As a conventional transportation modality, bicycles have been gradually electrified to meet the desire for convenient and green commuting patterns, especially in developed urban areas. The electric bicycle battery pack and its management system are core elements that determine key performance metrics such as electric range and output power. With respect to electric bicycle applications, focused research on the battery, its management system, and performance has received less attention compared to other energy storage applications. In this thesis, a well-developed conversion kit produced by BionX is studied. A data collecting system is first installed to record both mechanical and electrical data, such as speed, power and voltage; this enables defining two standard riding cycles at different riding conditions. Two benchmarking tests are performed to investigate the battery life in pure electric mode and at different threshold levels of optimal assistance. A novel quadratic programming based fitting algorithm is derived and applied in both time and frequency domain parameter identification tests. The proposed algorithm is able to fit single/multiple pulses by applying a masking vector. Sensitivity study and experimental results show the high robustness and fast computation time of the approach compared to existing and commonly used methods, such as fmincon. The comparison between hybrid power pulse characterization (HPPC) and electrochemical impedance spectrum (EIS) tests are performed in terms of extracted internal resistance. A second-order RC battery model is developed using parameters extracted from HPPC tests. The model is validated by experimental riding cycles and used to generate the reference SOC profiles that are employed in a SOC estimation study. Four estimation strategies, including extended Kalman Filter (EKF), Sigma point Kalman Filter (SPKF), Cubature Kalman Filter (CKF), and joint extended Kalman Filter (JEKF), are compared systematically in terms of accuracy, robustness and computation complexity. / Thesis / Master of Applied Science (MASc)
Tamasco, Cynthia M
06 August 2011
This thesis presents the development and implementation of a generalized optimization framework for use in sheet-stamping process simulation by finite element analysis. The generic framework consists of three main elements: a process simulation program, an optimization code, and a response filtering program. These elements can be filled by any combination of applicable software packages. Example sheet-stamping process simulations are presented to demonstrate the usage of the framework in various forming scenarios. Each of the example simulations is presented with a sensitivity analysis. These examples include analysis of a 2-dimensional single-stage forming, a 2-dimensional multi-stage forming, and two different 3-dimensional single-stage forming processes. A forming limit diagram is used to define failure in the 3-dimensional process simulations. Optimization results are presented using damage minimization, thinning minimization, and springback minimization with aluminum alloy 6061-T6 blanks.
Distributed Feedback Control Algorithms for Cooperative Locomotion: From Bipedal to Quadrupedal RobotsKamidi, Vinaykarthik Reddy 25 March 2022 (has links)
This thesis synthesizes general and scalable distributed nonlinear control algorithms with application to legged robots. It explores both naturally decentralized problems in legged locomotion, such as the collaborative control of human-lower extremity prosthesis and the decomposition of high-dimensional controllers of a naturally centralized problem into a net- work of low-dimensional controllers while preserving equivalent performance. In doing so, strong nonlinear interaction forces arise, which this thesis considers and sufficiently addresses. It generalizes to both symmetric and asymmetric combinations of subsystems. Specifically, this thesis results in two distinct distributed control algorithms based on the decomposition approach. Towards synthesizing the first algorithm, this thesis presents a formal foundation based on de- composition, Hybrid Zero Dynamics (HZD), and scalable optimization to develop distributed controllers for hybrid models of collaborative human-robot locomotion. This approach con- siders a centralized controller and then decomposes the dynamics and parameterizes the feedback laws to synthesize local controllers. The Jacobian matrix of the Poincaré map with local controllers is studied and compared with the centralized ones. An optimization problem is then set up to tune the parameters of the local controllers for asymptotic stability. It is shown that the proposed approach can significantly reduce the number of controller parameters to be optimized for the synthesis of distributed controllers, deeming the method computationally tractable. To evaluate the analytical results, we consider a human amputee with the point of separation just above the knee and assume the average physical parameters of a human male. For the lower-extremity prosthesis, we consider the PRleg, a powered knee-ankle prosthetic leg, and together, they form a 19 Degrees of Freedom (DoF) model. A multi-domain hybrid locomotion model is then employed to rigorously assess the performance of the afore-stated control algorithm via numerical simulations. Various simulations involving the application of unknown external forces and altering the physical parameters