Filtrage non linéaire pour la conception d'un brouilleur intelligent

Cet article traite de la conception d'un brouilleur "intelligent" dans le cas où les signaux émis sont non linéaires. Le terme "intelligent" est employé car le brouilleur doit être capable de suivre l'évolution des caractéristiques du signal à brouiller. En effet, ce brouilleur doit simultanément émettre un signal de brouillage (de fort niveau) et estimer les caractéristiques du signal utile (de faible niveau). La solution originale retenue est basée sur l'utilisation d'une séquence de brouillage à faible pouvoir d'excitation. Cette solution a été validée sur signaux sonar sous-marins par une procédure de traitement non linéaire des données montrant la faisabilité de la conception d'un brouilleur "intelligent"

Nonlinear Dynamic System Identification and Model Predictive Control Using Genetic Programming

During the last century, a lot of developments have been made in research of complex nonlinear process control. As a powerful control methodology, model predictive control (MPC) has been extensively applied to chemical industrial applications. Core to MPC is a predictive model of the dynamics of the system being controlled. Most practical systems exhibit complex nonlinear dynamics, which imposes big challenges in system modelling. Being able to automatically evolve both model structure and numeric parameters, Genetic Programming (GP) shows great potential in identifying nonlinear dynamic systems. This thesis is devoted to GP based system identification and model-based control of nonlinear systems. To improve the generalization ability of GP models, a series of experiments that use semantic-based local search within a multiobjective GP framework are reported. The influence of various ways of selecting target subtrees for local search as well as different methods for performing that search were investigated; a comparison with the Random Desired Operator (RDO) of Pawlak et al. was made by statistical hypothesis testing. Compared with the corresponding baseline GP algorithms, models produced by a standard steady state or generational GP followed by a carefully-designed single-objective GP implementing semantic-based local search are statistically more accurate and with smaller (or equal) tree size, compared with the RDO-based GP algorithms. Considering the practical application, how to correctly and efficiently apply an evolved GP model to other larger systems is a critical research concern. Currently, the replication of GP models is normally done by repeating other’s work given the necessary algorithm parameters. However, due to the empirical and stochastic nature of GP, it is difficult to completely reproduce research findings. An XML-based standard file format, named Genetic Programming Markup Language (GPML), is proposed for the interchange of GP trees. A formal definition of this standard and details of implementation are described. GPML provides convenience and modularity for further applications based on GP models. The large-scale adoption of MPC in buildings is not economically viable due to the time and cost involved in designing and adjusting predictive models by expert control engineers. A GP-based control framework is proposed for automatically evolving dynamic nonlinear models for the MPC of buildings. An open-loop system identification was conducted using the data generated by a building simulator, and the obtained GP model was then employed to construct the predictive model for the MPC. The experimental result shows GP is able to produce models that allow the MPC of building to achieve the desired temperature band in a single zone space

The design of periodic excitations for dynamic system identification

System identification techniques are developed for modelling linear and nonlinear systems. The main results of the work are concerned with the design and utilisation of periodic perturbation signals in general areas of time- and frequency-domain system identification. A design strategy is given for a new class of perturbation signals, together with examples of their use in system identification applications. Signal processing procedures are developed for the practical treatment of drift disturbances and transient effects, and also for the detection of nonlinear contributions to the measurement data. The techniques rely completely on the periodicity of the excitation, and so the advantageous properties of periodic input signals are considered in detail. The use of periodic excitations in discrete- and continuous-time nonlinear system identification is also reported, with the identification methods illustrating the worth of frequency-domain measurements in this area. An automatic tuning procedure for PID controllers is also developed, which illustrates an application of system identification techniques to control problems

Data driven nonlinear dynamic models for predicting heavy-duty diesel engine torque and combustion emissions

