Control and balancing of an inverted pendulum has gained a lot of attention over the past few decades due to its unstable properties. This has become a great challenge for control engineers to verify and test the control theory. To control and balance an inverted pendulum, proportional integrated derivative (PID) method or linear quadratic regulator (LQR) method can be used through which a lot of simulations can be done using the represented theories.Since urban population is increasing at a very alarming rate, there is a need to discover new ways of transportation to meet the future challenges and demands. Scania has come up with a new conceptual bus called NXT which aims to develop a modular vehicle that should configure and re-configure themselves between different transportation tasks. NXT vehicle has front and rear drive modules which can be represented as single axle, two-wheeled vehicles which in-turn can be viewed as an inverted pendulum with a huge Center of Gravity. Controlling and balancing of the pod or drive module precisely and accurately is an interesting challenge since it is an unstable inverted pendulum with huge center of gravity (COG). This behaviour of the system has created a research question whether the module is controllable or not.Therefore this thesis focuses on the possibility of controlling the pod which is a two-wheeled inverted pendulum vehicle with a COG offset. Also, the thesis focuses on the construction, mod-elling, testing and validation of a down-scaled model, what sensors are needed to balance the pod precisely, how the sensors must be integrated with the system and how the pod can be controlled remotely from a certain distance by a human. The developed pod houses the technologies like sensors, BLDC motor controllers, hoverboard, Arduino board and Bluetooth transmitters.The Master Thesis starts by presenting an introduction to the inverted pendulum theories, Scania NXT project, information about the research methods, thesis outline and structure . It continues by describing related literature about the inverted pendulums, segways, hoverboards, motor controllers and Arduino boards. Afterwards, the process of deriving a mathematical model, together with simulation in Matlab, Simulink and Simscape is described. Later, construction of the pod is made and lot of effort is put to run the pod. Since the pod needs to be controlled remotely by a human, a remote controlled systemis implemented via mobile phone using an app and finally the thesis is finished with a conclusion and ideas for future work. / Reglering och balancering av en inverterad pendel har väckt stor uppmärksamhet över de senaste decennierna på grund av dess instabila egenskaper. Det har skapat stora utmaningar för regleringenjörer eftersom området tillåter test och verifikation av diverese lösningar. För reglering och balansering av en inverterad pendel, så kan en regulator med proportionell, integral och derivat (PID) konstanter eller en linjär kvadratisk regulator (LQR) användas tillsammans med simuleringar för att bekräfta teorin.I och med att stadsbefolkningen ökar i mycket hög takt, så uppstår behovet av att uppfinna nya transportmedel för att lösa framtida utmaningar och krav. Scania har tagit fram en ny konceptbuss som heter NXT, med målet att utveckla ett modulfordon som kommer att konfigurera och rekonfigurera sig själva mellan olika transportuppgifter. NXT-fordonet har fram- och bakdriv-moduler som kan representeras som enaxlade tvåhjuliga fordon, vilka i sin tur kan betraktas som en inverterad pendel med en stor massa. Att reglera och balansera drivmodulen på ett noggrant sätt är en utmaning eftersom det är ett mycket instabilt system med enorm massa och en okänd tyngdpunkt. Systemets beteende har skapat en forskningsfråga om modulen är reglerbar eller inte.Denna uppsats fokuserar därmed på möjligheterna att kunna reglera drivmodulen samt vilka begränsningar det finns. Uppsatsen fokuserar också på konstruktion, modellering, testning och validering av en nedskalad modell, vilka sensorer som krävs för att balansera drivmodulem, samt hur sensorerna måste integreras med systemet för att kunna fjärstyra fordonet från ett visst avstånd. Utveckingen av en sådan nedskalad modell berör olika områden såsom sensorer, BLDC-motorstyrenheter, hoverboard balanserings scootrar, Arduino kretskort och Bluetooth-sändare/mottagare.Uppsasten börjar med en introduction om olika inverterade pendel teorier, Scania NXT project, forskningsmetoder, en översikt och övergripande struktur. Vidare fortsätter beskrivining av relaterade litteratur om inverterade pendel, Segway, hoverboard, borstlösa motor styrenheter och Arduino kretskort. Senare fortsätter processen för att härleda matematiska modeller som beskirver systemet, tillsammans med simuleringar i Matlab, Simulink och Simscape. Därefter beskrivs konstruktionen av en nedskalad modell av drivmodulen och beskrivning av nödvändiga processer för att få hårdvara och mjkukvara att fungera ihop. Då fordonen ska ha möjlighet att fjärrstyras, så implementerades en bluetooth enhet tillsammans med en programmerbar mobil applikation. Slutligen avlutas uppsatsen med resultat, slutsats och diskussioner och förslag till framtida arbeten.

