A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems

This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version

Instrumentation and control of anaerobic digestion processes: a review and some research challenges

The final publication is available at Springer via http://dx.doi.org/10.1007/s11157-015-9382-6[EN] To enhance energy production from methane or resource recovery from digestate, anaerobic digestion processes require advanced instrumentation and control tools. Over the years, research on these topics has evolved and followed the main fields of application of anaerobic digestion processes: from municipal sewage sludge to liquid mainly industrial then municipal organic fraction of solid waste and agricultural residues. Time constants of the processes have also changed with respect to the treated waste from minutes or hours to weeks or months. Since fast closed loop control is needed for short time constant processes, human operator is now included in the loop when taking decisions to optimize anaerobic digestion plants dealing with complex solid waste over a long retention time. Control objectives have also moved from the regulation of key variables measured online to the prediction of overall process perfor- mance based on global off-line measurements to optimize the feeding of the processes. Additionally, the need for more accurate prediction of methane production and organic matter biodegradation has impacted the complexity of instrumentation and should include a more detailed characterization of the waste (e.g., biochemical fractions like proteins, lipids and carbohydrates)andtheirbioaccessibility andbiodegradability characteristics. However, even if in the literature several methodologies have been developed to determine biodegradability based on organic matter characterization, only a few papers deal with bioaccessibility assessment. In this review, we emphasize the high potential of some promising techniques, such as spectral analysis, and we discuss issues that could appear in the near future concerning control of AD processes.The authors acknowledge the financial support of INRA (the French National Institute for Agricultural Research), the French National Research Agency (ANR) for the "Phycover" project (project ANR-14-CE04-0011) and ADEME for Inter-laboratory assay financial support.Jimenez, J.; Latrille, E.; Harmand, J.; Robles Martínez, Á.; Ferrer Polo, J.; Gaida, D.; Wolf, C.... (2015). Instrumentation and control of anaerobic digestion processes: a review and some research challenges. Reviews in Environmental Science and Biotechnology. 14(4):615-648. doi:10.1007/s11157-015-9382-6S615648144Aceves-Lara CA, Latrille E, Steyer JP (2010) Optimal control of hydrogen production in a continuous anaerobic fermentation bioreactor. 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Diagnostic, reconstruction et identification des défauts capteurs et actionneurs (application aux station d'épurations des eaux usées)

Ce travail de thèse propose une méthode générale de reconstruction de défauts. Cette méthode donne un aperçu sur le problème d observabilité des entrées inconnues. Par la suite, une méthodologie de détection et d isolation de défauts capteurs et actionneurs est proposée. Le schéma de FDI est basé sur une banque d observateurs. L implémentation de cette méthode pour un modèle ASM1 réduit conduit à une table de signature fortement localisante.La deuxième partie porte sur la problématique de l observation des systèmes non linéaires . Le filtre de Kalman étendu (FKE) est l un des observateurs les plus largement utilisé à cette fin. Cependant, la convergence de cet observateur n est pas prouvée. Lorsque le FKE est appliqué à un système mis sous une forme canonique d observabilité, il acquiert, des propriétés de convergence exponentielle globales. Cependant, ce dernier entraine une amplification de bruit. Afin de combiner l efficacité d un FKE en termes de lissage de bruit, et la réactivité d un OKE grand gain face aux larges variations, [Boizot et al., 2010] ont proposé un observateur adaptatif. Ainsi, cet observateur est appliqué au système non-linéaire MIMO d une station d épuration biologique. Une étude comparative entre ces trois observateurs est menée afin de mettre en évidence la pertinence de l observateur adaptatif.This thesis proposes a general methodology for identifying and reconstructing sensor faults on dynamical processes. This identification theory provides a general framework for the problem of "observability with unknown inputs". Next, a framework for fault detection and isolation of sensors and actuators is proposed. The FDI sheme is based on bank of high-gain observers. A simulation study of a waste water treatment plant shows the effectiveness of the proposed approach.The second point evoked in the thesis is the observability of nonlinear dynamic systems and state estimation. The Extended Kalman Filter (EKF) is a widely used observer for such nonlinear systems. However, it suffers from the lack of theoretical justifications. The EKF, when applied to a system put in a normal form of observability, it acquires the property of global exponential convergence. Unfortunately, this latter observer (HG-EKF) is very sensitive to measurement noise. In order to combine the behaviors of the EKF (efficiency with respect to noise smoothing) and of the HG-EKF (reactivity to large estimation errors), (Boizot et al, 2010) proposed an adaptive high gain observer. This observer is applied to a MIMO nonlinear system of an Activated Sludge Process. A comparison study of the performances of the three observers under consideration is carried out. Results show a clearly better state estimation for the adaptive observer.TOULON-Bibliotheque electronique (830629901) / SudocSudocFranceF