Diesel engines' reliable and durable structures, high torque generation capabilities at low speeds, and fuel consumption efficiencies make them irreplaceable for heavy-duty vehicles in the market. However, ine ciencies in the combustion process result in the release of emissions to the environment. In addition to the restrictive international regulations for emissions, the competitive demands for more powerful engines and increasing fuel prices obligate heavy-duty engine and vehicle manufacturers to seek for solutions to reduce the emissions while meeting the performance requirements. In line with these objectives, remarkable progress has been made in modern diesel engine systems such as air handling, fuel injection, combustion, and after-treatment. However, such systems utilize quite sophisticated equipment with a large number of calibratable parameters that increases the experimentation time and effort to find the optimal operating points. Therefore, a dynamic model-based transient calibration is required for an e cient combustion optimization which obeys the emission limits, and meets the desired power and efficiency requirements. This thesis is about developing optimizationoriented high delity nonlinear dynamic models for predicting heavy-duty diesel engine torque and combustion emissions. Contributions of the thesis are: (i) A new design of experiments is proposed where air-path and fuel-path input channels are excited by chirp signals with varying frequency pro les in terms of the number and directions of the sweeps. The proposed approach is a strong alternative to the steady-state experiment based approaches to reduce the testing time considerably and improve the modeling accuracy in both steady-state and transient conditions. (ii) A nonlinear nite impulse response (NFIR) model is developed to predict indicated torque by including the estimations of friction, pumping and inertia torques in addition to the torque measured from the engine dynamometer. (iii) Two different nonlinear autoregressive with exogenous input (NARX) models are proposed to predict NOx emissions. In the first structure, input regressor set for the nonlinear part of the model is reduced by an orthogonal least square (OLS) algorithm to increase the robustness and decrease the sensitivity to parameter changes, and linear output feedback is employed. In the second structure, only the previous output is used as the output regressor in the model due to the stability considerations. (iv) An analysis of model sensitivities to parameter changes is conducted and an easy-tointerpret map is introduced to select the best modeling parameters with limited testing time in powertrain development. (v) Soot (particulated matter) emission is predicted using LSTM type networks which provide more accurate and smoother predictions than NARX models. Experimental results obtained from the engine dynamometer tests show the e ectiveness of the proposed models in terms of prediction accuracies in both NEDC (New European Driving Cycle) and WHTC (World Harmonized Transient Cycle) cycle

Structural and seismic monitoring of historical and contemporary buildings: general principles and applications

Structural Health Monitoring (SHM) indicates the continuous or periodic assessment of the conditions of a structure or a set of structures using information from sensor systems, integrated or autonomous, and from any further operation that is aimed at preserving structural integrity. SHM is a broad and multidisciplinary field, both for the spectrum of sciences and technologies involved and for the variety of applications. The technological developments that have made the advancement of this discipline possible come from many fields, including physics, chemistry, materials science, biology, but above all aerospace, civil, electronic and mechanical engineering. The first applications, at the turn of the sixties and seventies, concerned the integrity control of remote structural elements, such as foundation piles and submerged parts of off-shore platforms, but nowadays this type of monitoring is practiced on airplanes, vehicles spacecraft, ships, helicopters, automobiles, bridges, buildings, civil infrastructure, power plants, pipelines, electronic systems, manufacturing and processing facilities, and biological systems. This paper carries out an extensive examination of the theoretical and applicative foundations of structural and seismic monitoring, focusing in particular on methods that exploit natural vibrations and their use both in the diagnosis and in the prediction of the seismic response of civil structures, infrastructure networks, and traditional and modern architectural heritage

Computational modelling of the human motor control system: Nonlinear enhancement of the adaptive model theory through simulation and experiment