of the human model unbeknownst to the local controllers still result in stable amputee loco- motion, demonstrating the inherent robustness of the proposed control algorithm. In the later part of this thesis, we are interested in developing distributed algorithms for the real-time control of legged robots. Inspired by the increasing popularity of Quadratic programming (QP)-based nonlinear controllers in the legged locomotion community due to their ability to encode control objectives subject to physical constraints, this thesis exploits the idea of distributed QPs. In particular, this thesis presents a formal foundation to systematically decompose QP-based centralized nonlinear controllers into a network of lower-dimensional local QPs. The proposed approach formulates a feedback structure be- tween the local QPs and leverages a one-step communication delay protocol. The properties of local QPs are analyzed, wherein it is established that their steady-state solutions on periodic orbits (representing gaits) coincide with that of the centralized QP. The asymptotic convergence of local QPs' solutions to the steady-state solution is studied via Floquet theory. Subsequently, to evaluate the effectiveness of the analytical results, we consider an 18 DoF quadrupedal robot, A1, as a representative example. The network of distributed QPs mentioned earlier is condensed to two local QPs by considering a front-hind decomposition scheme. The robustness of the distributed QP-based controller is then established through rigorous numerical simulations that involve exerting unmodelled external forces and intro- ducing unknown ground height variations. It is further shown that the proposed distributed QPs have reduced sensitivity to noise propagation when compared with the centralized QP. Finally, to demonstrate that the resultant distributed QP-based nonlinear control algorithm translates equivalently well to hardware, an extensive set of blind locomotion experiments on the A1 robot are undertaken. Similar to numerical simulations, unknown external forces in the form of aggressive pulls and pushes were applied, and terrain uncertainties were introduced with the help of arbitrarily displaced wooden blocks and compliant surfaces. Additionally, outdoor experiments involving a wide range of terrains such as gravel, mulch, and grass at various speeds up to 1.0 (m/s) reiterate the robust locomotion observed in numerical simulations. These experiments also show that the computation time is significantly dropped when the distributed QPs are considered over the centralized QP. / Doctor of Philosophy / Inspiration from animals and human beings has long driven the research of legged loco- motion and the subsequent design of the robotic counterparts: bipedal and quadrupedal robots. Legged robots have also been extended to assist human amputees with the help of powered prostheses and aiding people with paraplegia through the development of exoskeleton suits. However, in an effort to capture the same robustness and agility demonstrated by nature, our design abstractions have become increasingly complicated. As a result, the en- suing control algorithms that drive and stabilize the robot are equivalently complicated and subjected to the curse of dimensionality. This complication is undesirable as failing to compute and prescribe a control action quickly destabilizes and renders the robot uncontrollable. This thesis addresses this issue by seeking nature for inspiration through a different perspective. Specifically, through some earlier biological studies on cats, it was observed that some form of locality is implemented in the control of animals. This thesis extends this observation to the control of legged robots by advocating an unconventional solution. It proposes that a high-dimensional, single-legged agent be viewed as a virtual composition of multiple, low-dimensional subsystems. While this outlook is not new and forms precedent to the vast literature of distributed control, the focus has always been on large-scale systems such as power networks or urban traffic networks that preserve sparsity, mathematically speaking. On the contrary, legged robots are underactuated systems with strong interaction forces acting amongst each subsystem and dense mathematical structures. This thesis considers this problem in great detail and proposes developments that provide theoretical stability guarantees for the distributed control of interconnected legged robots. As a result, two distinctly different distributed control algorithms are formulated. We consider a naturally decentralized structure appearing in the form of a human-lower extremity prosthesis to synthesize distributed controllers using the first control algorithm. Subsequently, the resultant local controllers are rigorously validated through extensive full- order simulations. In order to validate the second algorithm, this thesis considers the problem of quadrupedal locomotion as a representative example. It assumes for the purposes of control synthesis that the quadruped is comprised of two subsystems separated at the geometric center, resulting in a front and hind subsystem. In addition to rigorous validation via numerical simulations, in the latter part of this thesis, to demonstrate that distributed controllers preserve practicality, rigorous and extensive experiments are undertaken in indoor and outdoor settings on a readily available quadrupedal robot A1.