Stop-and-Go Adaptive Cruise Control (ACC+) is an extension of Adaptive Cruise Control (ACC) that works at low speed as well as normal highway speeds to regulate the speed of the vehicle relative to the vehicle it is following. In this thesis, we design an ACC+ controller for a scale model electric vehicle that ensures the robust performance of the system under various models of uncertainty. We capture the operation of the hybrid system via a state-chart model that performs mode switching between different digital controllers with additional decision logic to guarantee the collision freedom of the system under normal operation. We apply different controller design methods such as Linear Quadratic Regulator (LQR) and H-infinity and perform multiple simulation runs in MATLAB/Simulink to validate the performance of the proposed designs. We compare the practicality of our design with existing formally verified ACC designs from the literature. The comparisons show that the other formally verified designs exhibit unacceptable behaviour in the form of mode thrashing that produces excessive acceleration and deceleration of the vehicle. While simulations provide some assurance of safe operation of the system design, they do not guarantee system safety under all possible cases. To increase confidence in the system, we use Differential Dynamic Logic (dL) to formally state environmental assumptions and prove safety goals, including collision freedom. The verification is done in two stages. First, we identify the invariant required to ensure the safe operation of the system and we formally verify that the invariant preserves the safety property of any system with similar dynamics. This procedure provides a high level abstraction of a class of safe solutions for ACC+ system designs. Second, we show that our ACC+ system design is a refinement of the abstract model. The safety of the closed loop ACC+ system is proven by verifying bounds on the system variables using the KeYmaera verification tool for hybrid systems. The thesis demonstrates how practical ACC+ controller designs optimized for fuel economy, passenger comfort, etc., can be verified by showing that they are a refinement of the abstract high level design. / Thesis / Master of Applied Science (MASc)

Energy storage systems with many Lithium Ion battery cells per string require sophisticated balancing hardware due to individual cells having manufacturing inconsistencies, different self discharge rates, internal resistances and temperature variations. For capacity maximization, safe operation, and extended lifetime, battery balancing is required. Redistributive Non-Dissipative balancing further improves the pack capacity and efficiency over a Dissipative approach where energy is wasted as heat across shunt resistors. Redistribution techniques dynamically shuttle charge to and from weak cells during operation such that all of the stored energy in the stack is utilized. This thesis identifies and develops different balancing control methods. These methods include a unconstrained optimization problem using a Linear Quadratic Regulator (LQR) and a constrained optimization problem using Model Predictive Control (MPC). These methods are benchmarked against traditional rule based (RB) balancing. The control systems are developed using MATLAB/Simulink and validated experimentally on a multiple transformer individual cell to stack topology. The implementation uses a DC2100A Demo-board from Linear Technology with bi-directional flyback converters to transfer the energy between the cells. The results of this thesis show that the MPC control method has the highest balancing efficiency and minimum balancing time. / Thesis / Master of Applied Science (MASc)

