Predictive feedback control using a multiple model approach

A new method of designing predictive controllers for SISO systems is presented. The controller selects the model used in the design of the control law from a given set of models according to a switching rule based on output prediction errors. The goal is to design, at each sample instant, a feedback control law that ensures robust stability of the closed–loop system and gives better performance for the current operating point. The overall multiple model predictive control scheme quickly identifies the closest linear model to the dynamics of the current operating point, and carries out an automatic reconfiguration of the control system to achieve a better performance. The results are illustrated with simulations of a continuous stirred tank reactor

Integration of process design and control: A review

There is a large variety of methods in literature for process design and control, which can be classified into two main categories. The methods in the first category have a sequential approach in which, the control system is designed, only after the details of process design are decided. However, when process design is fixed, there is little room left for improving the control performance. Recognizing the interactions between process design and control, the methods in the second category integrate some control aspects into process design. With the aim of providing an exploration map and identifying the potential areas of further contributions, this paper presents a thematic review of the methods for integration of process design and control. The evolution paths of these methods are described and the advantages and disadvantages of each method are explained. The paper concludes with suggestions for future research activities

Data-driven adaptive model-based predictive control with application in wastewater systems

This study is concerned with the development of a new data-driven adaptive model-based predictive controller (MBPC) with input constraints. The proposed methods employ subspace identification technique and a singular value decomposition (SVD)-based optimisation strategy to formulate the control algorithm and incorporate the input constraints. Both direct adaptive model-based predictive controller (DAMBPC) and indirect adaptive model-based predictive controller (IAMBPC) are considered. In DAMBPC, the direct identification of controller parameters is desired to reduce the design effort and computational load while the IAMBPC involves a two-stage process of model identification and controller design. The former method only requires a single QR decomposition for obtaining the controller parameters and uses a receding horizon approach to process input/output data for the identification. A suboptimal SVD-based optimisation technique is proposed to incorporate the input constraints. The proposed techniques are implemented and tested on a fourth order non-linear model of a wastewater system. Simulation results are presented to compare the direct and indirect adaptive methods and to demonstrate the performance of the proposed algorithms

Application of data mining techniques of batch profiles for process understanding and improvement

Batch processes are widely used in the chemical industry. Recently, much attention has been given to the monitoring and analysis of batch measurement data, or profiles, with an emphasis on the detection of problems. Similarly, methods to improve the final product quality in batch processes have multiplied in the literature. However, an area that is virtually unexplored is the utilization of the data mining techniques for monitoring and analysis of batch profiles for better understanding batch processes, rather than identifying problems in batches, in order to improve the process. The thrust of this work is to apply a systematic method to increase batch process understanding by sifting through the existing historical database of past batches, to discern directions for process improvement from the increased understanding, and to subsequently demonstrate better quality control through the use of online recipe adjustments. A database of past batches is generated from a simulated nylon-6, 6 process, with the main quality variable of interest being the number average molecular weight. The time and measurement variability in raw batch measurement profiles is characterized through scale parameters. These scale parameters are subjected to a standard principal component analysis (PCA) to understand the principal sources of variation present in a historical database of past batches. Directions for process improvement are discovered from the data mining study and appropriate manipulated variables to implement recipe adjustments are identified. Online predictions of the molecular weight are demonstrated which indicate off-target quality batches well before the end of the batch. A split-range linear molecular weight-based controller is developed that is able to reduce the variability in the quality around the target. Further process improvement is accomplished by reducing the cycle time in addition to tightly controlling the final quality. The approach for systematically analyzing batch process data is general and can be applied to any batch system, including non-reactive systems

Advanced Model Predictive Control Solutions for Performance Enhancement of Food Service Appliances