Development of monitoring and control systems for biotechnological processes

The field of biotechnology represents an important research area that has gained increasing success in recent times. Characterized by the involvement of biological organisms in manufacturing processes, its areas of application are broad and include the pharmaceuticals, agri-food, energy, and even waste treatment. The implication of living microorganisms represents the common element in all bioprocesses. Cell cultivations is undoubtedly the key step that requires maintaining environmental conditions in precise and defined ranges, having a significant impact on the process yield and thus on the desired product quality. The apparatus in which this process occurs is the bioreactor. Unfortunately, monitoring and controlling these processes can be a challenging task because of the complexity of the cell growth phenomenon and the limited number of variables can be monitored in real-time. The thesis presented here focuses on the monitoring and control of biotechnological processes, more specifically in the production of bioethanol by fermentation of sugars using yeasts. The study conducted addresses several issues related to the monitoring and control of the bioreactor, in which the fermentation takes place. First, the topic concerning the lack of proper sensors capable of providing online measurements of key variables (biomass, substrate, product) is investigated. For this purpose, nonlinear estimation techniques are analyzed to reconstruct unmeasurable states. In particular, the geometric observer approach is applied to select the best estimation structure and then a comparison with the extended Kalman filter is reported. Both estimators proposed demonstrate good estimation capabilities as input model parameters vary. Guaranteeing the achievement of the desired ethanol composition is the main goal of bioreactor control. To this end, different control strategies, evaluated for three different scenarios, are analzyed. The results show that the MIMO system, together with an estimator for ethanol composition, ensure the compliance with product quality. After analyzing these difficulties through numeric simulations, this research work shifts to testing a specific biotechnological process such as manufacturing bioethanol from brewery’s spent grain (BSG) as renewable waste biomass. Both acid pre-treatment, which is necessary to release sugars, and fermentation are optimized. Results show that a glucose yield of 18.12 per 100 g of dried biomass is obtained when the pre-treatment step is performed under optimized conditions (0.37 M H2SO4, 10% S-L ratio). Regarding the fermentation, T=25°C, pH=4.5, and inoculum volume equal to 12.25% v/v are selected as the best condition, at which an ethanol yield of 82.67% evaluated with respect to theoretical one is obtained. As a final step, the use of Raman spectroscopy combined with chemometric techniques such as Partial Least Square (PLS) analysis is evaluated to develop an online sensor for fermentation process monitoring. The results show that the biomass type involved significantly affects the acquired spectra, making them noisy and difficult to interpret. This represents a nontrivial limitation of the applied methodology, for which more experimental data and more robust statistical techniques could be helpful

A Systematic Review of the State of Cyber-Security in Water Systems

Critical infrastructure systems are evolving from isolated bespoke systems to those that use general-purpose computing hosts, IoT sensors, edge computing, wireless networks and artificial intelligence. Although this move improves sensing and control capacity and gives better integration with business requirements, it also increases the scope for attack from malicious entities that intend to conduct industrial espionage and sabotage against these systems. In this paper, we review the state of the cyber-security research that is focused on improving the security of the water supply and wastewater collection and treatment systems that form part of the critical national infrastructure. We cover the publication statistics of the research in this area, the aspects of security being addressed, and future work required to achieve better cyber-security for water systems

