Cílem práce byl návrh a implementace řídicích algoritmů pro optimalizaci spotřeby energie elektrického vozidla. Hlavním úkolem byla optimalizace rozložení energie mezi hlavním zdrojem energie (bateriemi) a super-kapacitory v průběhu jízdního cyklu. Jízdní výkonový profil je odhadován a předpovězen na základě 3D geografických souřadnic a matematického modelu vozidla. V první části jsou uvedeny komponenty vozidla a jejich modely. Poté jsou představeny algoritmy na základě klouzavého průměru a dynamického programování. Byly provedeny simulace a analýzy pro demostraci přínosů algoritmů. V poslední části je popsána Java implementace algoritmů a také aplikace pro operační systém Android.

Cílem práce byl návrh a implementace řídicích algoritmů pro optimalizaci spotřeby energie elektrického vozidla. Hlavním úkolem byla optimalizace rozložení energie mezi hlavním zdrojem energie (bateriemi) a super-kapacitory v průběhu jízdního cyklu. Jízdní výkonový profil je odhadován a předpovězen na základě 3D geografických souřadnic a matematického modelu vozidla. V první části jsou uvedeny komponenty vozidla a jejich modely. Poté jsou představeny algoritmy na základě klouzavého průměru a dynamického programování. Byly provedeny simulace a analýzy pro demostraci přínosů algoritmů. V poslední části je popsána Java implementace algoritmů a také aplikace pro operační systém Android.

<p>Multirotor unmanned aerial vehicles (UAVs) added with the ability to physically interact with the environment has opened endless possibilities for aerial mobile manipulation tasks. With the unlimited reachable workspace and physical interaction capabilities, such robots can enhance human ability to perform dangerous and hard-to-reach tasks. However, realizing aerial mobile manipulation in real-world scenarios is challenging with respect to the diversity in aerial platforms, control fidelity and susceptibility to variations in the environment. Therefore, the first part of the dissertation provides tools to  classify and evaluate different multirotor designs. A measure of responsiveness of a multirotor platform in exerting generalized forces and rejecting disturbances is discussed through the control bandwidth analysis. Superiority in control bandwidth for fully-actuated multirotors is established in a comparison with equivalent under-actuated multirotors. To further classify and distinguish multirotor platforms, a new mobility measure is proposed and compared by surveying all aerial platforms employed for aerial mobile manipulation. In compliance to the control bandwidth analysis, the mobility measure for fully-actuated multirotors is relatively higher making them better suited for manipulation tasks. </p> <p><br></p> <p><br></p> <p>Aerial physical interaction, as a part of aerial mobile manipulation, with partially unknown environments is challenging due to the uncertainties imposed while dexterously exerting force signatures. A hybrid physical interaction (HyPhI) controller is proposed to enable constrained force contact with a steady transition from unconstrained motion, by squelching excess energy during initial impact. However, uncertainties posed by the partially unknown environment requires to understand the surrounding environment and their current physical states, that can enhance interaction performance. The limited resources and flight time of the multirotors requires to simultaneously understand the environment and perform aerial physical interactions. Inspection-on-the-fly is an uncanny ability of humans to intuitively infer states during manipulation while reducing the necessity to conduct inspection and manipulation separately. In this dissertation, the inspection-on-the-fly method based HyPhI controller is proposed to engage in a steady contact with partially unknown environments, while simultaneously estimating the physical states of the surfaces. The proposed method is evaluated in a mockup of real-world facility, to understand the surface properties while engaging in steady interactions. Further, such inspection of surfaces and estimation of various states enables a deeper understanding of the environment while enhancing the ability to physically interact. </p>

