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Automatic Development and Adaptation of Concise Nonlinear Models for System Identification
Mathematical descriptions of natural and man-made processes are the bedrock of science, used by humans to understand, estimate, predict and control the natural and built world around them. The goal of system identification is to enable the inference of mathematical descriptions of the true behavior and dynamics of processes from their measured observations. The crux of this task is the identification of the dynamic model form (topology) in addition to its parameters. Model structures must be concise to offer insight to the user about the process in question. To that end, this dissertation proposes three methods to improve the ability of system identification to identify succinct nonlinear model structures.
The first is a model structure adaptation method (MSAM) that modifies first principles models to increase their predictive ability while maintaining intelligibility. Model structure identification is achieved by this method despite the presence of parametric error through a novel means of estimating the gradient of model structure perturbations. I demonstrate MSAM\u27s ability to identify underlying nonlinear dynamic models starting from linear models in the presence of parametric uncertainty. The main contribution of this method is the ability to adapt the structure of existing models of processes such that they more closely match the process observations.
The second method, known as epigenetic linear genetic programming (ELGP), conducts symbolic regression without a priori knowledge of the form of the model or its parameters. ELGP incorporates a layer of genetic regulation into genetic programming (GP) and adapts it by local search to tune the resultant model structures for accuracy and conciseness. The introduction of epigenetics is made simple by the use of a stack-based program representation. This method, tested on hundreds of dynamics problems, demonstrates the ability of epigenetic local search to improve GP by producing simpler and more accurate models.
The third method relies on a multidimensional GP approach (M4GP) for solving multiclass classification problems. The proposed method uses stack-based GP to conduct nonlinear feature transformations to optimize the clustering of data according to their classes. In comparison to several state-of-the-art methods, M4GP is able to classify test data better on several real-world problems. The main contribution of M4GP is its demonstrated ability to combine the strengths of GP (e.g. nonlinear feature transformations and feature selection) with the strengths of distance-based classification.
MSAM, ELGP and M4GP improve the identification of succinct nonlinear model structures for continuous dynamic processes with starting models, continuous dynamic processes without starting models, and multiclass dynamic processes without starting models, respectively. A considerable portion of this dissertation is devoted to the application of these methods to these three classes of real-world dynamic modeling problems. MSAM is applied to the restructuring of controllers to improve the closed-loop system response of nonlinear plants. ELGP is used to identify the closed-loop dynamics of an industrial scale wind turbine and to define a reduced-order model of fluid-structure interaction. Lastly, M4GP is used to identify a dynamic behavioral model of bald eagles from collected data. The methods are analyzed alongside many other state-of-the-art system identification methods in the context of model accuracy and conciseness
Proceedings. 24. Workshop Computational Intelligence, Dortmund, 27. - 28. November 2014
Dieser Tagungsband enthält die Beiträge des 24. Workshops "Computational Intelligence" des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA), der vom 27. - 28. November 2014 in Dortmund stattgefunden hat. Die Schwerpunkte sind Methoden, Anwendungen und Tools für Fuzzy-Systeme, Künstliche Neuronale Netze, Evolutionäre Algorithmen und Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen Anwendungen und Benchmark-Problemen
Reinforcement Learning based Adaptive Model Predictive Power Pinch Analysis Systems Level Energy Management Approach to Uncertainty in Isolated Hybrid Energy Storage Systems
Ph. D. ThesisHybrid energy storage systems (HESS) involves the integration of multiple energy storage
technologies with different complementary characteristics which are significantly advantageous
compared to a single energy storage system, and can greatly improve the reliability of
intermittent renewable energy sources (RES). Aside from the advantages HESS offer, the
control and coordination of the multiple energy storages and the vital elements of the system
via an optimised energy management strategy (EMS) involves increased computational time.
Nevertheless, a systems-level graphical EMS based on Power Pinch Analysis (PoPA) which
is a low burden computational tool was recently proposed for HESS. In this respect, the
EMS which effectively resolved deficit and excess energy objectives was effected via the
graphical PoPA tool, the power grand composite curve (PGCC). PGCC is basically a plot
of integrated energy demands and sources in the system as a function of time. Although of
proven success, accounting for uncertainty with PoPA is a cogent research question due to
the assumption of an ideal day ahead (DA) generation and load profiles forecast. Therefore,
the proposition of several graphical and reinforcement learning based ‘adaptive’ PoPA EMSs
in order to address the issue of uncertainty with PoPA, has been the major contribution of
this thesis. Firstly, to counteract the combined effect of uncertainty with PoPA, an Adaptive
PoPA EMS for a standalone HESS has been proposed. In the Adaptive PoPA, the PGCC was
implemented within a receding horizon model predictive framework with the current output
state of the energy storage (in this case the battery) used as control feedback to derive an
updated sequence of EMS, inferred via PGCC shaping. Additionally, during the control and operation of the HESS, re-computation of the PGCC only occurs if a forecast uncertainty
occurs such that the error between the real and estimated battery’s state of charge becomes
greater than an arbitrarily chosen threshold value of 5%. Secondly a Kalman filter for the
optimal estimation of uncertainty distributed as a normal Gaussian is integrated into the
Adaptive PoPA in order to recursively predict the State of Charge of the battery based on
the likelihood of uncertainty. Thus, the Kalman filter Adaptive PoPA by anticipating the
effect of uncertainty offers an improved approach to the Adaptive PoPA particularly when
the uncertainty is of a Gaussian distribution. The algorithm is therefore more sophisticated
than the Adaptive PoPA but nevertheless computationally efficient and offers a preventive
measure as an improvement. Furthermore, Tabular Dyna Q-learning algorithm, a subset of
reinforcement learning which employs a learning agent to solve a discrete Markov Decision
Process by maximising an expected reward in accordance with the Bellman optimality, is
integrated within the Power Pinch Analysis. Thereafter, a deep neural network is used to
approximate the Q-Learning Table. These aforementioned methods which have been highlighted
in order of computational time can be deployed with only a minimal level of historical
data requirements such as the average load profile or base load data and solar irradiance
forecast to produce a deterministic solution. Nevertheless, this thesis proposed a probabilistic
adaptive PoPA strategy based on a (recursive least square) Monte Carlo simulation chance
constrained framework, in the event where there is sufficient amount of historical data such
as the probability distribution of the uncertain model parameters. The probabilistic approach
is no doubt more computationally intensive than the deterministic methods presented though
it proffers a much more realistic solution to the problem of uncertainty. In order to enhance
the probabilistic adaptive PoPA, an actor-critic deep neural network reinforcement learning
agent is incorporated. The six methods are evaluated against the DA PoPA on an actual
isolated HESS microgrid built in Greece with respect to the violation of the energy storage
operating constraints and plummeting carbon emission footprint.Petroleum
Technology Development Funds (PTDF