Bibliographical review on cyber attacks from a control oriented perspective

This paper presents a bibliographical review of definitions, classifications and applications concerning cyber attacks in networked control systems (NCSs) and cyber-physical systems (CPSs). This review tackles the topic from a control-oriented perspective, which is complementary to information or communication ones. After motivating the importance of developing new methods for attack detection and secure control, this review presents security objectives, attack modeling, and a characterization of considered attacks and threats presenting the detection mechanisms and remedial actions. In order to show the properties of each attack, as well as to provide some deeper insight into possible defense mechanisms, examples available in the literature are discussed. Finally, open research issues and paths are presented.Peer ReviewedPostprint (author's final draft

Events Recognition System for Water Treatment Works

The supply of drinking water in sufficient quantity and required quality is a challenging task for water companies. Tackling this task successfully depends largely on ensuring a continuous high quality level of water treatment at Water Treatment Works (WTW). Therefore, processes at WTWs are highly automated and controlled. A reliable and rapid detection of faulty sensor data and failure events at WTWs processes is of prime importance for its efficient and effective operation. Therefore, the vast majority of WTWs operated in the UK make use of event detection systems that automatically generate alarms after the detection of abnormal behaviour on observed signals to ensure an early detection of WTW’s process failures. Event detection systems usually deployed at WTWs apply thresholds to the monitored signals for the recognition of WTW’s faulty processes. The research work described in this thesis investigates new methods for near real-time event detection at WTWs by the implementation of statistical process control and machine learning techniques applied for an automated near real-time recognition of failure events at WTWs processes. The resulting novel Hybrid CUSUM Event Recognition System (HC-ERS) makes use of new online sensor data validation and pre-processing techniques and utilises two distinct detection methodologies: first for fault detection on individual signals and second for the recognition of faulty processes and events at WTWs. The fault detection methodology automatically detects abnormal behaviour of observed water quality parameters in near real-time using the data of the corresponding sensors that is online validated and pre-processed. The methodology utilises CUSUM control charts to predict the presence of faults by tracking the variation of each signal individually to identify abnormal shifts in its mean. The basic CUSUM methodology was refined by investigating optimised interdependent parameters for each signal individually. The combined predictions of CUSUM fault detection on individual signals serves the basis for application of the second event detection methodology. The second event detection methodology automatically identifies faults at WTW’s processes respectively failure events at WTWs in near real-time, utilising the faults detected by CUSUM fault detection on individual signals beforehand. The method applies Random Forest classifiers to predict the presence of an event at WTW’s processes. All methods have been developed to be generic and generalising well across different drinking water treatment processes at WTWs. HC-ERS has proved to be effective in the detection of failure events at WTWs demonstrated by the application on real data of water quality signals with historical events from a UK’s WTWs. The methodology achieved a peak F1 value of 0.84 and generates 0.3 false alarms per week. These results demonstrate the ability of method to automatically and reliably detect failure events at WTW’s processes in near real-time and also show promise for practical application of the HC-ERS in industry. The combination of both methodologies presents a unique contribution to the field of near real-time event detection at WTW

Control and identification in activated sludge processes = Regeling en identifikatie in aktief-slib processen

