing the dynamic nonlinear relationships among the output variables and manipulated variables that can be measured in the Tennessee Eastman Process during the occurrence of faults or in the absence of them. In particular, we are focusing on specific faults that cannot be correctly detected and classified by traditional statistical methods nor by simpler Artificial Neural Networks (ANN). To increase the detectability of these faults, a deep Recurrent Neural Network (RNN) is programmed that uses dynamic information of the process along a pre-specified time horizon. In this research we first studied the effect of the number of samples feed into the RNN in order to capture more dynamical information of the faults and showed that accuracy increases with this number e.g. average classification rates were 79.8%, 80.3%, 81% and 84% for the RNN with 5, 15, 25 and 100 number of samples respectively. As well, to increase the classification accuracy of difficult to observe faults we developed a hierarchical structure where faults are grouped into subsets and classified with separate models for each subset. Also, to improve the classification for faults that resulted in responses with low signal to noise ratio excitation was added to the process through an implementation of a pseudo random signal(PRS). By applying the hierarchical structure there is an increment on the signal-to-noise ratio of faults 3 and 9, which translates in an improvement in the classification accuracy in both of these faults by 43.0% and 17.2% respectively for the case of 100 number of samples and by 8.7% and 23.4% for 25 number samples. On the other hand, applying a PRS to excite the system has showed a dramatic increase in the classification rates of the normal state to 88.7% and fault 15 up to 76.4%. Therefore, the proposed method is able to improve considerably both the detection and classification accuracy of several observable faults, as well as faults considered to be unobservable when using other detection algorithms. Overall, the comparison of the deep learning algorithms with Dynamic PCA (Principal Component Analysis) techniques showed a clear superiority of the deep learning techniques in classifying faults in nonlinear dynamic processes. Finally, we develop these same techniques to different operational modes of the TEP simulation, achieving comparable improvements to the classification accuracies

System modelling and control

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Identifying and Detecting Attacks in Industrial Control Systems

The integrity of industrial control systems (ICS) found in utilities, oil and natural gas pipelines, manufacturing plants and transportation is critical to national wellbeing and security. Such systems depend on hundreds of field devices to manage and monitor a physical process. Previously, these devices were specific to ICS but they are now being replaced by general purpose computing technologies and, increasingly, these are being augmented with Internet of Things (IoT) nodes. Whilst there are benefits to this approach in terms of cost and flexibility, it has attracted a wider community of adversaries. These include those with significant domain knowledge, such as those responsible for attacks on Iran’s Nuclear Facilities, a Steel Mill in Germany, and Ukraine’s power grid; however, non specialist attackers are becoming increasingly interested in the physical damage it is possible to cause. At the same time, the approach increases the number and range of vulnerabilities to which ICS are subject; regrettably, conventional techniques for analysing such a large attack space are inadequate, a cause of major national concern. In this thesis we introduce a generalisable approach based on evolutionary multiobjective algorithms to assist in identifying vulnerabilities in complex heterogeneous ICS systems. This is both challenging and an area that is currently lacking research. Our approach has been to review the security of currently deployed ICS systems, and then to make use of an internationally recognised ICS simulation testbed for experiments, assuming that the attacking community largely lack specific ICS knowledge. Using the simulator, we identified vulnerabilities in individual components and then made use of these to generate attacks. A defence against these attacks in the form of novel intrusion detection systems were developed, based on a range of machine learning models. Finally, this was further subject to attacks created using the evolutionary multiobjective algorithms, demonstrating, for the first time, the feasibility of creating sophisticated attacks against a well-protected adversary using automated mechanisms