The bioreactor landfill is an emerging concept for solid waste management that has gained significant attention in the last decade. This technology employs specific operational practices to enhance the microbial decomposition processes in landfills. However, the unsupervised management and lack of operational guidelines for the bioreactor landfill, specifically leachate manipulation and recirculation processes, usually results in less than optimal system performance. Therefore, these limitations have led to the development of SMART (Sensor-based Monitoring and Remote-control Technology), an expert control system that utilizes real-time monitoring of key system parameters in the management of bioreactor landfills. SMART replaces conventional open-loop control with a feedback control system that aids the human operator in making decisions and managing complex control issues. The target from this control system is to provide optimum conditions for the biodegradation of the refuse, and also, to enhance the performance of the bioreactor in terms of biogas generation. SMART includes multiple cascading logic controllers and mathematical calculations through which the quantity and quality of the recirculated solution are determined. The expert system computes the required quantities of leachate, buffer, supplemental water, and nutritional amendments in order to provide the bioreactor landfill microbial consortia with their optimum growth requirements. Soft computational methods, particularly fuzzy logic, were incorporated in the logic controllers of SMART so as to accommodate the uncertainty, complexity, and nonlinearity of the bioreactor landfill processes. Fuzzy logic was used to solve complex operational issues in the control program of SMART including: (1) identify the current operational phase of the bioreactor landfill based on quantifiable parameters of the leachate generated and biogas produced, (2) evaluate the toxicological status of the leachate based on certain parameters that directly contribute to or indirectly indicates bacterial inhibition, and (3) predict biogas generation rates based on the operational phase, leachate recirculation, and sludge addition. The later fuzzy logic model was upgraded to a hybrid model that employed the learning algorithm of artificial neural networks to optimize the model parameters. SMART was applied to a pilot-scale bioreactor landfill prototype that incorporated the hardware components (sensors, communication devices, and control elements) and the software components (user interface and control program) of the system. During a one-year monitoring period, the feasibility and effectiveness of the SMART system were evaluated in terms of multiple leachate, biogas, and waste parameters. In addition, leachate heating was evaluated as a potential temperature control tool in bioreactor landfills. The pilot-scale implementation of SMART demonstrated the applicability of the system. SMART led to a significant improvement in the overall performance of the BL in terms of methane production and leachate stabilization. Temperature control via recirculation of heated leachate achieved high degradation rates of organic matter and improved the methanogenic activity.

The iterative learning control (ILC) problem considers control tasks that perform a specific tracking command, and the command is to be performed is many times. The system returns to the same initial conditions on the desired trajectory for each repetition, also called run, or iteration. The learning law adjusts the command to a feedback system based on the error observed in the previous run, and aims to converge to zero-tracking error at sampled times as the iterations progress. The ILC problem is an inverse problem: it seeks to converge to that command that produces the desired output. Mathematically that command is given by the inverse of the transfer function of the feedback system, times the desired output. However, in many applications that unique command is often an unstable function of time. A discrete-time system, converted from a continuous-time system fed by a zero-order hold, often has non-minimum phase zeros which become unstable poles in the inverse problem. An inverse discrete-time system will have at least one unstable pole, if the pole-zero excess of the original continuous-time counterpart is equal to or larger than three, and the sample rate is fast enough. The corresponding difference equation has roots larger than one, and the homogeneous solution has components that are the values of these poles to the power of k, with k being the time step. This creates an unstable command growing in magnitude with time step. If the ILC law aims at zero-tracking error for such systems, the command produced by the ILC iterations will ask for a command input that grows exponentially in magnitude with each time step. This thesis examines several ways to circumvent this difficulty, designing filters that prevent the growth in ILC. The sister field of ILC, repetitive control (RC), aims at zero-error at sample times when tracking a periodic command or eliminating a periodic disturbance of known period, or both. Instead of learning from a previous run always starting from the same initial condition, RC learns from the error in the previous period of the periodic command or disturbance. Unlike ILC, the system in RC eventually enters into steady state as time progresses. As a result, one can use frequency response thinking. In ILC, the frequency thinking is not applicable since the output of the system has transients for every run. RC is also an inverse problem and the periodic command to the system converges to the inverse of the system times the desired output. Because what RC needs is zero error after