This thesis is about control and identification in activated sludge processes (ASP's). The chapters in this thesis are divided in two parts. Part I deals with the development of the best feasible, close-to-optimal adaptive receding horizon optimal controller (RHOC) for N-removal in a continuously mixed alternating activated sludge process reactor. Subsequently this controller and the most common existing controllers are mutually compared by means of simulations. In addition the application of the close-to-optimal RHOC controller to a system of two hydraulically connected alternating reactors is simulated for a range of plant designs within this class. In this way the combination of design and operation is optimized. Part II concerns identification on the basis of DO-measurements and respirometry. First the DO-dynamics in a continuously mixed ASP reactor are identified, including the non-linear relation between k L a and q air . Subsequenly the dynamics of a (DO-sensor based) continuous flow respirometer are identified by exciting its dynamics.In chapter 1 the principles of the N-removing ASP are shortly explained. The new problem of total-N removal is discussed. The general features of the ASP control problem are listed: disturbance attenuation, storm events, process uncertainty and variation, multiple time-scales. Special attention is paid to the potential of RHOC. The literature with respect to operational aspects of N-removal as well as the use of DO-sensors and respirometers in ASP operation is coarsely reviewed. It is argued that the anoxic periods approach for N-removal offers two principle advantages over the anoxic zones approach: excitation of dynamics and no need for internal recirculation. Some problems in the field are indicated. With respect to DO-sensors it is illustrated that the challenges today are in the field of extracting not only DO but also additional information from its readings. All experiments in this thesis have been carried out at a pilot scale ASP. A description of this pilot plant is given in chapter 1. The chapter ends with the formulation of research objectives and the thesis outline.Chapters 2 till 5 present the design procedure for the adaptive RHOC for control of NH 4 and NO x , though not exactly chronologically. The first step is presented in chapter 4, it concerns application of optimal control to the N-removal part of the generally accepted Activated Sludge Model no. 1. From this optimal control study it occurs that alternating nitrification/denitrification, as opposed to simultaneous nitrification/denitrification, may be optimal indeed. This, together with the risk of sludge bulking at limiting DO-values, justifies the limitation to alternating process operation. To implement an optimal control strategy on-line the receding horizon principle is needed, leading to RHOC. RHOC uses an internal process model for short term predictions. Hence a computationally efficient process model is required. Such a model is developed in chapter 2 by capturing the slower process dynamics in time-varying model parameters. It is taken into account that the model structure must be suited for recursive identification of the time-varying model parameters from the measurements.RHOC, like any model predictive controller, computes the current controls on the basis of model predictions upto horizon H . Hence the sum of squared 1, 2, .., H -step ahead prediction errors is a natural identification criterion. In chapter 2 this idea is postulated and applied to NH 4 /NO x measurements collected from the pilot scale ASP described in chapter 1. H appears to affect the parameter estimates significantly, supporting the idea that use of this new identification criterion will improve MPC performance in general.In chapter 4 RHOC with this simple model is applied to the pilot plant's alternating reactor. The controller successfully passed several tests, but it also appeared that the performance of this controller is suboptimal due to inaccurate model predictions. This was to be expected, as the simplicity of the N-removal model in chapter 2 has been achieved by capturing the slower process dynamics in the model parameters, while in this stage they are not recursively estimated.The results of chapter 4 illustrate that recursive identification of (some of the) model parameters is required to keep the model uptodate. Chapter 3 presents the algorithm for recursive identification of those model parameters. The Kalman filter is used, because it has the attractive feature that the filter gain accompanying non-identifiable parameters ( e.g. the nitrification rate during anoxic periods) increases linearly in time. It is proven that this increase of the filter gain will not cause instability during normal process operation. The method performs excellently on real data.Chapter 5 concerns adaptive RHOC of N-removal in alternating ASP reactors, being the combination of the recursively identified model in chapter 3 and the RHOC controller in chapter 4. Although stability of the nonlinear RHOC feedback controller has not been proven, not to mention its combination with recursive identification, only one source of instability was encountered in many experiments. This is the scenario in which NH 4 dominates the objective functional, its setpoint is zero and the estimated rate of nitrification has become negative for whatever reason. In that case the controller will keep aeration off to prevent the predicted production of NH 4 , as a consequence no new information to update the estimated nitrification rate is obtained and the deadlock is there. Obviously this scenario is easy to prevent and does not occur under normal operating conditions.In chapter 4 the unusual observation is done that the RHOC performance is nearly invariant to its prediction horizon. This triggered a study on the cause of this phenomena and an effort to generalize the results as far as possible, the results are presented in chapter 6. It has led to the derivation of an l 1 -norm optimal state feedback controller for 2-dimensional linear time invariant systems with decoupled dynamics and a single control input.In chapter 7 the close-to-optimal adaptive RHOC of chapter 5 and three existing