This work is done in collaboration with Prof. Felice Andrea Pellegrino and Prof. Gianfranco Fenu of the University of Trieste, my colleague Ph.D. Francesco Forte and my place of work in the AD\&T Laboratory at Electrolux Professional Group. The purpose of this research is the design of a control system with the aim of improving the performance of professional appliances dedicated to the food processing in order to meet the objectives of energy saving and culinary quality. Furthermore, it is necessary to design real-time control software that is able to predict the behavior of the device, estimate non-measurable physical quantities, respect the constraints on energy consumption imposed a priori, reduce the effect of delay response with the aim of having smarter and more robust solutions. Therefore, we apply the model predictive control (MPC) strategy in an industrial setting, specifically for controlling the temperature of Oven Professional Appliances. The workflow includes identifying and validating a model of the cell temperature and incorporating disturbance models. MPC is implemented using a state-space formulation. The proposed method shows significant energy saving and error tracking reduction with respect to the current oven control; its effectiveness has been demonstrated through several tests carried out on a professional oven.This work is done in collaboration with Prof. Felice Andrea Pellegrino and Prof. Gianfranco Fenu of the University of Trieste, my colleague Ph.D. Francesco Forte and my place of work in the AD\&T Laboratory at Electrolux Professional Group. The purpose of this research is the design of a control system with the aim of improving the performance of professional appliances dedicated to the food processing in order to meet the objectives of energy saving and culinary quality. Furthermore, it is necessary to design real-time control software that is able to predict the behavior of the device, estimate non-measurable physical quantities, respect the constraints on energy consumption imposed a priori, reduce the effect of delay response with the aim of having smarter and more robust solutions. Therefore, we apply the model predictive control (MPC) strategy in an industrial setting, specifically for controlling the temperature of Oven Professional Appliances. The workflow includes identifying and validating a model of the cell temperature and incorporating disturbance models. MPC is implemented using a state-space formulation. The proposed method shows significant energy saving and error tracking reduction with respect to the current oven control; its effectiveness has been demonstrated through several tests carried out on a professional oven

Energy efficiency improvement through MPC-based peripherals management for an industrial process test-bench

High energy costs evince the growing need for energy efficiency in industrial companies. This paper presents a solution at the industrial machine level to obtain efficient energy consumption. Therefore, a controller inspired by the well-known model predictive control (MPC) strategy was developed for the management of peripheral devices. The validation of the control requires a test-bench to emulate the energy consumption of a manufacturing machine. The test-bench has four devices, two used to emulate the periodic and fixed energy consumption of the manufacturing process and two as peripherals, subject to rules associated with the process. Consequently, a subspace identification (SI) was employed to identify energy models to simulate the behavior of the device. As a final step, a performance comparison between a rule-based control (RBC) and the proposed predictive-like controller revealed the remarkable energy savings. The MPC results show an energy saving of around 3% with respect to RBC as well as an instant maximum energy consumption reduction of 8%, approximately.Peer ReviewedPostprint (published version

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

Advanced control systems for fast orbit feedback of synchrotron electron beams

Diamond Light Source is the UK’s national synchrotron facility that produces synchrotron radiation for research. At source points of synchrotron radiation, the electron beam stability relative to the beam size is critical for the optimal performance of synchrotrons. The current requirement at Diamond is that variations in the beam position should not exceed 10% of the beam size for frequencies up to 140Hz. This is guaranteed by the fast orbit feedback that actuates hundreds of corrector magnets at a sampling rate of 10kHz to reduce beam vibrations down to sub-micron levels. For the next-generation upgrade, Diamond-II, the beam stability requirements will be raised to 3% up to 1kHz. Consequently, the sampling rate will be increased to 100kHz and an additional array of fast correctors will be introduced, which precludes the use of the existing controller. This thesis develops two different control approaches to accommodate the additional array of fast correctors at Diamond-II: internal model control based on the generalised singular value decomposition (GSVD) and model predictive control (MPC). In contrast to existing controllers, the proposed approaches treat the control problem as a whole and consider both arrays simultaneously. To achieve the sampling rate of 100kHz, this thesis proposes to reduce the computational complexity of the controllers in several ways, such as by exploiting symmetries of the magnetic lattice. To validate the controllers for Diamond-II, a real-time control system is implemented on high-performance hardware and integrated in the existing synchrotron. As a first-of-its-kind application to electron beam stabilisation in synchrotrons, this thesis presents real-world results from both MPC and GSVD-based controllers, demonstrating that the proposed approaches meet theoretical expectations with respect to performance and robustness in practice. The results from this thesis, and in particular the novel GSVD-based method, were successfully adopted for the Diamond-II upgrade. This may enable the use of more advanced control systems in similar large-scale and high-speed applications in the future