Submitted by camilla martins (camillasmmartins@gmail.com) on 2016-12-09T15:11:41Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Tese_ControleInteligenteLQR.pdf: 2310311 bytes, checksum: 11af2680d8c53f2af5c55aa84abe2822 (MD5) / Rejected by Edisangela Bastos (edisangela@ufpa.br), reason: on 2016-12-15T12:03:10Z (GMT) / Submitted by camilla martins (camillasmmartins@gmail.com) on 2016-12-15T13:34:29Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Tese_ControleInteligenteLQR.pdf: 2310311 bytes, checksum: 11af2680d8c53f2af5c55aa84abe2822 (MD5) / Rejected by Edisangela Bastos (edisangela@ufpa.br), reason: on 2016-12-15T14:00:21Z (GMT) / Submitted by camilla martins (camillasmmartins@gmail.com) on 2016-12-15T14:24:22Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Tese_ControleInteligenteLQR.pdf: 2310311 bytes, checksum: 11af2680d8c53f2af5c55aa84abe2822 (MD5) / Approved for entry into archive by Edisangela Bastos (edisangela@ufpa.br) on 2016-12-19T13:20:56Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Tese_ControleInteligenteLQR.pdf: 2310311 bytes, checksum: 11af2680d8c53f2af5c55aa84abe2822 (MD5) / Made available in DSpace on 2016-12-19T13:20:56Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Tese_ControleInteligenteLQR.pdf: 2310311 bytes, checksum: 11af2680d8c53f2af5c55aa84abe2822 (MD5) Previous issue date: 2008-08-30 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Nesta tese é apresentado um modelo neuro-genético, orientado a síntese de controladores no espaço de estado baseado no projeto do Regulador Linear Quadrático, para alocação de autoestrutura em sistemas dinâmicos multivariáveis. O modelo neuro-genético representa uma fusão de um algoritmo genético e uma rede neural recorrente para realizar a seleção das matrizes de ponderação e resolver a equação algébrica de Riccati, respectivamente. Um modelo de 6a ordem de uma aeronave, um modelo de 6a ordem de um gerador de indução duplamente alimentado de uma planta eólica e um modelo de 4a ordem de um circuito elétrico, são usados para avaliar a fusão dos paradigmas de inteligência computacional e o desempenho da metodologia do projeto de controle. O desempenho dos modelos neuro-genéticos são avaliados por momentos estatísticos de primeira e segunda ordem para o algoritmo genético, enquanto que a rede neural é avaliada por superfícies da função energia e da norma do infinito da equação algébrica de Riccati. São feitas comparações com o método de Schur. / In this thesis is presented a neural-genetic model, oriented to state space controllers synthesis, based on the Linear Quadratic Regulator design, for eigenstructure assignment of multivariable dynamic systems. The neural-genetic model represents a fusion of a genetic algorithm and a recurrent neural network to perform the weighting matrices selection and the algebraic Riccati equation solution, respectively. In order to a assess the LQR design, the procedure was applied in a 6th order aircraft model, 6th order doubly fed induction generator model of a wind plant and a 4th order electric circuit model which were used to evaluate the fusion of the computational intelligence paradigms and the control design method performance.The performance of the neural-genetic models are evaluated by the first and second statistics moments for the genetic algorithm, whereas the neural network is evaluated by surfaces of the energy function and of the norm of the infinity of the algebraic equation of Riccati and the results compared to the results obtained by using Schur’s Method.

This thesis develops dynamic models for the two-mode FWD EVT, develops a control system based on those models that is capable of meeting driver torque demands and performing synchronous mode shifts between different EVT modes while also accommodating preferred engine operating points. The two-input two-output transmission controller proposed herein incorporates motor-generator dynamics, is based on a general state-space integral control structure, and has feedback gains determined using linear quadratic regulator (LQR) optimization. Dynamic modeling of the vehicle is categorized as dynamic modeling of the mechanical and electrical subsystems where the mechanical subsystem consists of the planetary gear sets, the transmission and the engine whereas the electrical subsystem consists of the motor-generator units and the battery pack. A discussion of load torque is also considered as part of the mechanical subsystem. With the help of these derived dynamic models, a distinction is made between dynamic output torque and steady-state output torque. The overall control system consisting of multiple subsystems such as the human driver, power management unit (PMU), friction brakes, combustion engine, transmission control unit (TCU) and motor-generator units is designed. The logic for synchronous mode shifts between different EVT modes is also detailed as part of the control system design. Finally, the thesis presents results for responses in individual operating modes, EVT mode shifting and a full UDDS drive cycle simulation.

Micro aerial vehicles and other autonomous systems have the potential to truly transform life as we know it, however much of the potential of autonomous systems remains unrealized because reliable navigation is still an unsolved problem with significant challenges. This dissertation presents solutions to many aspects of autonomous navigation. First, it presents ROSflight, a software and hardware architure that allows for rapid prototyping and experimentation of autonomy algorithms on MAVs with lightweight, efficient flight control. Next, this dissertation presents improvments to the state-of-the-art in optimal control of quadrotors by utilizing the error-state formulation frequently utilized in state estimation. It is shown that performing optimal control directly over the error-state results in a vastly more computationally efficient system than competing methods while also dealing with the non-vector rotation components of the state in a principled way. In addition, real-time robust flight planning is considered with a method to navigate cluttered, potentially unknown scenarios with real-time obstacle avoidance. Robust state estimation is a critical component to reliable operation, and this dissertation focuses on improving the robustness of visual-inertial state estimation in a filtering framework by extending the state-of-the-art to include better modeling and sensor fusion. Further, this dissertation takes concepts from the visual-inertial estimation community and applies it to tightly-coupled GNSS, visual-inertial state estimation. This method is shown to demonstrate significantly more reliable state estimation than visual-inertial or GNSS-inertial state estimation alone in a hardware experiment through a GNSS-GNSS denied transition flying under a building and back out into open sky. Finally, this dissertation explores a novel method to combine measurements from multiple agents into a coherent map. Traditional approaches to this problem attempt to solve for the position of multiple agents at specific times in their trajectories. This dissertation instead attempts to solve this problem in a relative context, resulting in a much more robust approach that is able to handle much greater intial error than traditional approaches.