A dynamic flotation model for real-time control and optimisation

Thesis (PhD (Electronic Engineering))--University of Pretoria, 2023.Froth flotation models that are developed for circuit design applications are often not suitable for model-based dynamic control and optimisation applications. For real-time control and optimisation applications dynamic models of the key flotation mechanisms are required, as these use real-time measurements to update internal model states and estimate model parameters in real-time. The development of a dynamic froth flotation model is described, based on a combination of fundamental mass and volume balances, fundamental steady-state froth models and empirical models for bubble size and air recovery. The model outputs are defined to correspond with real-time measurements that are commonly available on industrial flotation circuits, including measurements from froth imaging devices in combination with measurements of levels, flow rates, densities and grades. The flotation model is analysed for state observability and controllability, and it is shown that the model states and parameters can be estimated from real-time process measurements that are commonly available on industrial flotation circuits. The ability to estimate process parameters in real-time opens up opportunities for improved process control and optimisation by compensating for a specific flotation mechanism rather than the combined effect of multiple flotation mechanisms. The speed of response can also be improved when more accurate models are maintained by continuously updating model parameters. The flotation model, a state and a parameter estimator and model predictive controller are combined to simulate the potential benefits of using a non-linear model-based approach with state and parameter estimation capabilities in a dynamic control and optimisation application on flotation circuits. The strategy is shown to reject typical process disturbances effectively in the presence of process noise and outperforms a linear non-model based control strategy by a significant margin.Electrical, Electronic and Computer EngineeringPhD (Electronic Engineering)Unrestricte

Intelligent techniques for dynamic and transient analysis of multi stage desalination plant

This thesis is concerned with dynamic and transient analysis of MSF desalination plants. The technique is developed using artificial neural networks (ANN) approach for the purpose of prediction, analysis, modelling, and control of MSF desalination plant. The applicability of the method to predict an approximation of the transient operating conditions as well as the control action are shown satisfactory. The network architecture and learning algorithm are developed based on the Multilayered Feed forward Networks (MFN) with the Back Propagation (BP) learning algorithm. It was shown that the approach could intelligently capture the dynamics of the system. An improved technique is developed for the BP learning algorithm based on Global Error Node Evaluation (GENE) approach for MFN to retains the function approximation requirements for a nonlinear dynamic behaviour. However, by using this approach considerable improvement for the generalization capability could be obtained for the case study under consideration. The technique provides the necessary dynamic learning, behaviour required for MFN. This approach appears to be effective for the input - output dynamic modelling of complex process systems and therefore on-line adaptation is possible (when the characteristic of the system is changing or when more test data are available for another operating range). The developed algorithm is used for the development and validation of an empirical multi-controller structure for MSF desalination plant. Satisfactory results are obtained from practical examples with the additional training ability

Peripheral control tools for a run-of-mine ore milling circuit

Run-of-mine ore milling circuits are generally difficult to control owing to the presence of strong external disturbances, poor process models and the unavailability of important process variable measurements. These shortcomings are common for processes in the mineral-processing industry. For processes that fall into this class, the peripheral control tools in the control loop are considered to be as important as the controller itself. This work addresses the implementation of peripheral control tools on a run-of-mine ore milling circuit to help overcome the deteriorated control performance resulting from the aforementioned shortcomings. The effects of strong external disturbances are suppressed through the application of a disturbance observer. A fractional order disturbance observer is also implemented and a novel Bode ideal cutoff disturbance observer is introduced. The issue of poor process models is addressed through the detection of significant mismatch between the actual plant and the available model from process data. A closed-form expression is given for the case where the controller has a transfer function. If the controller does not have a transfer function, a partial correlation analysis is used to detect the transfer function elements in the model transfer function matrix that contain significant mismatch. The mill states and important mill parameters are estimated with the use of particle filters. Simultaneous state and parameter estimation is compared with a novel dual particle filtering scheme. A sensitivity analysis shows the class of systems for which dual estimation would provide superiorestimation accuracy over simultaneous estimation. The implemented peripheral control tools show promise for current milling circuits where proportional-integral-derivative (PID) control is prevalent, and also for advanced control strategies, such as model predictive control, which are expected to become more common in the future. AFRIKAANS : Maalkringe wat onbehandelde erts maal is oor die algemeen moeilik om te beheer as gevolg van die teenwoordigheid van sterk eksterne steurings, onakkurate aanlegmodelle en metings van belangrike prosesveranderlikes wat ontbreek. Hierdie probleme is algemeen vir aanlegte in die mineraalprosesseringsbedryf. Vir aanlegte in hierdie klas word die randbeheerinstrumente as net so belangrik as die beheerder beskou. Hierdie verhandeling beskryf die implementering van randbeheerinstrumente vir ’n maalkring wat onbehandelde erts maal, om die verswakte beheerverrigting teen te werk wat veroorsaak word deur bogenoemde probleme. Die impak van sterk eksterne steurings word teengewerk deur die implementering van ’n steuringsafskatter. ’n Breuk-orde-steuringsafskatter is ook geïmplementeer en ’n nuwe Bode ideale afsnysteuringsafskatter word voorgestel. Die kwessie van onakkurate aanlegmodelle word hanteer deur van die aanlegdata af vas te stel of daar ’n verskil is tussen die aanleg en die beskikbare model van die aanleg. ’n Uitdrukking word gegee vir hierdie verskil vir die geval waar die beheerder met ’n oordragsfunksie voorgestel kan word. Indien die beheerder nie ’n oordragsfunksie het nie, word van ‘n parsiële korrelasie-analise gebruik gemaak om die element, of elemente, in die aanleg se oordragsfunksiematriks te identifiseer wat van die werklike aanleg verskil. Die toestande en belangrike parameters in die meul word beraam deur van partikel-filters gebruikte maak. Gelyktydige toestand- en parameter-beraming word vergelyk met ’n nuwe dubbel-partikelfilter skema. ’n Sensitiwiteitsanalise wys die klas van stelsels waarvoor dubbel-afskatting meer akkurate waardes sal gee as gelyktydige afskatting. Die voorgestelde randbeheerinstrumente is toepaslik vir huidige maalkringe waar PID-beheer algemeen is, asook vir gevorderde beheerstrategieë, soos model-voorspellende beheer, wat na verwagting in die toekoms meer algemeen sal word. CopyrightDissertation (MEng)--University of Pretoria, 2012.Electrical, Electronic and Computer Engineeringunrestricte