Adaptive Model Theory (AMT) proposes that the brain forms and adaptively maintains inverse models of the world around it for adaptive feedforward control. This leading motor control theory unites principles of neurobiology, psychology and engineering. A modified version of AMT was developed with the capacity to control nonlinear systems, to predict signals with nonlinear statistical characteristics, and to perform simultaneous feedback and feedforward adaptive control. The modified version is called nonlinear Adaptive Model Theory or nAMT. An experimental study was also performed investigating inverse model formation in the human motor control system, the results of which were then compared with the nAMT model. A nonlinear dynamic system identification method was developed for nAMT to replace the linear structures employed by AMT. This method employs a neurobiologically-inspired locally-recurrent neural-network structure. A multi-layer adaptation algorithm was also developed specifically for this structure. Nonlinear AutoRegressive Moving-Average (NARMA) adaptive predictor structures replace the linear Moving Average (MA) predictor circuits used in AMT. Adaptive feedback control is augmented using a nonlinear dynamic forward model observer to improve the quality of the estimated response signal. Nonlinear dynamic inverse models are formed by placing the forward model in an internal feedback loop in which the gain function is adjusted to maintain stability. The internal inverse model motor-control hypothesis was tested experimentally in a study looking at human open-loop performance in a tracking task. The study was aimed at directly demonstrating the formation of an internal inverse model of a novel visuomotor relationship for feedforward control in the brain. The study involved 20 normal adult subjects who performed a pursuit random tracking task with a steering wheel for input. During learning the response cursor was periodically blanked, removing all feedback about the external system (i.e., about the relationship between hand motion and response cursor motion). Results showed a transfer of learning from the unblanked runs to the blanked runs for a static nonlinear system (14% median improvement between first 4 and last 4 runs, p = .001) thereby demonstrating adaptive feedforward control in the nervous system. No such transfer was observed for a dynamic linear system, indicating a dominant adaptive feedback control component. The observed open-loop responses showed a high-pass frequency response which could not be explained using traditional control-systems motor control models. Experimental results were compared with simulated results from the nAMT model. Results from the experimental study were used to verify and tune the computational model. The resulting simulations produced effects that mirrored the closed- and openloop characteristics of the experimental response trajectories. This supports the claim that an internal feedback loop is used for the inversion of external systems in the human brain. Other control-systems models (both AMT and feedback-error learning) would require substantial ad hoc modification to reproduce the observed disparity between closed- and open-loop results. In contrast, nAMT naturally reproduced the effect as a consequence of its novel nonlinear inversion method. In nAMT an inverse model is formed by embedding a forward model in an internal feedback loop incorporating a low derivative gain. The derivative loop-gain caused the inverse model to be relatively inaccurate at low frequencies, for which the feedback control loop was adequate, but to be increasingly accurate at higher frequencies. Maintenance of the loop-gain at the lowest possible levels maximizes the internal stability of the inverse. The simulation work confirmed that the nAMT model is capable of reproducing human behaviour under a wide range of conditions

Process identification using second order Volterra models for nonlinear model predictive control design of flotation circuits

The control of flotation circuits is a complicated problem, since flotation circuits are nonlinear multivariable processes with a significant degree of interaction between the variables. Isolated PID controllers usually do not perform adequately. The application of a nonlinear model predictive algorithm based on second order Volterra models was investigated. Volterra series models are a higher order extension of linear impulse response models. The nonlinear model predictive control algorithm can also be seen as a linear model predictive controller with higher order correction terms. A dynamic model of a flotation circuit based on the governing continuity equations was developed. The responses obtained represented the qualitative relationships between the model inputs and the controlled variables. This model exhibited strong nonlinearities, including asymmetrical responses to symmetrical inputs and gain sign changes. This dynamic model was treated as the plant to be identified and from which second order Volterra models were obtained. Full Volterra models required excessively large data sets, but significant reductions in the size of the required data set could be achieved if some of the second order coefficients were constrained to zero. These "pruned" Volterra models represented the plant dynamics significantly better than linear models. In particular, these second order Volterra models were able to model asymmetrical responses including gain sign changes. A special case of "pruned" second order Volterra models are diagonal second order models, where all the off-diagonal coefficients (hij where i ≠ j) are constrained to zero. These models required less data than pruned Volterra models containing off-diagonal coefficients, but were less accurate. The performance of nonlinear model predictive controllers based on a pruned second order and diagonal second order Volterra models was evaluated. The performance of these controllers was also compared to the performance obtained with a first order (linear) Volterra model. All three controllers gave equivalent results for large manipulated variable weights. However, when the controllers were tuned more aggressively, results obtained from the three controllers differed considerably. The pruned nonlinear controller performed well even when tuned aggressively while the performance of the linear controller deteriorated. For the case of disturbance rejection, the linear controller performed slightly better than the nonlinear controllers.Dissertation (MEng (Control Engineering))--University of Pretoria, 2006.Chemical Engineeringunrestricte

