The automotive industry has been involved in making vehicles autonomous to different levels in the past decade rapidly. Particularly in the commercial vehicle market, there is a significant necessity to make trucks have a certain level of automation to help reduce dependence on human efforts to drive. This could help in reducing several accidents caused by human error. Interestingly there are several challenges and solutions in achieving and implementing autonomous driving for trucks. First, a benchmark of different control architectures that can make a truck drive autonomously are explored. The chosen controllers (Pure Pursuit, Stanley, Linear Quadratic Regulator, Sliding Mode Control and Model Predictive Control) vary in their simplicity in implementation and versatility in handling different vehicle parameters and constraints. A thorough comparison of these path tracking controllers are performed using several metrics. Second, a collision avoidance system based on cubic polynomials, inspired by rapidly exploring random tree (RRT) is presented. Some of the path tracking controllers are limited by their ability and hence a standalone collision avoidance system is needed to provide safe maneuvering. Simulations are performed for different test cases with and without obstacles. These simulations help compare safety margin and driving comfort of each path tracking controller that are integrated with the collision avoidance system. Third, different performance metrics like change in acceleration input, change in steering input, error in path tracking, deviation from base frame of track file and lateral and longitudinal margin between ego and target vehicle are presented. To conclude, a set of suitable controllers for heavy articulated vehicles are developed and benchmarked. / Bilindustrin har varit involverad i att göra fordon autonoma till olika nivåer under det senaste decenniet snabbt. Särskilt på marknaden för kommersiella fordon finns det ett stort behov av att få lastbilar att ha en viss nivå av automatisering för att minska beroendet av mänskliga ansträngningar att köra. Detta kan hjälpa till att minska flera olyckor orsakade av mänskliga fel. Intressant nog finns det flera utmaningar och lösningar för att uppnå och implementera autonom körning för lastbilar. Först utforskas ett riktmärke av olika styrarkitekturer som kan få en lastbil att köra autonomt. De valda kontrollerna (Pure Pursuit, Stanley, Linear Quadratic Regulator, Sliding Mode Control och Model Predictive Control) varierar i sin enkelhet i implementering och mångsidighet när det gäller att hantera olika fordonsparametrar och begränsningar. En grundlig jämförelse av dessa vägspårningskontroller utförs med hjälp av flera mätvärden. För det andra presenteras ett system för undvikande av kollisioner baserat på kubiska polynom, inspirerat av snabbt utforskande slumpmässiga träd (RRT). Vissa av vägspårningskontrollerna är begränsade av sin förmåga och därför behövs ett fristående system för att undvika kollisioner för att ge säker manövrering. Simuleringar utförs för olika testfall med och utan hinder. Dessa simuleringar hjälper till att jämföra säkerhetsmarginal och körkomfort för varje vägspårningskontroller som är integrerade med kollisionsundvikande systemet. För det tredje presenteras olika prestandamått som förändring i accelerationsinmatning, förändring i styrinmatning, fel i banspårning, avvikelse från basramen för spårfilen och lateral och longitudinell marginal mellan ego och målfordon. Avslutningsvis utvecklas och benchmarkas en uppsättning lämpliga styrenheter för tunga ledade fordon.