The application of neural networks in active suspension

This thesis considers the application of neural networks to automotive suspension systems. In particular their ability to learn non-linear feedback control relationships. The speed of processing, once trained, means that neural networks open up new opportunities and allow increased complexity in the control strategies employed. The suitability of neural networks for this task is demonstrated here using multilayer perceptron, (MLP) feed forward neural networks applied to a quarter vehicle simulation model. Initially neural networks are trained from a training data set created using a non-linear optimal control strategy, the complexity of which prohibits its direct use. They are shown to be successful in learning the relationship between the current system states and the optimal control. [Continues.

Active Suppression ofAerofoil Flutter via Neural-Network-Based Adaptive Nonlinear Optimal Control

This thesis deals with active flutter suppression (AFS) on aerofoils via adaptive nonlinear optimal control using neural networks (NNs). Aeroelastic flutter can damage aerofoils if not properly controlled. AFS not only ensures flutter-free flight but also enables the use of aerodynamically more efficient lightweight aerofoils. However, existing optimal controllers for AFS are generally susceptible to modelling errors while other controllers less prone to uncertainties do not provide optimal control. This thesis, thus, aims to reduce the impact of the dilemma by proposing new solutions based on nonlinear optimal control online synthesis (NOCOS) according to online updated dynamics. Existing NOCOS methods, with NNs as essential elements, require a separate initial stabilising control law for the overall system, an additional stabilising tuning loop for the actor NN, or an additional stabilising term in the critic NN tuning law, to guarantee the closed-loop stability for unstable and marginally stable systems. The resulting complexity is undesired in AFS applications due to computational concerns in real-time implementation. Moreover, the existing NOCOS methods are confined to locally nonlinear systems, while aeroelastic systems under consideration are globally nonlinear. These make all the existing NOCOS algorithms inapplicable to AFS without modification and improvement. Therefore, this thesis solves the aforementioned problems through the following step-by-step approaches. Firstly, a four degrees-of-freedom (4-DOF) aeroelastic model is considered, where leading- and trailing-edge control surfaces of the aerofoil are used to actively suppress flutter. Accordingly, a virtual stiffness-damping system (VSDS) is developed to simulate physical stiffness in the aeroelastic system. The VSDS, together with a scaled-down typical aerofoil section placed in a wind tunnel, serve as an experimental 4-DOF aeroelastic test-bed for synthesis and validation of proposed AFS controllers that follow. Secondly, a Modified form of NN-based Value Function Approximation (MVFA), tuned by gradient-descent learning, is proposed for NOCOS to address the closedloop stability in a compact controller configuration suitable for real-time implementation. Its validity and efficacy are examined by the Lyapunov stability analysis and numerical studies. Thirdly, a systematic procedure based on linear matrix inequalities is further proposed for synthesising a scheduled parameter matrix to generalise the MVFA to to globally nonlinear cases, so that the new NN controller suits AFS applications. In addition, the extended Kalman filter (EKF) is proposed for the new NN controller for fast parameter convergence. An identifier NN is also derived to capture and update aeroelastic dynamics in real time to mitigate the impact of modelling errors. Wind-tunnel experiments were conducted for validation. Finally, a non-quadratic functional is introduced to generalise the performance index to tackle the problem where control inputs are constrained. The feasibility of including the non-quadratic cost function under the proposed control scheme based on the MVFA is examined via the Lyapunov stability analysis and was also experimentally evaluated through the wind-tunnel testings. The proposed NN controllers are compact in structure and shown capable of maintaining the closed-loop stability while eliminating the need for a separate initial stabilising control law for the overall system, an additional tuning loop for the actor NN, and an additional stabilising term in the critic NN tuning law. Under the new control schemes, online synthesised nonlinear control laws are optimal in the cases with and without constraints in control. Comparisons drawn with a popular linear-parameter-varying (LPV) controller in the form of the widely used linear quadratic regulator (LQR) in experiments show that the proposed NN controllers outperform the LPV-LQR algorithm and improve AFS from the optimal control perspective. Specifically, the proposed NN controllers can effectively mitigate the impact of modelling errors, successfully solving the mentioned dilemma involved in AFS. The results also confirm that the proposed NN controllers are suitable for real-time implementation.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