<p>Предложен контролни алгоритам за инвертор повезан на мрежу<br />омогућава реализацију сваког профила контролисаних електричних<br />величина који је у домену физичких могућности дистрибуираног извора.<br />Другим речима, управљачки програм не обезбеђује остваривање<br />дискретног спектра режима, као што је то до сада био случај са<br />решењима предложеним у научној литератури, већ омогућава сигурну<br />производњу произвољног профила фазних струја и напона. Показано је<br />да предложено решење задовољава најстроже релевантне законске<br />регулативе. У раду је анализиран већи број функционалности и испитан<br />је њихов утицај на процес производње електричне енергије с акцентом на<br />хаваријске режиме. Разматрана је интеракција дистрибуираних изво-ра и<br />дистрибутивне мреже у режимима са поремећајима и приложен је већи<br />број експерименталних резултата прикупљених употребом HIL окружења.</p> / <p>Predložen kontrolni algoritam za invertor povezan na mrežu<br />omogućava realizaciju svakog profila kontrolisanih električnih<br />veličina koji je u domenu fizičkih mogućnosti distribuiranog izvora.<br />Drugim rečima, upravljački program ne obezbeđuje ostvarivanje<br />diskretnog spektra režima, kao što je to do sada bio slučaj sa<br />rešenjima predloženim u naučnoj literaturi, već omogućava sigurnu<br />proizvodnju proizvoljnog profila faznih struja i napona. Pokazano je<br />da predloženo rešenje zadovoljava najstrože relevantne zakonske<br />regulative. U radu je analiziran veći broj funkcionalnosti i ispitan<br />je njihov uticaj na proces proizvodnje električne energije s akcentom na<br />havarijske režime. Razmatrana je interakcija distribuiranih izvo-ra i<br />distributivne mreže u režimima sa poremećajima i priložen je veći<br />broj eksperimentalnih rezultata prikupljenih upotrebom HIL okruženja.</p> / <p>Proposed grid-connected inverter control algorithm enables production of any<br />controlled variables&rsquo; profile that is in the domain of converter&rsquo;s physical<br />capabilities. In other words, the control program does not allow only for a<br />narrow spectrum of different working regimes to be realized, as it was the case<br />previously with the solutions proposed in the literature, but can result in a safe<br />production of arbitrary meaningful phase currents and voltages profiles. It was<br />demonstrated that the proposed solution satisfies the most stringent of the Grid<br />Codes. Furthermore, advanced functionalities not addressed in the Grid Codes<br />were also analysed and the influence of those functionalities&rsquo; application on<br />the process of the energy production during the grid faults was evaluated in<br />the dissertation. Interaction between the distributed generation units and the<br />distribution network was studied and a number of HIL experimental results are<br />provided.</p>