reaching steady state, one can aim to invert the steady state frequency response of the system instead of the system transfer function in order to have a stable solution to the inverse problem. This can be accomplished by designing a Finite Impulse Response (FIR) filter that mimics the steady state frequency response, and which can be used in real time. This dissertation discusses how the digital feedback control system configuration affects the locations of sampling zeros and discusses the effectiveness of RC design methods for these possible sampling zeros. The sampling zeros are zeros introduced by the discretization process from continuous-time system to the discrete-time system. In the RC problem, the feedback control system can have sampling zeros outside the unit circle, and they are challenges for the RC law design. Previous research concentrated on the situation where the sampling zeros of the feedback control system come from a zero-order hold on the input of a continuous-time feedback system, and studied the influence of these zeros including the influence of these sampling zeros as the sampling rate is changed from the asymptotic value of sample time interval approaching zero. Effective RC design methods are developed and tested based for this configuration. In the real world, the feedback control system may not be the continuous-time system. Here we investigate the possible sampling zero locations that can be encountered in digital control systems where the zero-order hold can be in various possible places in the control loop. We show that various new situations can occur. We discuss the sampling zeros location with different feedback system structures, and show that the RC design methods still work. Moreover, we compare the learning rates of different RC design methods and show that the RC design method based on a quadratic fit of the reciprocal of the steady state frequency response will have the desired learning rate features that balance the robustness with efficiency. This dissertation discusses the steady-state response filter of the finite-time signal used in ILC. The ILC problem is sensitive to model errors and unmodelled high frequency dynamics, thus it needs a zero-phase low-pass filter to cutoff learning for frequencies where there is too much model inaccuracy for convergence. But typical zero-phase low-pass filters, like Filtfilt used by MATLAB, gives the filtered results with transients that can destabilize ILC. The associated issues are examined from several points of view. First, the dissertation discusses use of a partial inverse of the feedback system as both learning gain matrix and a low-pass filter to address this problem The approach is used to make a partial system inverse for frequencies where the model is accurate, eliminating the robustness issue. The concept is used as a way to improve a feedback control system performance whose bandwidth is not as high as desired. When the feedback control system design is unable to achieve the desired bandwidth, the partial system inverse for frequency in a range above the bandwidth can boost the bandwidth. If needed ILC can be used to further correct response up to the new bandwidth. The dissertation then discusses Discrete Fourier Transform (DFT) based filters to cut off the learning at high frequencies where model uncertainty is too large for convergence. The concept of a low pass filter is based on steady state frequency response, but ILC is always a finite time problem. This forms a mismatch in the design process, and we seek to address this. A math proof is given showing the DFT based filters directly give the steady-state response of the filter for the finite-time signal which can eliminate the possibility of instability of ILC. However, such filters have problems of frequency leakage and Gibbs phenomenon in applications, produced by the difference between the signal being filtered at the start time and at the final time, This difference applies to the signal filtered for nearly all iterations in ILC. This dissertation discusses the use of single reflection that produced a signal that has the start time and end times matching and then using the original signal portion of the result. In addition, a double reflection of the signal is studied that aims not only to eliminate the discontinuity that produces Gibbs, but also aims to have continuity of the first derivative. It applies a specific kind of double reflection. It is shown mathematically that the two reflection methods reduce the Gibbs phenomenon. A criterion is given to determine when one should consider using such reflection methods on any signal. The numerical simulations demonstrate the benefits of these reflection methods in reducing the tracking error of the system.

Yes / This correspondence presents an investigation into the comparative performance of an active vibration control (AVC) system using a number of intelligent learning algorithms. Recursive least square (RLS), evolutionary genetic algorithms (GAs), general regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) algorithms are proposed to develop the mechanisms of an AVC system. The controller is designed on the basis of optimal vibration suppression using a plant model. A simulation platform of a flexible beam system in transverse vibration using a finite difference method is considered to demonstrate the capabilities of the AVC system using RLS, GAs, GRNN, and ANFIS. The simulation model of the AVC system is implemented, tested, and its performance is assessed for the system identification models using the proposed algorithms. Finally, a comparative performance of the algorithms in implementing the model of the AVC system is presented and discussed through a set of experiments.