Mitigation of nonlinear receiver effects in modern radar: advanced signal processing techniques

This thesis presents a study into nonlinearities in the radar receiver and investigates advanced digital signal processing (DSP) techniques capable of mitigating the resultant deleterious effects. The need for these mitigation techniques has become more prevalent as the use of commercial radar sensors has increased rapidly over the last decade. While advancements in low-cost radio frequency (RF) technologies have made mass-produced radar systems more feasible, they also pose a significant risk to the functionality of the sensor. One of the major compromises when employing low-cost commercial off-theshelf (COTS) components in the radar receiver is system linearity. This linearity trade-off leaves the radar susceptible to interfering signals as the RF receiver can now be driven into the weakly nonlinear regime. Radars are not designed to operate in the nonlinear regime as distortion is observed in the radar output if they do. If radars are to maintain operational performance in an RF environment that is becoming increasingly crowded, novel techniques that allow the sensor to operate in the nonlinear regime must be developed. Advanced DSP techniques offer a low-cost low-impact solution to the nonlinear receiver problem in modern radar. While there is very little work published on this topic in the radar literature, inspiration can be taken from the related field of communications where techniques have been successfully employed. It is clear from the communications literature that for any mitigation algorithm to be successful, the mechanisms driving the nonlinear distortion in the receiver must be understood in great detail. Therefore, a behavioural modelling technique capable of capturing both the nonlinear amplitude and phase effects in the radar receiver is presented before any mitigation techniques are studied. Two distinct groups of mitigation algorithms are then developed specifically for radar systems with their performance tested in the medium pulse repetition frequency (MPRF) mode of operation. The first of these is the look-up table (LUT) approach which has the benefit of being mode independent and computationally inexpensive to implement. The limitations of this communications-based technique are discussed with particular emphasis placed on its performance against receiver nonlinearities that exhibit complex nonlinear memory effects. The second group of mitigation algorithms to be developed is the forward modelling technique. While this novel technique is both mode dependent and computationally intensive to implement, it has a unique formalisation that allows it to be extended to include nonlinear memory effects in a well-defined manner. The performance of this forward modelling technique is analysed and discussed in detail. It was shown in this study that nonlinearities generated in the radar receiver can be successfully mitigated using advanced DSP techniques. For this to be the case however, the behaviour of the RF receiver must be characterised to a high degree of accuracy both in the linear and weakly nonlinear regimes. In the case where nonlinear memory effects are significant in the radar receiver, it was shown that memoryless mitigation techniques can become decorrelated drastically reducing their effectiveness. Importantly however, it was demonstrated that the LUT and forward modelling techniques can both be extended to compensate for complex nonlinear memory effects generated in the RF receiver. It was also found that the forward modelling technique dealt with the nonlinear memory effects in a far more robust manner than the LUT approach leading to a superior mitigation performance in the memory rich case

New Methods for Analysis of Nonlinear Systems in the Frequency Domain with Applications in Condition Monitoring and Engineering Systems