Having a accurate model of a system is essential for many applications today, especially those related to advanced process control. When executing a project often a lot of time is spent performing experiments on the real system to estimate a model. By designing higher quality experiments the time needed to estimate and identify these models can be reduced saving both resources and engineering efforts. This masters thesis investigates optimal input design to minimize the time needed to identify a linear time-invariant multivariable system fulfilling certain requirements on the model accuracy. Previous input designs mostly focused on sequential excitation but here the effects of using combined simultaneous and sequential excitation is investigated. The design is performed in simulations and evaluated in closed loop using a model predictive controller to further guarantee that the output constraints are not violated. The results indicate that there are many cases where using combined simultaneous and sequential excitation outperforms the previous methods. The effects of the color of the noise on the input design is investigated and the ability of different designs to estimate system delay is also studied. In addition it is shown how an iterative scheme can be used to guarantee that the accuracy requirements on the estimated model are met. / Att ha en god modell av ett system är viktigt för många applikationer idag, särskilt de som är relaterade till avancerad processtyrning. När man genomför ett projekt läggs ofta mycket tid på att utföra experiment på det verkliga systemet för att identifiera en modell. Genom att utforma experiment av hög kvalitet kan den tid som behövs för att identifiera dessa modeller minskas, vilket minimerar både processpåverkan och ingenjörsinsatsen. Denna masteruppsats undersöker metoder för optimal experimentdesign för att minimera tiden som behövs för att identifiera ett multivariabelt system där det finns krav på modellens noggrannhet. Tidigare metoder fokuserade mest på sekventiella experiment, men här undersöks effekterna av att använda en kombination av samtidiga och sekventiella experiment. Här används simuleringar som utvärderas i sluten loop med hjälp av en modellprediktiv regulator för att undvika att utsignalbegränsningarna inte överskrids. Resultatet indikerar att det finns många fall där användning av kombinerade samtidiga och sekventiella experiment överträffar tidigare metoder. Effekterna av färgat brus på ingångsdesignen undersöks och olika metoders förmåga att uppskatta systemfördröjning studeras också. Dessutom visas hur ett iterativt schema kan användas för att garantera att noggrannhetskraven på den uppskattade modellen uppfylls.

The thesis focuses on the design of a control strategy for safety-critical remote teleoperation. The main goal is to make the controlled system track the desired velocity specified by a human operator while avoiding obstacles despite communication delays. Different methods adopting Control Barrier Functions (CBFs) and Model Predictive Control (MPC) have been explored and tested. In this combination, CBFs are used to define the safety constraints the system has to respect to avoid obstacles, while MPC provides the framework for filtering the desired input by solving an optimization problem. The resulting input is sent to the remote system, where appropriate low-level velocity controllers translate it into system-specific commands. The main novelty of the thesis is a method to make the CBFs robust against the uncertainties affecting the system’s state due to network delays. Other techniques are investigated to improve the quality of the system information starting from the delayed one and to formulate the optimization problem without knowing the specific dynamic of the controlled system. The results show how the proposed method successfully solves the safetycritical teleoperation problem, making the controlled systems avoid obstacles with different types of network delay. The controller has also been tested in simulation and on a real manipulator, demonstrating its general applicability when reliable low-level velocity controllers are available. / Avhandlingen fokuserar på utformningen av en kontrollstrategi för säkerhetskritisk fjärrstyrd teleoperation. Huvudmålet är att få det kontrollerade systemet att följa den önskade hastigheten som specificeras av en mänsklig operatör samtidigt som hinder undviks trots kommunikationsfördröjningar. Olika metoder som använder Control Barrier Functions (CBFs) och Model Predictive Control har undersökts och testats. I denna kombination används CBFs för att definiera de säkerhetsbegränsningar som systemet måste respektera för att undvika hinder, medan MPC utgör ramverket för filtrering av den önskade indata genom att lösa ett optimeringsproblem. Den resulterande indata skickas till fjärrsystemet, där lämpliga hastighetsregulatorer på låg nivå översätter den till systemspecifika kommandon. Den viktigaste nyheten i avhandlingen är en metod för att göra CBFs robust mot de osäkerheter som påverkar systemets tillstånd på grund av nätverksfördröjningar. Andra tekniker undersöks för att förbättra kvaliteten på systeminformationen med utgångspunkt från den fördröjda informationen och för att formulera optimeringsproblemet utan att känna till det kontrollerade systemets specifika dynamik. Resultaten visar hur den föreslagna metoden framgångsrikt löser det säkerhetskritiska teleoperationsproblemet, vilket gör att de kontrollerade systemen undviker hinder med olika typer av nätverksfördröjningar. Styrningen har också testats i simulering och på en verklig manipulator, vilket visar dess allmänna tillämpbarhet när tillförlitliga lågnivåhastighetsregulatorer finns tillgängliga.