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The present dynamic manufacturing environment has been characterized by a greater variety of products, shorter life-cycles of products and rapid introduction of new technologies, etc. Recently, a new manufacturing paradigm, i.e. Reconfigurable Manufacturing Systems (RMS), has emerged to address such challenging issues. RMSs are able to adapt themselves to new business conditions timely and economically with a modular design of hardware/software system. Although a lot of research has been conducted in areas related to RMS, very few studies on system-level control for RMS have been reported in literature. However, the rigidity of current manufacturing systems is mainly from their monolithic design of control systems. Some new developments in Information Technology (IT) bring new opportunities to overcome the inflexibility that shadowed control systems for years. Component-based software development gains its popularity in 1990's. However, some well-known drawbacks, such as complexity and poor real-time features counteract its advantages in developing reconfigurable control system. New emerging Extensible Markup Language (XML) and Web Services, which are based on non-proprietary format, can eliminate the interoperability problems that traditional software technologies are incompetent to accomplish. Another new development in IT that affects the manufacturing sector is the advent of agent technology. The characteristics of agent-based systems include autonomous, cooperative, extendible nature that can be advantageous in different shop floor activities. This dissertation presents an innovative control architecture, entitled Component-based Intelligent Control Architecture (CICA), designed for system-level control of RMS. Software components and open-standard integration technologies together are able to provide a reconfigurable software structure, whereas agent-based paradigm can add the reconfigurability into the control logic of CICA. Since an agent-based system cannot guarantee the best global performance, agents in the reference architecture are used to be exception handlers. Some widely neglected problems associated with agent-based system such as communication load and local interest conflicts are also studied. The experimental results reveal the advantage of new agent-based decision making system over the existing methodologies. The proposed control system provides the reconfigurability that lacks in current manufacturing control systems. The CICA control architecture is promising to bring the flexibility in manufacturing systems based on experimental tests performed. / Ph. D.

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This paper presents a theoretical development of adaptive cruise control models and platoon operation logic for Automated Highway Systems in the Advanced Vehicle Control Systems (AVeS). Three control modes, constant speed, emergency and vehicle-following, are defined based on the minimum safe stopping distance, and applied to the platoon operations. Desired acceleration model is built for the different cruise control mode by considering the relative velocity, the difference between the relative distance and desired spacing, and the acceleration of the preceding vehicle. A control system model is proposed based on the analysis of vehicle dynamics. The contribution of uncontrolled forces from the air, slop and friction to the vehicle acceleration is considered. Application of control models for two successive vehicles is simulated under the situations of speed transition and emergency stopping. Proper control parameters are determined for different operation mode subject to the conditions: collision avoidance and stability. Same criteria are utilized to the platoon simulation in which the operation logic is regulated so that the platoon leader is operated under either emergency mode or constant speed mode depending upon the . distance from the downstream vehicle, while the intraplatoon vehicles are forced to operate under vehicle-following mode. Three cases under speed transition, emergency stopping and platoon leader splitting are simulated to determine the stable control parameters. Lane capacity analysis shows the tradeoff between safety and efficiency for platoon. modal operations on freeway with guideline or automated highway. / Master of Science

A variety of factors such as increasing electrical energy demand, slow expansion of transmission infrastructures, and electric energy market deregulation, are forcing utilities and system operators to operate power systems closer to their design limits. Operating under stressed regimes can have a detrimental effect on the rotor-angle stability of the system. This stability reduction is often reflected by the emergence or worsening of poorly damped low-frequency electromechanical oscillations. Without appropriate measures these can lead to costly blackouts. To guarantee system security, operators are sometimes forced to limit power transfers that are economically beneficial but that can result in poorly damped oscillations. Controllers that damp these oscillations can improve system reliability by preventing blackouts and provide long term economic gains by enabling more extensive utilization of the transmission infrastructure. Previous research in the use of artificial neural network-based intelligent controllers for power system damping control has shown promise when tested in small power system models. However, these controllers do not scale-up well enough to be deployed in realistically-sized power systems. The work in this dissertation focuses on improving the scalability of intelligent power system stabilizing controls so that they can significantly improve the rotor-angle stability of large-scale power systems. A framework for designing effective and robust intelligent controllers capable of scaling-up to large scale power systems is proposed. Extensive simulation results on a large-scale power system simulation model demonstrate the rotor-angle stability improvements attained by controllers designed using this framework.