The study of nonlinear systems has received great attention in recent years because of the necessity of dealing with practical problems that cannot be modelled by linear representations. Although the availability of greater computational power and advances in the field of system identification have allowed significant progresses towards modelling real world processes, a systematic method for understanding the systems characteristics is still an open problem. In this context, as has been demonstrated in many studies, the extension of the well-known concept of linear Frequency Response Function (FRF) to nonlinear systems are a significant potential solution. The condition monitoring problem is closely associated with the analysis of systems characteristics and can therefore be considered as part of this scenario. Modern industrial processes have grown significantly in both size and complexity, creating the demand for automatic systems that can aid human operators in the important task of recognising when the process is experiencing malfunctions. Although this problem has been studied from the perspective of a wide scope of disciplines, such as modelling, signal processing, intelligent systems and statistical analysis, in many cases, data oriented methods or generic problem solvers (such as neural networks) often have to be applied. This is because complicated system behaviours are often difficult to interpret so as to associate them with possible faulty conditions.nonlinear system analysis in the frequency domain, and studies the application of these new methods for solving condition monitoring problems. The principle is based on the idea that a nonlinear system formulation can be used to deal with situations of practical interest where nonlinear behaviour cannot be neglected and that the frequency domain analysis approach can be applied to conduct an in-depth study of the system properties for the purpose of characterising systems faulty behaviours. In order to apply this principle, several issues need to be addressed, including the evaluation of the frequency characteristics of nonlinear systems and the generation of useful features that allow an effective characterisation of faulty system conditions. Motivated by these needs, the following research studies are conducted in this thesis: In order to address these challenges, this thesis proposes new methods for nonlinear system analysis in the frequency domain, and studies the application of these new methods for solving condition monitoring problems. The principle is based on the idea that a nonlinear system formulation can be used to deal with situations of practical interest where nonlinear behaviour cannot be neglected and that the frequency domain analysis approach can be applied to conduct an in-depth study of the system properties for the purpose of characterising systems faulty behaviours. In order to apply this principle, several issues need to be addressed, including the evaluation of the frequency characteristics of nonlinear systems and the generation of useful features that allow an effective characterisation of faulty system conditions. Motivated by these needs, the following research studies are conducted in this thesis: 1 - Development of new methods that allow an eficient extraction of the frequency domain representations of nonlinear systems, namely, Generalised Frequency Response Functions (GFRFs) and Nonlinear Output Frequency Response Functions (NOFRFs). The thesis first derives a comprehensive methodology that allows an efficient and systematic extraction of GFRFs from a polynomial NARX (Nonlinear Auto-Regressive with eXogenous inputs) model. Then the same idea is used for addressing issues regarding the computation of NOFRFs, providing efficient algorithms that allow an effective determination of the NORRFs in both numerical and analytical forms. 2- Establishment of a condition monitoring framework based on the new GFRFs/NOFRFs evaluation methods. This framework is constructed over a practical background where physical knowledge about the system is scarce, although process history data is available. In this context, black-box models can be built and the system properties can be extracted by computing the system's GFRFs/NOFRFs via the newly proposed methods. These functions provide fundamental information for deriving useful features that can be used for characterising faults and building effective diagnosis systems. The effectiveness of the proposed methods has been verified by both simulation studies and real data analysis tests, demonstrating the advantage of the new condition monitoring framework for engineering applications. These studies significantly improve current frequency analysis methods for nonlinear systems and, at the same time, provide effective condition monitoring approaches for a wide range of engineering systems

Adaptive torque-feedback based engine control

The aim of this study was to develop a self-tuning or adaptive SI engine controller using torque feedback as the main control variable, based on direct/indirect measurement and estimation techniques. The indirect methods include in-cylinder pressure measurement, ion current measurement, and crankshaft rotational frequency variation. It is proposed that torque feedback would not only allow the operating set-points to be monitored and achieved under wider conditions (including the extremes of humidity and throttle transients), but to actively select and optimise the set-points on the basis of both performance and fuel economy. A further application could allow the use of multiple fuel types and/or combustion enhancing methods to best effect. An existing experimental facility which comprised a Jaguar AJ-V8 SI engine coupled to a Heenan-Froude Dynamatic GVAL (Mk 1) dynamometer was adopted for this work, in order to provide a flexible distributed engine test system comprising a combined user interface and cylinder pressure monitoring system, a functional dynamometer controller, and a modular engine controller which is close coupled to an embedded PC has been created. The considerable challenges involved in creating this system have meant that the core research objectives of this project have not been met. Nevertheless, an open-architecture software and hardware engine controller and independent throttle controller have been developed, to the point of testing. For the purposes of optimum ignition timing validation and combustion knock detection, an optical cylinder pressure measurement system with crank angle synchronous sampling has been developed. The departure from the project’s initial aims have also highlighted several important aspects of eddy-current dynamometer control, whose closed-loop behaviour was modelled in Simulink to study its control and dynamic response. The design of the dynamometer real-time controller was successfully implemented and evaluated in a more contemporary context using an embedded digital controller.EThOS - Electronic Theses Online ServiceSchool of Mechanical & Systems EngineeringNewcastle UniversityGBUnited Kingdo