Concentrated solar power (CSP) is an efficient means of renewable energy that makes use of solar radiation to produce electricity instead of making use of conventional fossil fuel techniques such as burning coal. The aim of this study is the simulation and characterisation of a CSP plant in order to gain a better understanding of the dominant plant dynamics. Due to the nature of the study, the dissertation is divided into two main parts namely the simulation of a CSP plant model and the characterisation of the plant model. Modelling the CSP plant takes the form of developing an accurate Flownex® model of a 40 MW combined cycle CSP plant. The model includes thermal energy storage as well as making use of a duct burner. The Flownex® model is based on an existing TRNSYS model of the same plant. The Flownex® model is verified and validated, by making use of a bottom-up approach, to ensure that the developed model is in fact correct. The characterisation part of this dissertation involves evaluating the dynamic responses unique to that of a CSP plant as stated in the literature. This involves evaluating the dominant dynamic behaviour, the presence of resonant and anti-resonant modes found within the control bandwidth, and the change in the dynamics of the plant as the plants’ operating points change throughout the day. Once the developed model is validated, characterisation in the form of evaluating the open loop local linear models of the plant is implemented. In order to do so, these models are developed based on model identification processes, which include the use of system identification software such as Matlab® SID Toolbox®. The dominant dynamic behaviour of the plant model, obtained from the developed local linear models, represents that of an over damped second order system that changes as the operating points of the plant change; with the models’ time responses and the bandwidth decreasing and increasing respectively as the thermal energy inputs to the plant increases. The frequency response of the developed local linear models also illustrates the presence of resonant and antiresonant modes found within the control bandwidth of the solar collector field’s temperature response. These modes however are not found to be present in the mechanical power output response of the plant. The use of adaptive control, such as feedforward and gain-scheduled controllers, for the plant should be developed to compensate for the dynamic behaviours associated with that of a CSP plant. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2015

Concentrated solar power (CSP) is an efficient means of renewable energy that makes use of solar radiation to produce electricity instead of making use of conventional fossil fuel techniques such as burning coal. The aim of this study is the simulation and characterisation of a CSP plant in order to gain a better understanding of the dominant plant dynamics. Due to the nature of the study, the dissertation is divided into two main parts namely the simulation of a CSP plant model and the characterisation of the plant model. Modelling the CSP plant takes the form of developing an accurate Flownex® model of a 40 MW combined cycle CSP plant. The model includes thermal energy storage as well as making use of a duct burner. The Flownex® model is based on an existing TRNSYS model of the same plant. The Flownex® model is verified and validated, by making use of a bottom-up approach, to ensure that the developed model is in fact correct. The characterisation part of this dissertation involves evaluating the dynamic responses unique to that of a CSP plant as stated in the literature. This involves evaluating the dominant dynamic behaviour, the presence of resonant and anti-resonant modes found within the control bandwidth, and the change in the dynamics of the plant as the plants’ operating points change throughout the day. Once the developed model is validated, characterisation in the form of evaluating the open loop local linear models of the plant is implemented. In order to do so, these models are developed based on model identification processes, which include the use of system identification software such as Matlab® SID Toolbox®. The dominant dynamic behaviour of the plant model, obtained from the developed local linear models, represents that of an over damped second order system that changes as the operating points of the plant change; with the models’ time responses and the bandwidth decreasing and increasing respectively as the thermal energy inputs to the plant increases. The frequency response of the developed local linear models also illustrates the presence of resonant and antiresonant modes found within the control bandwidth of the solar collector field’s temperature response. These modes however are not found to be present in the mechanical power output response of the plant. The use of adaptive control, such as feedforward and gain-scheduled controllers, for the plant should be developed to compensate for the dynamic behaviours associated with that of a CSP plant. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2015