Thesis (PhD)--University of Stellenbosch, 2004. / 271 Leaves printed single pages, preliminary pages i-xviii and 253 numberd pages. Includes bibliography. List of figures, List of tables. / ENGLISH ABSTRACT: A Neurocontrol Paradigm for Intelligent Process Control using Evolutionary Reinforcement Learning Balancing multiple business and operational objectives within a comprehensive control strategy is a complex configuration task. Non-linearities and complex multiple process interactions combine as formidable cause-effect interrelationships. A clear understanding of these relationships is often instrumental to meeting the process control objectives. However, such control system configurations are generally conceived in a qualitative manner and with pronounced reliance on past effective configurations (Foss, 1973). Thirty years after Foss' critique, control system configuration remains a largely heuristic affair. Biological methods of processing information are fundamentally different from the methods used in conventional control techniques. Biological neural mechanisms (i.e., intelligent systems) are based on partial models, largely devoid of the system's underlying natural laws. Neural control strategies are carried out without a pure mathematical formulation of the task or the environment. Rather, biological systems rely on knowledge of cause-effect interactions, creating robust control strategies from ill-defined dynamic systems. Dynamic modelling may be either phenomenological or empirical. Phenomenological models are derived from first principles and typically consist of algebraic and differential equations. First principles modelling is both time consuming and expensive. Vast data warehouses of historical plant data make empirical modelling attractive. Singular spectrum analysis (SSA) is a rapid model development technique for identifying dominant state variables from historical plant time series data. Since time series data invariably covers a limited region of the state space, SSA models are almost necessarily partial models. Interpreting and learning causal relationships from dynamic models requires sufficient feedback of the environment's state. Systemisation of the learning task is imperative. Reinforcement learning is a computational approach to understanding and automating goal-directed learning. This thesis aimed to establish a neurocontrol paradigm for non-linear, high dimensional processes within an evolutionary reinforcement learning (ERL) framework. Symbiotic memetic neuro-evolution (SMNE) is an ERL algorithm developed for global tuning of neurocontroller weights. SMNE is comprised of a symbiotic evolutionary algorithm and local particle swarm optimisation. Implicit fitness sharing ensures a global search and the synergy between global and local search speeds convergence.Several simulation studies have been undertaken, viz. a highly non-linear bioreactor, a rigorous ball mill grinding circuit and the Tennessee Eastman control challenge. Pseudo-empirical modelling of an industrial fed-batch fermentation shows the application of SSA for developing partial models. Using SSA, state estimation is forthcoming without resorting to fundamental models. A dynamic model of a multieffect batch distillation (MEBAD) pilot plant was fashioned using SSA. Thereafter, SMNE developed a neurocontroller for on-line implementation using the SSA model of the MEBAD pilot plant. Both simulated and experimental studies confirmed the robust performance of ERL neurocontrollers. Coordinated flow sheet design, steady state optimisation and nonlinear controller development encompass a comprehensive methodology. Effective selection of controlled variables and pairing of process and manipulated variables were implicit to the SMNE methodology. High economic performance was attained in highly non-linear regions of the state space. SMNE imparted significant generalisation in the face of process uncertainty. Nevertheless, changing process conditions may necessitate neurocontroller adaptation. Adaptive neural swarming (ANS) allows for adaptation to drifting process conditions and tracking of the economic optimum online. Additionally, SMNE allows for control strategy design beyond single unit operations. SMNE is equally applicable to processes with high dimensionality, developing plant-wide control strategies. Many of the difficulties in conventional plant-wide control may be circumvented in the biologically motivated approach of the SMNE algorithm. Future work will focus on refinements to both SMNE and SSA. SMNE and SSA thus offer a non-heuristic, quantitative approach that requires minimal engineering judgement or knowledge, making the methodology free of subjective design input. Evolutionary reinforcement learning offers significant advantages for developing high performance control strategies for the chemical, mineral and metallurgical industries. Symbiotic memetic neuro-evolution (SMNE), adaptive neural swarming (ANS) and singular spectrum analysis (SSA) present a response to Foss' critique. / AFRIKAANSE