control strategies (timers, NH 4 -bounds based and ORP, Oxidation Reduction Potential, based) for N-removal in continuously mixed alternating reactors are compared by means of simulation. The simulations are carried out in SIMBA TM, a commercially available application within the MATLAB/SIMULINK TMenvironment, based on the Activated Sludge Model no. 1. Drawback of simulations is that the dynamics of both the sensors and the process need to be modelled. And even the best model of the ASP is nothing but a poor resemblance of the real process. However, a fair experimental comparison of multiple controllers is impossible, not only for financial reasons. Simultaneous experimental testing would require the availability of multiple identical plants in parallel. Sequential testing on one plant would disrupt the results by changes in process conditions and influent, disabling a mutual comparison. Hence simulation is the best way to compare different control-schemes. It appears that three totally different controllers (timers, NH 4 -bounds based and adaptive RHOC) can achieve a more or less equal performance, if tuned optimally. Adaptive RHOC turns out to be superior in terms of sensitivity to suboptimal tunings. The timers approach is attractive for its simplicity, but very sensitive to suboptimal tuning.Chapter 8 describes a simulation study with the scope to optimise the plant design and operation strategy of alternating activated sludge processes for N-removal with two hydraulically connected reactors. The methodology is to simulate the application of RHOC to a range of different plant designs within this class of systems. The RHOC algorithm is obtained by reformulating the controller of chapter 4 for a 2-reactors system. It appears that in the optimal process design the two reactors are placed in series, the first reactor is about four times as large as the second one. A conceptually simple feedback controller straightforwardly implements the improved operation strategy. The results of this chapter strongly advocate the simulation of optimal control applied to complex process models. The results are unexpected and indicate a significant outperformance of the current operation strategy. This kind of simulation studies at least serves as an ideas generator.Chapter 9 presents a grey-box modelling approach for the identification of the nonlinear DO dynamics. Herein, singular value decomposition of the locally available Jacobian matrix, or equivalently eigenvalue decomposition of the parameter covariance matrix, as well as parameter transformation are essential techniques. The use of respiration rate measurements greatly simplifies the modelling procedure. The approach is amongst others capable of identifying the non-linear function k L a ( q air ), i.e. the relationship between k L a and the aeration input signal q air . This is especially valuable in experimental identification of the relationship between k L a ( q air ) and the design of (newly developed) aeration equipment, the use of specific carrier materials in aerated reactors, or the presence of certain detergents. After all a higher k L a at a given q air results in a higher efficiency of energy usage for aeration, and hence identification of k L a ( q air ) for newly developed equipment can yield important sales arguments.Chapters 10 and 11 both deal with excitation of the respiration chamber dynamics in a continuous flow respirometer with the objective to extract additional information from its dissolved oxygen (DO) sensor readings. Chapter 10 is an effort to improve the accuracy of the BOD st -estimation technique developed by Spanjers et al . (1994). Contrary to expectation, the estimates still suffer from unacceptable inaccuracy due to large parameter correlation. However, a slight modification in the measurement strategy is proposed which is expected to enable more accurate estimation. The results of experiments with this modified measurement strategy are reported in chapter 11. The estimation results convincingly discourage further efforts to identify sludge kinetics and BOD st from this type of experiments.The two other objectives of chapter 11 are the identification of the DO-sensor dynamics and the dilution rate in a continuous flow respirometer by excitation of the respiration chamber dynamics. Two separate simple procedures are presented. Both procedures consist of on-purpose in-sensor experiments succeeded by an ordinary least squares estimation step. The feasibility of both procedures is verified in experiments with activated sludge, fed with municipal wastewater. Large experimental data sets are presented, which strongly advocate the on-line incorporation of both procedures in the everyday operation of the respirometer.In chapter 12 those conclusions drawn in the individual chapters which are of direct relevance to practitioners are summarized. Moreover some remaining ideas, which I believe are novel and likely to be succesfull, are shortly expounded in chapter 12 as well. The ideas concern: 1) Meeting N-total effluent standards by permitting elevated effluent NH 4 ; 2) Control explicitly aiming at meeting yearly averaged effluent standards; 3) The use of pH-measurements for continuous on-line tuning of timers in a timer-based operation strategy for alternating N-removal in a continuously mixed ASP reactor.</p

Control and Automation

Control and automation systems are at the heart of our every day lives. This book is a collection of novel ideas and findings in these fields, published as part of the Special Issue on Control and Automation. The core focus of this issue was original ideas and potential contributions for both theory and practice. It received a total number of 21 submissions, out of which 7 were accepted. These published manuscripts tackle some novel approaches in control, including fractional order control systems, with applications in robotics, biomedical engineering, electrical engineering, vibratory systems, and wastewater treatment plants. This Special Issue has gathered a selection of novel research results regarding control systems in several distinct research areas. We hope that these papers will evoke new ideas, concepts, and further developments in the field