The advances in computational and experimental modeling in the area of structural mechanics have stimulated research in a class of hybrid problems that require both of these modeling capabilities to be combined to achieve certain objectives. Real-time substructure (RTS) testing, structural system identification (SSI) and active control techniques fall in the category of hybrid problems that need efficient tools in both computational and experimental phases for their successful implementation. RTS is a hybrid testing method, which aims to overcome the scaling problems associated with the conventional dynamic testing methods (such as shake table test, effective force test and pseudo dynamic test) by testing the critical part of the structure experimentally with minimum compromise on spatio-temporal scaling, while modeling the remaining part numerically. The problem of SSI constitutes an important component within the broader framework of problems of structural health monitoring where, based on the in-situ measurements on the loading and a subset of critical responses of the structure, the system parameters are estimated with a view to detecting damage/degradation. Active control techniques are employed to maintain the functionality of important structures, especially under extreme dynamic loading. The work reported in the present thesis contributes to the areas of RTS, SSI and active control of nonlinear systems, the main focus being the computational aspects, i.e., in developing numerical strategies to address some of the unsolved issues, although limited efforts have also been made to undertake laboratory experimental investigations in the area of nonlinear SSI. The thesis is organized into seven chapters and five appendices. The first chapter contains an overview of the state of the art techniques in dynamic testing, SSI and structural control. The topics covered include effective force test, pseudo dynamic test, RTS test, time and frequency domain methods of nonlinear system identification, dynamic state estimation techniques with emphasis on particle filters, Rao-Blackwellization, structural control methods, control algorithms and active control of nonlinear systems. The review identifies a set of open problems that are subsequently addressed, to an extent, in the thesis. Chapter 2 focuses on the development of a time domain coupling technique, involving combined computational and experimental modeling, for vibration analysis of structures built-up of linear/nonlinear substructures. The numerical and experimental substructures are allowed to interact in real-time. The equation of motion of the numerical substructure is integrated using a step-by-step procedure that is formulated in the state space. For systems with nonlinear substructures, a multi-step transversal linearization method is used to integrate the equations of motion; and, a multi-step extrapolation scheme, based on the reproducing kernel particle method, is employed to handle the time delays that arise while accounting for the interaction between the substructures. Numerical illustrations on a few low dimensional vibrating structures are presented and these examples are fashioned after problems of seismic qualification testing of engineering structures using RTS testing techniques. The concept of substructuring is extended in Chapter 3 for implementing Rao-Blackwellization, a technique of combining particle filters with analytical computation through Kalman filters, for state and parameter estimations of a class of nonlinear dynamical systems with additive Gaussian process/observation noises. The strategy is based on decomposing the system to be estimated into mutually coupled linear and nonlinear substructures and then putting in place a rational framework to account for coupling between the substructures. While particle filters are applied to the nonlinear substructures, estimation of linear substructures proceeds using a bank of Kalman filters. Numerical illustrations for state/parameter estimations of a few linear and nonlinear oscillators with noise in both the process and measurements are provided to demonstrate the potential of the Rao-Blackwellized particle filter (RBPF) with substructuring. In Chapter 4, the concept of Rao-Blackwellization is extended to handle more general nonlinear systems, using two different schemes of linearization. A semi-analytical filter and a conditionally linearized filter, within the framework of Monte Carlo simulations, are proposed for state and parameter estimations of nonlinear dynamical systems with additively Gaussian process/observation noises. The first filter uses a local linearization of the nonlinear drift fields in the process/observation equations based on explicit Ito-Taylor expansions to transform the given nonlinear system into a family of locally linearized systems. Using the most recent observation, conditionally Gaussian posterior density functions of the linearized systems are analytically obtained through the Kalman filter. In the second filter, the marginalized posterior distribution of an appropriately chosen subset of the state vector is obtained using a particle filter. Samples of these marginalized states are then used to construct a family of conditionally linearized system of equations to obtain the posterior distribution of the states using a bank of Kalman filters. The potential of the proposed filters in state/parameter estimations is demonstrated through numerical illustrations on a few nonlinear oscillators. The problem of active control of nonlinear structural dynamical systems, in the presence of both process and measurement noises, is considered in Chapter 5. The focus of the study is on the exploitability of particle filters for state estimation in feedback control algorithms for nonlinear structures, when a limited number of noisy output measurements are available. The control design is done using the state dependent Riccati equation (SDRE) method. The Bayesian bootstrap filter and another based on sequential importance sampling are employed for state estimation. Numerical illustrations are provided for a few typically nonlinear oscillators of interest in structural engineering. The experimental validation of the RBPF using substructuring (developed in Chapter 3) and the conditionally linearized Monte Carlo filter (developed in Chapter 4), for parameter estimation, is reported in Chapter 6. Measured data available through laboratory experiments on simple building frame models subjected to harmonic base motions is processed using the proposed algorithms to identify the unknown parameters of the model. A brief summary of the contributions made in this thesis, together with a few suggestions for future research, are presented in Chapter 7. Appendix A provides an account of the multi-step transversal linearization method. The derivation of the reproducing kernel shape functions are presented in Appendix B. Appendix C provides the details of the stochastic Taylor expansion and derivation of the covariance structure of Gaussian MSI-s. The performance of a particle filtering algorithm (bootstrap filter) and Kalman filter in the state estimation of a linear system is provided in Appendix D and Appendix E contains the theoretical details of the Rao-Blackwellized particle filter.

This master„s thesis elaborates with EC motors problematic. There are described essential features of brushless DC motors, their principles, construction and methods for rotor position detection. There are mentioned commonly used control algorithms of EC motors including theory of three-phase convertors. This thesis also contains a complex design of the universal convertor for EC motor and its practical implementation. Power parts losses calculation, heat-sink calculation and measurements at the convertor prototype are also described in this thesis.