Fuel consumption reduction is one of the main challenges in the automotiveindustry due to its economical and environmental impacts as well as legalregulations. While fuel consumption reduction is important for all vehicles,it has larger benefits for commercial ones due to their long operational timesand much higher fuel consumption. Optimal control of multiple energy buffers within the vehicle proves aneffective approach for reducing energy consumption. Energy is temporarilystored in a buffer when its cost is small and released when it is relativelyexpensive. An example of an energy buffer is the vehicle body. Before goingup a hill, the vehicle can accelerate to increase its kinetic energy, which canthen be consumed on the uphill stretch to reduce the engine load. The simplestrategy proves effective for reducing fuel consumption. The thesis generalizes the energy buffer concept to various vehicular componentswith distinct physical disciplines so that they share the same modelstructure reflecting energy flow. The thesis furthermore improves widely appliedcontrol methods and apply them to new applications. The contribution of the thesis can be summarized as follows: • Developing a new function to make the equivalent consumption minimizationstrategy (ECMS) controller (which is one of the well-knownoptimal energy management methods in hybrid electric vehicles (HEVs))more robust. • Developing an integrated controller to optimize torque split and gearnumber simultaneously for both reducing fuel consumption and improvingdrivability of HEVs. • Developing a one-step prediction control method for improving the gearchanging decision. • Studying the potential fuel efficiency improvement of using electromechanicalbrake (EMB) on a hybrid electric city bus. • Evaluating the potential improvement of fuel economy of the electricallyactuated engine cooling system through the off-line global optimizationmethod. • Developing a linear time variant model predictive controller (LTV-MPC)for the real-time control of the electric engine cooling system of heavytrucks and implementing it on a real truck. / <p>QC 20160128</p>

Thesis (MScEng (Electrical and Electronic Engineering))--Stellenbosch University, 2011. / ENGLISH ABSTRACT: This thesis investigates the viability of constructing a solid-state transformer (SST) with a series-input, parallel-output connection of full-bridge, three-level ying-capacitor converters. It focusses on the active recti er front-end of the SST which is used to control the input current to be sinusoidal and in-phase with the sinusoidal input voltage. A stack of two converters are built and tested. The input current, as well as the ying capacitor voltages of the two active recti ers in the stack, are actively controlled by a nite-state model-based predictive (FS-MPC) controller. The use of multiple ying-capacitor converters poses a problem when using FS-MPC because of the large number of possible switching states to include in the prediction equations. Three FS-MPC control algorithms are proposed to attempt to overcome the problem associated with the large number of switching states. They are implemented on an FPGA digital controller. The algorithms are compared on the bases of voltage and current errors, as well as their responses to disturbances that are introduced into the system. The simulation and experimental results that are presented shows that by interleaving the control actions for the two converters, one can obtain fast and robust responses of the controlled variables. The viability of extending the interleaving control algorithm beyond two converters is also motivated. / AFRIKAANSE OPSOMMING: Hierdie tesis ondersoek die moontlikheid van volbrug, drievlak vlieënde-kapasitoromsetters wat gebruik word om 'n serie-intree, parallel-uittree drywingselektroniese transformator (DET) te bou. Dit fokus op die aktiewe gelykrigter van die DET wat gebruik word om die intreestroom te beheer om sinusvormig en in fase met die sinusvormige intreespanning te wees. 'n Stapel van twee omsetters word gebou en getoets. Die intreestroom, sowel as die vlieënde kapasitorspannings van die twee aktiewe gelykrigters in die stapel, word aktief beheer met behulp van 'n eindige-toestand, model-gebaseerde voorspellende beheerder (ET-MVB). Die gebruik van veelvuldige vlieënde-kapasitoromsetters bemoeilik die implementering van 'n ET-MVB-beheerder as gevolg van die groot aantal skakeltoestande wat in die voorspellende vergelykings in ag geneem moet word. Drie ET-MVB-algoritmes word voorgestel om te poog om die probleme, wat met die groot aantal skakeltoestande geassosieer word, te oorkom. Die algoritmes word in 'n FPGA digitale verwerker geïmplementeer. Die algoritmes word vergelyk op grond van hul stroom- en spanningsfoute, asook hul reaksie op steurings wat op die stelsel ingevoer word. Die simulasie en praktiese resultate toon dat, deur die beheeraksies vir die twee omsetters te laat oorvleuel, die gedrag van die beheerde veranderlikes vinniger en meer robuust is. Die moontlikheid om die oorvleuelende beheeraksies uit te brei tot meer as twee omsetters word ook gemotiveer.

