<p>Nonlinear systems occur in industrial processes, economical systems, biotechnology and in many other areas. The thesis treats methods for system identification and control of such nonlinear systems, and applies the proposed methods to a solar heating/cooling plant. </p><p>Two applications, an anaerobic digestion process and a domestic solar heating system are first used to illustrate properties of an existing nonlinear recursive prediction error identification algorithm. In both cases, the accuracy of the obtained nonlinear black-box models are comparable to the results of application specific grey-box models. Next a convergence analysis is performed, where conditions for convergence are formulated. The results, together with the examples, indicate the need of a method for providing initial parameters for the nonlinear prediction error algorithm. Such a method is then suggested and shown to increase the usefulness of the prediction error algorithm, significantly decreasing the risk for convergence to suboptimal minimum points. </p><p>Next, the thesis treats model based control of systems with input signal dependent time delays. The approach taken is to develop a controller for systems with constant time delays, and embed it by input signal dependent resampling; the resampling acting as an interface between the system and the controller.</p><p>Finally a solar collector field for combined cooling and heating of office buildings is used to illustrate the system identification and control strategies discussed earlier in the thesis, the control objective being to control the solar collector output temperature. The system has nonlinear dynamic behavior and large flow dependent time delays. The simulated evaluation using measured disturbances confirm that the controller works as intended. A significant reduction of the impact of variations in solar radiation on the collector outlet temperature is achieved, though the limited control range of the system itself prevents full exploitation of the proposed feedforward control. The methods and results contribute to a better utilization of solar power.</p>

Nonlinear systems occur in industrial processes, economical systems, biotechnology and in many other areas. The thesis treats methods for system identification and control of such nonlinear systems, and applies the proposed methods to a solar heating/cooling plant. Two applications, an anaerobic digestion process and a domestic solar heating system are first used to illustrate properties of an existing nonlinear recursive prediction error identification algorithm. In both cases, the accuracy of the obtained nonlinear black-box models are comparable to the results of application specific grey-box models. Next a convergence analysis is performed, where conditions for convergence are formulated. The results, together with the examples, indicate the need of a method for providing initial parameters for the nonlinear prediction error algorithm. Such a method is then suggested and shown to increase the usefulness of the prediction error algorithm, significantly decreasing the risk for convergence to suboptimal minimum points. Next, the thesis treats model based control of systems with input signal dependent time delays. The approach taken is to develop a controller for systems with constant time delays, and embed it by input signal dependent resampling; the resampling acting as an interface between the system and the controller. Finally a solar collector field for combined cooling and heating of office buildings is used to illustrate the system identification and control strategies discussed earlier in the thesis, the control objective being to control the solar collector output temperature. The system has nonlinear dynamic behavior and large flow dependent time delays. The simulated evaluation using measured disturbances confirm that the controller works as intended. A significant reduction of the impact of variations in solar radiation on the collector outlet temperature is achieved, though the limited control range of the system itself prevents full exploitation of the proposed feedforward control. The methods and results contribute to a better utilization of solar power.