This thesis deals with the optimal control of PDEs. After a brief introduction in the theory of elliptic and parabolic PDEs, we introduce a software that solves systems of PDEs by the finite elements method. In the second chapter we derive optimality conditions in terms of function spaces, i.e. a systems of PDEs coupled by some pointwise relations. Now we present algorithms to solve the optimality systems numerically and present some numerical test cases. A further chapter deals with the so called lack of adjointness, an issue of gradient methods applied on parabolic optimal control problems. However, since optimal control problems lead to large numerical schemes, model reduction becomes popular. We analyze the proper orthogonal decomposition method and apply it to our model problems. Finally, we apply all considered techniques to a real world problem.:Introduction The state equation Optimal control and optimality conditions Algorithms The \"lack of adjointness\" Numerical examples Efficient solution of PDEs and KKT- systems A real world application Functional analytical basics Codes of the examples

The dynamic wave model (the complete form of the saint-Venant equations), as applied to flow routing in irrigation canals or flood routing in natural channels, is associated with parameter and model uncertainties. The parameter uncertainty arises due to imprecision in the estimation of Manning’s n used for calculating the friction slope (sf) in the momentum equation of the dynamic wave model. Accurate estimation of n is difficult due to its dependence on several channel and flow characteristics. The model uncertainty of the dynamic wave model arises due to difficulty in applying the momentum equation to curved channels, as it is a vector equation. The one-dimensional form of the momentum equation is derived assuming that the longitudinal axis of the channel is a straight line, so that the net force vector is equal to the algebraic sum of the forces involved. Curved channel reaches have to be discretized into small straight sub-reaches while applying the momentum equation. Otherwise, two- or three-dimensional forms of the momentum equation need to be adopted. A main objective of the study presented in the thesis is to develop a fuzzy dynamic wave model (FDWM), which is capable of overcoming the parameter and model uncertainties of the dynamic wave model mentioned above, specifically for problems of flow routing in irrigation canals and flood routing in natural channels. It has been demonstrated earlier in literature that the problem of parameter uncertainty in infiltration models can be addressed by replacing the momentum equation by a fuzzy rule based model while retaining the continuity equation in its complete form. The FDWM is developed by adopting the same methodology: i.e. By replacing the momentum equation of the dynamic wave model by a fuzzy rule based model while retaining the continuity equation in its complete form. The fuzzy rule based model is developed based on fuzzification of a new equation for wave velocity, to account for the model uncertainty and backwater effects. A fuzzy dynamic wave routing model (FDWRM) is developed based on application of the FDWM to flow routing in irrigation canals. The fuzzy rule based model is developed based on the observation that inertia dominated gravity wave predominates in irrigation canal flows. Development of the FDWRM and the method of computation are explained. The FDWRM is tested by applying it to cases of hypothetical flow routing in a wide rectangular channel and also to a real case of flow routing in a field canal. For the cases of hypothetical flow routing in the wide rectangular channel, the FDWRM results match well with those of an implicit numerical model (INM), which solves the dynamic wave model; but the accuracy of the results reduces with increase in backwater effects. For the case of flow routing in the field canal, the FDWRM outputs match well with measured data and also are much better than those of the INM. A fuzzy dynamic flood routing model (FDFRM) is developed based on application of the FDWM to flood routing in natural channels. The fuzzy rule based model is developed based on the observation that monoclinal waves prevail during floods in natural channels. The natural channel reach is discredited into a number of approximately uniform sub-reaches and the fuzzy rule based model for each sub-reach is obtained using the discharge (q)–area (a) relationship at its mean section, based on the kleitz-seddon principle. Development of the FDFRM and the method of computation are explained. The FDFRM is tested by applying it to cases of flood routing in fictitious channels and to flood routing in a natural channel, which is described in the HEC-RAS (hydrologic engineering center – river analysis system) application guide. For the cases of flood routing in the fictitious channels, the FDFRM outputs match well with the INM results. For the case of flood routing in the natural channel, optimized fuzzy rule based models are derived using a neuro-fuzzy algorithm, to take the heterogeneity of the channel sub-reaches into account. The resulting FDFRM outputs are found to be comparable to the HEC-RAS outputs. Also, in literature, the dynamic wave model has been applied in the inverse direction for the development of centralized control algorithms for irrigation canals. In the present study, a centralized control algorithm based on inversion of the fuzzy dynamic wave model (FDWM) is developed to overcome the drawbacks of the existing centralized control algorithms. A fuzzy logic based dynamic wave model inversion algorithm (FDWMIA) is developed for this purpose, based on the inversion of the FDWM. The FDWMIA is tested by applying it to two canal control problems reported in literature: the first problem deals with water level control in a fictitious canal with a single pool and the second, with water level control in a real canal with a series of pools (ASCE Test Canal 2). In both cases, the FDWMIA results are comparable to those of the existing centralized control algorithms.