OPSOMMING: 'n Neurobeheer paradigma vir intelligente prosesbeheer deur die gebruik van evolusionêre versterkingsleer Dit is 'n komplekse ontwikkelingstaak om menigte besigheids- en operasionele doelwitte in 'n omvattende beheerstrategie te vereenselwig. Nie-lineêriteite en vele komplekse prosesinteraksies kombineer om ingewikkelde aksie-reaksie verwantskappe te vorm. Dit is dikwels noodsaaklik om hierdie interaksies omvattend te verstaan, voordat prosesbeheer doelwitte doeltreffend gedoen kan word. Tog word sulke beheerstelsels dikwels saamgestel op grond van kwalitatiewe kriteria en word ook dikwels staatgemaak op historiese benaderings wat voorheen effektief was (Foss, 1973). Dertig jaar na Foss se kritiek, bly prosesbeheerstelsel ontwerp 'n heuristiese saak. Die biologiese prosessering van informasie is fundamenteel verskillend van metodes wat gebruik word in konvensionele beheertegnieke. Biologiese neurale meganismes (d.w.s., intelligente stelsels) word gebaseer op gedeeltelike modelle, wat grotendeels verwyderd is van die onderskrywende natuurwette. Neurobeheerstrategieë word toegepas sonder suiwer wiskundige formulering van die taak of die omgewing. Biologiese stelsels maak eerder staat op kennis van aksie-reaksie verhoudings en skep robuuste beheerstrategieë van swak gedefineerde dinamiese stelsels. Dinamiese modelle is of fundamenteel of empiries. Fundamentele modelle word ontwikkel vanaf eerste beginsels en word tipies uit algebraïese en differensiële vergelykings saamgestel. Modellering vanaf eerste beginsels is beide tydrowend en duur. Groot databasisse van historiese aanlegdata maak empiriese modellering aantreklik. Singuliere spektrumanalise (SSA) maak die vinnige ontwerp van empiriese modelle moontlik, waardeur dominante veranderlikes vanaf historiese tydreekse onttrek kan word. Aangesien tydreeksdata slegs 'n gedeelte van die prosesomgewing verteenwoordig, is SSA modelle noodwendig gedeeltelike modelle. Die interpretasie en aanleer van kousale verhoudings vanaf dinamiese modelle vereis voldoende terugvoer van omgewingstoestande. Die leertaak moet sistematies uitgevoer word. Versterkingsleer is 'n ramingsbenadering tot 'n doelwit-gedrewe leerproses. Hierdie tesis bewerkstellig 'n neurobeheerparadigme vir nie-lineêre prosesse met hoë dimensies binne 'n evolusionêre versterkingsleer (EVL) raamwerk. Simbiotiese, memetiese neuro-evolusie (SMNE) is 'n EVL algoritme wat ontwikkel is vir globale verstelling van die gewigte van ‘n neurobeheerder. SMNE is saamgestel uit 'n simbiotiese evolusionêre algoritme en 'n lokale partikelswerm-algoritme. Implisiete fiksheidsdeling verseker 'n globale soektog en die sinergie tussen globale en lokale soektogte bespoedig konvergensie.Verskeie simulasie studies is onderneem, o.a. die van 'n hoogs nie-lineêre bioreaktor, 'n balmeulaanleg en die Tennessee Eastman beheer probleem. Empiriese modellering van 'n industriële enkelladingsfermentasie demonstreer die aanwending van SSA vir die ontwikkeling van gedeeltelike modelle. SSA benader die toestand van 'n dinamiese stelsel sonder die aanwending van fundamentele modellering. 'n Dinamiese model van 'n multi-effek-enkelladingsdistillasie (MEBAD) proefaanleg is bewerkstellig deur die gebruik van SSA. Daarna is SMNE gebruik om 'n neurobeheerder te skep vanaf die SSA model vir die beheer van die MEBAD proefaanleg. Beide simulasie en eksperimentele studies het die robuuste aanwending van EVL neurobeheerders bevestig. Die gekoördineerde ontwerp van vloeidiagramme, gestadigde toestand-optimering en nie-lineêre beheerderontwikkeling vereis 'n omvattende metodologie. Beheerveranderlikes en die koppeling van proses- en uitvoerveranderlikes is implisiet en effektief. Maksimale ekonomiese aanwins was moontlik in hoogs nie-lineêre dele van die toestandsruimte. SMNE het besondere veralgemening toegevoeg tot neurobeheerderstrategieë ten spyte van prosesonsekerhede. Nietemin, veranderende prosestoestande mag neurobeheerderaanpassing genoodsaak. Aanpasbare neurale swerm (ANS) algoritmes pas neurobeheerders aan tydens veranderende proseskondisies en volg die ekonomiese optimum, terwyl die beheerder die proses beheer. SMNE bewerkstellig ook die ontwikkeling van beheerstrategieë vir prosesse met meer as een eenheidsoperasie. SMNE skaal na prosesse met hoë dimensionaliteit vir die ontwikkeling van aanlegwye beheerstrategieë. Talle kwelvrae in konvensionele aanleg-wye prosesbeheer word deur die biologies gemotiveerde benadering van die SMNE algoritme uit die weg geruim. Toekomstige werk sal fokus op die verfyning van beide SMNE en SSA. SMNE en SSA bied 'n nie-heuristiese, kwantitatiewe benadering wat minimale ingenieurskennis of oordeel vereis. Die metodologie is dus vry van subjektiewe ontwerpsoordeel. Evolusionêre versterkingsleer bied talle voordele vir 'n ontwikkeling van effektiewe beheerstrategieë vir die chemiese, mineraal en metallurgiese industrieë. Simbiotiese memetiese neuro-evolusie (SMNE), aanpasbare neurale swerm metodes (ANS) en singulêre spektrum analise (SSA) gee antwoord op Foss se kritiek.