In this thesis, black-box models describing the quadcopter system dynamics for attitude control have been estimated using closed-loop data. A quadcopter is a naturally unstable multiple input multiple output (MIMO) system and is therefore an interesting platform to test and evaluate ideas in system identification and control theory on. The estimated attitude models have been shown to explain the output signals well enough during simulations to properly tune a PID controller for outdoor flight purposes. With data collected in closed loop during outdoor flights, knowledge about the controller and IMU measurements, three decoupled models have been estimated for the angles and angular rates in roll, pitch and yaw. The models for roll and pitch have been forced to have the same model structure and orders since this reflects the geometry of the quadcopter. The models have been validated by simulating the closed-loop system where they could explain the output signals well. The estimated models have then been used to design attitude controllers to stabilize the quadcopter around the hovering state. Three PID controllers have been implemented on the quadcopter and evaluated in simulation before being tested during both indoor and outdoor flights. The controllers have been shown to stabilize the quadcopter with good reference tracking. However, the performance of the pitch controller could be improved further as there have been small oscillations present that may indicate a stronger correlation between the roll and pitch channels than assumed.

In system identification, different methods are often classified as parametric or non-parametric methods. For parametric methods, a parametric model of a system is considered and the model parameters are estimated. For non-parametric methods, no parametric model is used and the result of the identification is given as a curve or a function. One of the non-parametric methods is the impulse response analysis. This approach is dynamic simulation. This thesis introduces a new paradigm for dynamic simulation, called impulse-based simulation. This approach is based on choosing a Dirac function as input, and as a result, the output will be equal to the impulse response. However, a Dirac function cannot be realized in practice, and an approximation has to be used. As a consequence, the output will deviate from the impulse response. Once the impulse response is estimated, a parametric model can be fitted to the estimation. This thesis aims to determine the parameters in a parametric model from an estimated impulse response. The process of investigating the models is a critical aspect of the project. Correlation analysis is used to obtain the weighting function from the estimates of covariance functions. Later, a relation formed between the parameters and the estimates (obtained by correlation analysis) in the form of a linear system of equations. Furthermore, simulations are carried out using Monte Carlo for investigating the properties of the two step approach, which involves in correlation analysis to find h-parameters and least squares and total least squares methods to solve for the parameters of the model. In order to evaluate the complete capability of the approach to the noise variation a study of signal to noise ratio and mean, mean square error and variances of the estimated parameters is carried out. The results of the Monte Carlo study indicate that two-step approach can give rather accurate parameter estimates. In addition, the least squares and total least squares methods give similar results.

A linear model of an annealing furnace is developed using a black-box system identification approach, and used when testing three different control strategies to improve temperature control. The purpose of the investigation was to see if it was possible to improve the temperature control while at the same time  decrease the switching frequency of the  burners. This will lead to a more efficient process as well as less maintenance, which has both economic and environmental benefits. The estimated model has been used to simulate the furnace with both the existing controller and possible new controllers such as a split range controller and a model predictive controller (MPC). A split range controller is a control strategy which can be used when more than one control signal affect the output signal, and the control signals have different range. The main advantage with MPC is that it can take limitations and constraints into account for the controlled process, and with the use of integer programming, explicitly account for the discrete switching behavior of the burners. In simulation both new controllers succeed in decreasing the switching and the MPC also improved the temperature control. This suggest that the control of the furnace can be improved by implementing one of the evaluated controllers.

With an ongoing desire to improve product performance, in combination with the continuously growing complexity of engineering structures, there is a need for well-tested and reliable engineering tools that can aid the decision making and facilitate an efficient and effective product development. The technical assessment of the dynamic characteristics of mechanical systems often relies on linear analysis techniques which are well developed and generally accepted. However, sometimes the errors due to linearization are too large to be acceptable, making it necessary to take nonlinear effects into account. Many existing analysis techniques for nonlinear mechanical systems build on the assumption that the input excitation of the system is periodic and deterministic. This often results in highly inefficient analysis procedures when nonlinear mechanical systems are studied in a non-deterministic environment where the excitation of the system is stochastic. The aim of this thesis is to develop and validate new efficient analysis methods for the theoretical and experimental study of nonlinear mechanical systems under stochastic excitation, with emphasis on two specific problem areas; forced response simulation and system identification from measurement data. A fundamental concept in the presented methodology is to model the nonlinearities as external forces acting on an underlying linear system, and thereby making it possible to use much of the linear theories for simulation and identification. The developed simulation methods utilize a digital filter to achieve a stable and condensed representation of the linear subparts of the system which is then solved recursively at each time step together with the counteracting nonlinear forces. The result is computationally efficient simulation routines, which are particularly suitable for performance predictions when the input excitation consist of long segments of discrete data representing a realization of the stochastic excitation of the system. Similarly, the presented identification methods take advantage of linear Multiple-Input-Multiple-Output theories for random data by using the measured responses to create artificial inputs which can separate the linear system from the nonlinear parameters. The developed methods have been tested with extensive numerical simulations and with experimental test rigs with promising results. Furthermore, an industrial case study of a wave energy converter, with nonlinear characteristics, has been carried out and an analysis procedure capable of evaluating the performance of the system in non-deterministic ocean waves is presented.