Sliding Mode Control

The main objective of this monograph is to present a broad range of well worked out, recent application studies as well as theoretical contributions in the field of sliding mode control system analysis and design. The contributions presented here include new theoretical developments as well as successful applications of variable structure controllers primarily in the field of power electronics, electric drives and motion steering systems. They enrich the current state of the art, and motivate and encourage new ideas and solutions in the sliding mode control area

Adaptive control and neural network control of nonlinear discrete-time systems

Ph.DDOCTOR OF PHILOSOPH

Supervised fault tolerant control architecture for nonlinear systems

Scope: The growing complexity of physical plants and control missions inevitably leads to increasing occurrence, diversity and severity of faults. Availability, defined as the probability that a system or equipment will operate satisfactory and effectively at any point of time, becomes a factor of increasing importance. Fault Tolerant Control (FTC) is a field of research that aims to increase availability and reduce the risk of safety hazards and other undesirable consequences by specifically designing control algorithms capable of maintaining stability and/or performance despite the occurrence of faults. This report presents a novel FTC solution based on a hierarchical architecture in which an adaptive critic controller is overseen by a supervisor managing a dynamic model bank of fault solutions.Findings and Conclusions: The presented work has demonstrated that the implementation of a synergistic combination of a reconfigurable controller and a fault diagnosis and controller malfunction detection supervisor based on three distinct quality indexes generates an efficient and reliable FTC architecture. The application of adaptive critic designs as reconfigurable controllers is shown to give the hierarchical algorithm the degree of flexibility required to deal with both abrupt and incipient unknown changes in the plant dynamics due to faults. The proposed supervisor system is used to accelerate the convergence of the method by loading new initial conditions to the controller when the plant is affected by a known abrupt fault. Moreover, the developed fault diagnosis decision logic is capable of recognizing new fault scenarios and assimilating them online to the dynamic model bank, along with parameters for the corresponding controller. The introduction of the weight quality index has made possible to distinguish between faults in the plant and controller malfunctions caused by online training divergence or local minima convergence. In order to achieve application-specific key FTC specifications, a methodology for initializing and tuning twelve distinct parameters of the quality indexes was also developed. Finally, a series of key steps that form the basis for the fault development information extraction module capable of providing the probability of occurrence of future faults to the user, are also included in this report

Inferential measurement and control of ballast water treatment system

As a result of interaction with the surrounding environment, shipping has become one of the vectors of bio-invasion across the globe. Ballast water is one of the means of bio-invasion from shipping through which microorganisms break through natural barriers and establish in a new location. Shipboard treatment systems are predominately considered as mitigating measures for bio-invasion via a ballast water system. Currently shipboard performance monitoring of ballast water treatment systems, and thus assessment of discharge quality of ballast water as required by the Convention, depends on off-line laboratory assays with long delay analysis. Lack of online measurement sensors to assess the viability of microorganisms after treatment has made monitoring and thus control of ballast water treatment systems difficult. In this study, a methodology was developed, through a mathematical algorithm, to provide an inferential model-based measurement system in order to monitor and thus control non-observable ballast water systems. In the developed inferential measurement the primary output of the treatment system is inferred by using easy to measure secondary output variables and a model relating these two outputs. Data-driven modeling techniques, including Artificial Neural Networks (ANN), were used to develop an estimator for the small scale UV treatment system based on the data obtained from conducted experiments. The results from ANN showed more accuracy in term of Root Mean Squared Error (RMSE) and Linear Correlation Coefficient (LCC) when compared to the other techniques. The same methodology was implemented to a larger scale treatment system comprising micro-filter and UV reactor. A software-based inferential measurement for online monitoring of the treatment system was then developed. Following monitoring, inferential control of the treatment setup was also accomplished using direct inverse control strategy. A software-based “Decision Making Tool” consisted of two intelligent inverse models, which were used to control treatment flow rate and maintain the effective average UV dose. The results from this study showed that software-based estimation of treatment technologies can provide online measurement and control for ballast water system.EThOS - Electronic Theses Online ServiceEuropean funded project “BaWaPla”GBUnited Kingdo