Alternative Methods for Operational Optimization of Hydro Power Plants / Alternativa Metoder för Driftoptimering av VattenkraftverkAlmgrund, Jonas January 2019 (has links)
The aim of this thesis is to optimize hydro power plants with data generated from observations and field tests at the plants. The output is optimal production tables and curves in order to operate and plan hydro power plants in an optimized way concerning power output, efficiency and distribution of water. The thesis is performed in collaboration with Vattenfall AB, which currently use an internal optimization program called SEVAP. Two alternative methods have been selected, employed and compared with the current optimization program, these are Interior-Point Method and Sequential Quadratic Programming. Three start-point strategies are created to increase the probability of finding a global optima. A heuristic rule is used for selection of strategy in order to prevent rapid changes in load distribution for small variations in dispatched water. The optimization is performed at three plants in Sweden with different size and setup. The results of this evaluation showed marginally better results for the employed methods in comparison to the currently used optimization. Further, the developed program is more flexible and compatible to integrate with future digitalization projects. / Syftet med detta examensarbete är att optimera vattenkraftverk med data som genererats från indextester vid kraftverken. Resultatet är optimala produktionstabeller och kurvor för drift och planering av vattenkraftverk. Dessa är baserade på att optimalt fördela vattnet mellan aggregaten för att maximera uteffekt och verkningsgrad. Detta arbete har utförts i samarbete med Vattenfall AB, som för närvarande använder ett internt optimeringsprogram som heter SEVAP. Två optimeringsmetoder har valts, implementerats och jämförts med det nuvarande optimeringsprogrammet. Dessa metoder är inrepunktsmetoden (IPM) och sekventiell kvadratiskt programmering (SQP). Tre startpunktsstrategier har används för att öka sannolikheten att hitta ett globalt optima. För att förhindra hastiga förändringar i lastfördelning för små variationer av avsänt vatten har en heuristisk regel används. Optimeringen har utförts på tre stationer med olika uppsättning och storlek. Resultatet av detta examensarbete visar marginellt bättre resultat för de använda metoderna i jämförelse med den nuvarande optimeringen. Det utvecklade programmet är flexibelt och kompatibelt att integrera med framtida digitaliseringsprojekt.
Kalyani, Radha Padma,
(has links) (PDF)
Thesis (Ph. D.)--University of Missouri--Rolla, 2007. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed November 29, 2007) Includes bibliographical references.
Performance optimization of engineering systems with particular reference to dry-cooled power plants /Conradie, Antonie Eduard. January 1995 (has links)
Dissertation (PhD)--University of Stellenbosch, 1995. / Bibliography. Also available via the Internet.
Singh, Mukti Nath.
Thesis (M.S.)--Mississippi State University. Department of Aerospace Engineering. / Title from title screen. Includes bibliographical references.
Aerodynamic design applying automatic differentiation and using robust variable fidelity optimizationTakemiya, Tetsushi 03 September 2008 (has links)
In modern aerospace engineering, the physics-based computational design method is becoming more important. However, high-fidelity models require longer computational time, so the advantage of efficiency is partially lost. This problem has been overcome with the development of the approximation management framework (AMF). In the AMF, objective and constraint functions of a low-fidelity model are scaled at a design point so that the scaled functions, referred to as gsurrogate functions, h match those of a high-fidelity model. Since scaling functions and the low-fidelity model constitutes surrogate functions, evaluating the surrogate functions is faster than evaluating the high-fidelity model. Therefore, in the optimization process of the AMF, the surrogate functions are used to obtain a new design point. However, the author found that 1) the AMF is very vulnerable when the computational analysis models have numerical noise, and that 2) the AMF terminates optimization prematurely when the optimization problems have constraints. In order to solve the first problem, automatic differentiation (AD) technique is applied. If derivatives are computed with the generated derivative code, they are analytical, and the computational time is independent of the number of design variables. However, if analysis models implement iterative computations such as computational fluid dynamics (CFD), computing derivatives through the AD requires a massive memory size. The author solved this deficiency by modifying the AD approach and developing a more efficient implementation with CFD. In order to solve the second problem, the governing equation of the trust region ratio is modified so that it can accept the violation of constraints within some tolerance. By accepting violations of constraints during the optimization process, the AMF can continue optimization without terminating immaturely and eventually find the true optimum design point. With these modifications, the AMF is referred to as gRobust AMF, h and it is applied to airfoil and wing designs using Euler CFD software. The proposed AD method computes derivatives more accurately and faster than the finite differentiation method, and the Robust AMF successfully optimizes shapes of the airfoil and the wing in a much shorter time than the sequential quadratic programming with only high-fidelity models.