<p>The purpose of this thesis work was to develop an automatic fault detection system for surveillance of bearing temperature in hydropower turbines. The parameters used except the bearing temperature were cooling water temperature and cooling water flow. A simple static model based on data sampled every minute was developed to estimate the bearing temperature. Then a detector for detection of change in bearing temperature based on the CUSUM-algorithm was designed. Since the amount of data was very small the developed model was too uncertain to be used in a working system.</p><p>The designed fault detection system showed to work well for the available data. It is, however, recommended that the performance of the system should be evaluated using more data. Another model based on data sampled once every minute for at least a year has to be developed before the system can be fully evaluated. The results shown were:</p><p>• The fault detection system can discover fast and slow changes in bearing temperature.</p><p>• No false alarms were given for measuring faults and sensor faults of the types used in this thesis. If a measuring fault occurs for too long there will be an alarm.</p><p>The fault detection algorithm was also implemented in Delphi to be used in a working system over the Internet where for example trends and alarms will be presented.</p> / <p>Syftet med examensarbetet var att utveckla ett automatiskt feldetekteringssystem för övervakning av bärlagertemperaturen i vattenkraftturbiner. De ingående parametrarna förutom bärlagertemperaturen var kylvattentemperaturen och kylvattenflödet. En enkel statisk modell baserad på data samplat en gång per minut togs fram för att estimera bärlagertemperaturen. Därefter utvecklades en detektor för att upptäcka avvikelser i bärlagertemperaturen baserad på CUSUM-algoritmen. På grund av en för liten mängd data var den framtagna modellen alltför osäker för att kunna implementeras i ett fungerande system.</p><p>Det framtagna feldetekteringssystemet visade sig fungera bra för de data som fanns tillgängliga. Det är däremot rekommenderat att utvärdera systemets prestanda med längre dataserier. En ytterligare modell baserad på minutdata över ett år måste tas fram innan systemet kan fungera på riktigt. De resultat som erhölls var:</p><p>• Feldetekteringssystemet klarar av att upptäcka abrupta och långsamma avvikelser av bärlagertemperaturen.</p><p>• Inga falsklarm ges då det är enstaka mätfel eller givarfel av sådan typ som tagits upp i arbetet. Pågår ett mätfel alltför länge ges dock ett larm.</p><p>Feldetekteringsalgoritmen implementerades även i Delphi för att kunna användas i ett fungerande system över Internet där t.ex. trendkurvor och larmsignaler skall kunna presenteras.</p>