Ce travail de thèse traite du problème de surveillance en ligne de défaillances électriques dans les entrainements électriques à base de machines synchrones à aimants permanents (MSAP) par une méthode de suivi paramétrique. Les défauts de court-circuit entre spires au stator sont souvent critiques et doivent être détectés au plus tôt avec un bon taux de confiance afin d’informer un système superviseur de la présence d’une défaillance pour limiter les risques encourues par l’environnement matériel et humain situé autour de la machine en défaut. La méthode que nous proposons de mettre en œuvre pour la détection des courts-circuits statoriques est basée des techniques d’identifications récursives. Nous proposons d’identifier en ligne les paramètres d’un modèle diphasé électrique de l’actionneur synchrone et d’analyser les variations des paramètres identifiées lors de l’apparition d’un défaut. Pour assurer les performances des méthodes d’identification, il est souvent nécessaire de disposer d’un signal d’excitation additionnel pour assurer les bonnes performances des algorithmes. Ces signaux peuvent cependant perturber le fonctionnement normal de la machine et entrainer des pertes additionnelles. Dans ce contexte, nous proposons une approche par identification faisant appel à un modèle diphasé spécifique appelé « le repère de Park à courants orientés ». Ce repère permet, tout en réduisant la complexité du problème d’identification, d’obtenir des propriétés d’auto-excitation intéressantes et donc d’éviter l’utilisation d’une excitation additionnelle. Des simulations sont menées à l’aide d’un modèle fin de la machine permettant de reproduire des situations de défaillances de manière virtuelle et d’éprouver l’efficacité des algorithmes dans ces situations dégradées. Cette machine, pouvant fonctionner en générateur ou en moteur, est intégrée dans un environnement complet, incluant le cas échéant une alimentation, une charge mécanique et éventuellement une commande, ce qui permet également de tester les algorithmes pour des fonctionnements en boucle ouverte et en boucle fermée. Les résultats présentés permettent de valider les techniques proposées et montrent qu’elles permettent d’extraire automatiquement, à partir des variations des paramètres identifiés, un indicateur de défaut. Des résultats expérimentaux sont également présentés en fonctionnement générateur sur une machine spécialement re-bobinée pour permettre la réalisation de défaut statoriques. Les algorithmes sont implantés sur une cible de calcul numérique afin de démontrer la faisabilité temps réelle de la détection / This work deals with the on-line monitoring of electrical faults in permanent magnet synchronous machine (PMSM) by parameter monitoring method. The inter-turns short-circuits faults in stator are often critical and have to be detected as early as possible with a high confidence rate to inform the supervisor system of the fault presence in order to limit the risk for the material and human environment. The proposed method is focus on the detection of short-circuits in stator and based on recursive identification technique. The on-line parameter identification uses an electrical diphase model of the PMSM and the analysis of the estimated parameter variations is performed to detect the presence of stator faults. In a general way, to ensure the performance of identification algorithms, it is necessary to have additional excitation signals. Consequently, those signals could disturb the normal operation of the drive. To overcome this problem, a specific diphase model in currents oriented Park reference frame is introduced for identification process. By reducing the complexity of identification problem, this reference frame provides an interesting auto-excitation property that leads to avoid the utilisation of additional excitation signals. The simulations are performed using an accurate model of PMSM that allows reproducing the failure situation and prove the efficiency of algorithms in degraded situations. This machine, operating as generator or motor, is integrated in a complete environment, included a power supply, mechanical load and control process. The detection scheme is then tested in open and closed loop operation. The results obtained from the simulation process underline the ability of the proposed technique to detect a stator fault occurrence and show that a fault indicator can be extracted automatically from the variation of estimated parameters. Experimental results are also achieved. A PMSM, with a specific winding including additional connexion points for stator short-circuit realisation is used. The algorithms are implemented in a numerical calculator in order to demonstrate the feasibility of the real-time faults detection for a generator operation mode

This master's thesis considers adaptive optimal controllers. It shows principles of optimal controllers, recursive identification using least-mean squares method and identification based on neural network.

Simultaneous evaluation of the whole set of the model parameters of different orders together with an ability to track unmodeled dynamics are desired features in the tasks of parameter estimation. A technique handling with the factors produced by an augmented covariance (ACM) or information (AIM) matrices is considered to be an appropriate tool for designing multiple model estimation. This is where the name augmented identification (AI) by using the least-squares method was taken. The method AI attains numerical stability of the calculation of the conventional least squares method while in the same time, fully extracts information contained in the observation. In order to track time varying parameters can be found that all the information pertinent to recursive identification and thus to data driven forgetting is concentrated in ACM as well as in AIM. In this thesis will be introduced how to selective forgetting to ACM should be applied in an effective way. It means forget only a portion of accumulated information which will be further modified by the newest data included in the regressor. In the estimation problems the knowledge of the inner states of the identified system is often required. Because the augmented identification belongs within the class so called prediction error method (PEM), some rational requirements can be deduced. As a result, state filter should constitute optimization procedure minimizing the predicted error of given state space model representation with respect to the vector of states. The proposed scheme will considerably extend the family of algorithms based on processing of ACM (AIM) about augmented filtering (AF). This all will establish a comprehensive concept of parametric estimation that compared with conventional approaches is characterized by versatility, low demands on a priori process information and by excellent numerical properties (robust against overparametrization, capable solving the multiple model problem).

Master’s thesis describes adaptive optimal controller design and it’s settings. Identification with principles of artificial intelligence and recursive least squares identification with exponential and directional forgetting are compared separately and as part of controller. Adaptive optimal controller is tested on physical model and compared with solidly adjusted PSD controller. Possibilities of implementation of adaptive optimal controller into programmable logic controller B&R are show and tested.