01 September 2010
(has links) (PDF)
In statistics, regression analysis is a technique, used to understand and model the relationship between a dependent variable and one or more independent variables. Multiple Adaptive Regression Spline (MARS) is a form of regression analysis. It is a non-parametric regression technique and can be seen as an extension of linear models that automatically models non-linearities and interactions. MARS is very important in both classification and regression, with an increasing number of applications in many areas of science, economy and technology. In our study, we analyzed Generalized Partial Linear Models (GPLMs), which are particular semiparametric models. GPLMs separate input variables into two parts and additively integrates classical linear models with nonlinear model part. In order to smooth this nonparametric part, we use Conic Multiple Adaptive Regression Spline (CMARS), which is a modified form of MARS. MARS is very benefical for high dimensional problems and does not require any particular class of relationship between the regressor variables and outcome variable of interest. This technique offers a great advantage for fitting nonlinear multivariate functions. Also, the contribution of the basis functions can be estimated by MARS, so that both the additive and interaction effects of the regressors are allowed to determine the dependent variable. There are two steps in the MARS algorithm: the forward and backward stepwise algorithms. In the first step, the model is constructed by adding basis functions until a maximum level of complexity is reached. Conversely, in the second step, the backward stepwise algorithm reduces the complexity by throwing the least significant basis functions from the model. In this thesis, we suggest not using backward stepwise algorithm, instead, we employ a Penalized Residual Sum of Squares (PRSS). We construct PRSS for MARS as a Tikhonov Regularization Problem. We treat this problem using continuous optimization techniques which we consider to become an important complementary technology and alternative to the concept of the backward stepwise algorithm. Especially, we apply the elegant framework of Conic Quadratic Programming (CQP) an area of convex optimization that is very well-structured, hereby, resembling linear programming and, therefore, permitting the use of interior point methods. At the end of this study, we compare CQP with Tikhonov Regularization problem for two different data sets, which are with and without interaction effects. Moreover, by using two another data sets, we make a comparison between CMARS and two other classification methods which are Infinite Kernel Learning (IKL) and Tikhonov Regularization whose results are obtained from the thesis, which is on progress.
Predictive Energy Management of Long-Haul Hybrid Trucks : Using Quadratic Programming and Branch-and-BoundJonsson Holm, Erik January 2021 (has links)
This thesis presents a predictive energy management controller for long-haul hybrid trucks. In a receding horizon control framework, the vehicle speed reference, battery energy reference, and engine on/off decision are optimized over a prediction horizon. A mixed-integer quadratic program (MIQP) is formulated by performing modelling approximations and by including the binary engine on/off decision in the optimal control problem. The branch-and-bound algorithm is applied to solve this problem. Simulation results show fuel consumption reductions between 10-15%, depending on driving cycle, compared to a conventional truck. The hybrid truck without the predictive control saves significantly less. Fuel consumption is reduced by 3-8% in this case. A sensitivity analysis studies the effects on branch-and-bound iterations and fuel consumption when varying parameters related to the binary engine on/off decision. In addition, it is shown that the control strategy can maintain a safe time gap to a leading vehicle. Also, the introduction of the battery temperature state makes it possible to approximately model the dynamic battery power limitations over the prediction horizon. The main contributions of the thesis are the MIQP control problem formulation, the strategy to solve this with the branch-and-bound method, and the sensitivity analysis.
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