This thesis presents the development of self adaptive flight controller for an unmanned helicopter system under hovering manoeuvre. The neural network (NN) based model predictive control (MPC) approach is utilised in this work. We use this controller due to its ability to handle system constraints and the time varying nature of the helicopter dynamics. The non-linear NN based MPC controller is known to produce slow solution convergence due to high computation demand in the optimisation process. To solve this problem, the automatic flight controller system is designed using the NN based approximate predictive control (NNAPC) approach that relies on extraction of linear models from the non-linear NN model at each time step. The sequence of control input is generated using the prediction from the linearised model and the optimisation routine of MPC subject to the imposed hard constraints. In this project, the optimisation of the MPC objective criterion is implemented using simple and fast computation of the Hildreth's Quadratic Programming (QP) procedure. The system identification of the helicopter dynamics is typically performed using the time regression network (NNARX) with the input variables. Their time lags are fed into a static feed-forward network such as the multi-layered perceptron (MLP) network. NN based modelling that uses the NNARX structure to represent a dynamical system usually requires a priori knowledge about the model order of the system. Low model order assumption generally leads to deterioration of model prediction accuracy. Furthermore, massive amount of weights in the standard NNARX model can result in an increased NN training time and limit the application of the NNARX model in a real-time application. In this thesis, three types of NN architectures are considered to represent the time regression network: the multi-layered perceptron (MLP), the hybrid multi-layered perceptron (HMLP) and the modified Elman network. The latter two architectures are introduced to improve the training time and the convergence rate of the NN model. The model structures for the proposed architecture are selected using the proposed Lipschitz coefficient and k-cross validation methods to determine the best network configuration that guarantees good generalisation performance for model prediction. Most NN based modelling techniques attempt to model the time varying dynamics of a helicopter system using the off-line modelling approach which are incapable of representing the entire operating points of the flight envelope very well. Past research works attempt to update the NN model during flight using the mini-batch Levenberg-Marquardt (LM) training. However, due to the limited processing power available in the real-time processor, such approaches can only be employed to relatively small networks and they are limited to model uncoupled helicopter dynamics. In order to accommodate the time-varying properties of helicopter dynamics which change frequently during flight, a recursive Gauss-Newton (rGN) algorithm is developed to properly track the dynamics of the system under consideration. It is found that the predicted response from the off-line trained neural network model is suitable for modelling the UAS helicopter dynamics correctly. The model structure of the MLP network can be identified correctly using the proposed validation methods. Further comparison with model structure selection from previous studies shows that the identified model structure using the proposed validation methods offers improvements in terms of generalisation error. Moreover, the minimum number of neurons to be included in the model can be easily determined using the proposed cross validation method. The HMLP and modified Elman networks are proposed in this work to reduce the total number of weights used in the standard MLP network. Reduction in the total number of weights in the network structure contributes significantly to the reduction in the computation time needed to train the NN model. Based on the validation test results, the model structure of the HMLP and modified Elman networks are found to be much smaller than the standard MLP network. Although the total number of weights for both of the HMLP and modified Elman networks are lower than the MLP network, the prediction performance of both of the NN models are on par with the prediction quality of the MLP network. The identification results further indicate that the rGN algorithm is more adaptive to the changes in dynamic properties, although the generalisation error of repeated rGN is slightly higher than the off-line LM method. The rGN method is found capable of producing satisfactory prediction accuracy even though the model structure is not accurately defined. The recursive method presented here in this work is suitable to model the UAS helicopter in real time within the control sampling time and computational resource constraints. Moreover, the implementation of proposed network architectures such as the HMLP and modified Elman networks is found to improve the learning rate of NN prediction. These positive findings inspire the implementation of the real time recursive learning of NN models for the proposed MPC controller. The proposed system identification and hovering control of the unmanned helicopter system are validated in a 6 degree of freedom (DOF) safety test rig. The experimental results confirm the effectiveness and the robustness of the proposed controller under disturbances and parameter changes of the dynamic system.

A novel system identification (SI)-based structural health assessment (SHA) procedure has been developed integrating several theoretical and implementation aspects. The procedure assesses health of structures using limited noise-contaminated dynamic responses and without using input excitation information. Since most practical structures are three dimensional (3D), the procedure has been developed for general 3D structures, represented by finite elements (FEs). The procedure identifies defects by tracking the changes in the stiffness of the elements in the FE representation. Once a defective element is identified, defect spot can be identified accurately within the defective element. The procedure is denoted as 3D Generalized Iterative Least-Squares Extended Kalman Filter with Unknown Input (3D GILS-EKF-UI) and implemented in two stages. In Stage 1, based on the available responses, substructure(s) are selected and the 3D GILS-UI procedure is used to generate the unknown input excitation, stiffness parameters of the elements in the substructure, and two Rayleigh damping coefficients. Using information from Stage 1, stiffness parameters for the whole structure are identified using EKF with Weighted Global Iteration (EKF-WGI) in Stage 2. The procedure accurately identified defect-free and defective states of various 3D structures using only analytically generated limited responses. To increase the robustness, 3D GILS-EKF-UI has been extended to develop an integrated structural health assessment strategy, denoted as Iterative Least-Squares Extended Kalman Filter with Unknown Input and Advanced Digital Integration Technique (ILS-EKF-UI-ADIT). The procedure has been implemented in three stages. In Stage 1, an advanced digital integration technique (ADIT) is implemented for post-processing of noise-contaminated acceleration time-histories, addressing all major challenges of digital integration. It also overcomes non-convergence issue in Stage 2 that arises due to phase-shift and amplitude errors. In Stage 2, substructure(s) are identified using the least-squares procedure. In Stage 3, stiffness parameters for the whole structure are identified using the EKF-WGI procedure. ILS-EKF-UI-ADIT has been verified in presence of relatively large noise in the acceleration time-histories, measured at small part(s) of defect-free and defective structures, without using excitation information. The SHA procedure is robust and has the potential to be applied for the health assessment, maintenance, retrofitting, and life extension of existing structural systems.

Parameterised analytical models that describe the trimmed inflight behaviour of classical aircraft have been studied and are widely accepted by the flight dynamics community. Therefore, the primary role of aircraft parameter estimation is to quantify the parameter values which make up the models and define the physical relationship of the air vehicle with respect to its local environment. Nevertheless, a priori empirical predictions dependent on aircraft design parameters also exist, and these provide a useful means of generating preliminary values predicting the aircraft behaviour at the design stage. However, at present the only feasible means that exist to actually prove and validate these parameter values remains to extract them through physical experimentation either in a wind-tunnel or from a flight test. With the advancement of UAVs, and in particular smaller UAVs (less than 1m span) the ability to fly the full scale vehicle and generate flight test data presents an exciting opportunity. Furthermore, UAV testing lends itself well to the ability to perform rapid prototyping with the use of COTS equipment. Real-time system identification was first used to monitor highly unstable aircraft behaviour in non-linear flight regimes, while expanding the operational flight envelope. Recent development has focused on creating self-healing control systems, such as adaptive re-configurable control laws to provide robustness against airframe damage, control surface failures or inflight icing. In the case of UAVs real-time identification, would facilitate rapid prototyping especially in low-cost projects with their constrained development time. In a small UAV scenario, flight trials could potentialy be focused towards dynamic model validation, with the prior verification step done using the simulation environment. Furthermore, the ability to check the estimated derivatives while the aircraft is flying would enable detection of poor data readings due to deficient excitation manoeuvres or atmospheric turbulence. Subsequently, appropriate action could then be taken while all the equipment and personnel are in place. This thesis describes the development of algorithms in order to perform online system identification for UAVs which require minimal analyst intervention. Issues pertinent to UAV applications were: the type of excitation manoeuvers needed and the necessary instrumentation required to record air-data. Throughout the research, algorithm development was undertaken using an in-house Simulink© model of the Aerosonde UAV which provided a rapid and flexible means of generating simulated data for analysis. In addition, the algorithms were further tested with real flight test data that was acquired from the Cranfield University Jestream-31 aircraft G-NFLA during its routine operation as a flying classroom. Two estimation methods were principally considered, the maximum likelihood and least squares estimators, with the aforementioned found to be best suited to the proposed requirements. In time-domain analysis reconstruction of the velocity state derivatives ˙W and ˙V needed for the SPPO and DR modes respectively, provided more statistically reliable parameter estimates without the need of a α- or β- vane. By formulating the least squares method in the frequency domain, data issues regarding the removal of bias and trim offsets could be more easily addressed while obtaining timely and reliable parameter estimates. Finally, the importance of using an appropriate input to excite the UAV dynamics allowing the vehicle to show its characteristics must be stressed.