<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>

Electro-Hydraulic (EH) systems are commonly used in the industry for applications that require high power-weight ratios and large driving forces. The EH system studied in this master thesis have recently been upgraded with new hardware components and as a part of this upgrade a new controller was requested. The system consists of a controller that computes a control signal for an electric motor. The motor drives a gear pump that generates a flow of hydraulic fluid. The flow is then directed to a cylinder. The movements of a piston in the cylinder is affected by the flow and the piston position can be measured. The measured piston position is then fed back to the controller and the control loop is complete. The system was previously controlled using a Proportional-Integral-Derivative (PID) controller and the purpose of this thesis is to compare the old controller with alternative control strategies suitable for this application. The evaluation of the controllers is based on both software and hardware simulations and results in a recommendation for final implementation of the best suited controller. The control strategies chosen for investigation are: a retuned PID controller, a PID controller with feed forward from reference, a PID based cascade controller, a Linear Quadratic (LQ) controller, and a Model Predictive Controller (MPC). To synthesize the controllers an approximate model of the system is formed and implemented in the software environment Matlab Simulink. The model is tuned to fit recorded data and provides a decent estimation of the actual system. The proposed control strategies are then simulated and evaluated in Simulink with the model posing as the real system. These simulations resulted in the elimination of the cascade controller as a possible candidate since it proved unstable for large steps in the reference signal. The remaining four controllers were all selected for simulation on the real hardware system. Unfortunately the MPC was never successfully implemented on the hardware due to some unknown compatibility error and hence eliminated as a possible candidate. The three remaining control strategies, PID, PID with feed forward from reference and the LQ controller, were all successfully implemented and simulated on hardware. The results from the hardware simulations compared to simulations made with the old controller, as well as the results from the software simulations, were then evaluated. Depending on the purpose one of two control strategies is recommended for this application. The LQ controller achieved the best overall performance and is presented as the control strategy best suited for this application.

This thesis develops efficient optimization methods for Model Predictive Control (MPC) to enable its application to constrained systems with fast and uncertain dynamics. The key contribution is an active set method which exploits the parametric nature of the sequential optimization problem and is obtained from a dynamic programming formulation of the MPC problem. This method is first applied to the nominal linear MPC problem and is successively extended to linear systems with additive uncertainty and input constraints or state/input constraints. The thesis discusses both offline (projection-based) and online (active set) methods for the solution of controllability problems for linear systems with additive uncertainty. The active set method uses first-order necessary conditions for optimality to construct parametric programming regions for a particular given active set locally along a line of search in the space of feasible initial conditions. Along this line of search the homotopy of optimal solutions is exploited: a known solution at some given plant state is continuously deformed into the solution at the actual measured current plant state by performing the required active set changes whenever a boundary of a parametric programming region is crossed during the line search operation. The sequence of solutions for the finite horizon optimal control problem is therefore obtained locally for the given plant state. This method overcomes the main limitation of parametric programming methods that have been applied in the MPC context which usually require the offline precomputation of all possible regions. In contrast to this the proposed approach is an online method with very low computational demands which efficiently exploits the parametric nature of the solution and returns exact local DP solutions. The final chapter of this thesis discusses an application of robust tube-based MPC to the nonlinear MPC problem based on successive linearization.

<div>Class 8 trucks on average drive the most miles and consume the most fuel of any major vehicle category annually. Indiana specifically is the fifth busiest state for commercial freight traffic and moves $750 billion dollars of freight annually, and this number is expected to grow by 60% by 2040. Reducing fuel consumption for class 8 trucks would have a significant benefit on business and the proportional decrease in CO₂ would be exceptionally beneficial for the environment.</div><div><br></div><div>Platooning is one of the most important strategies for increasing class 8 truck fuel savings. Platooning alone can help trucks save upwards of 7% platoon average fuel savings on flat ground. However, it can be difficult for a platooning controller to maintain a desired truck separation during uncoordinated shifting events. Using a high-fidelity simulation model, it is shown that simultaneous shifting–having the follow truck shift whenever the lead truck shifts (unless shifting would cause its engine to overspeed or underspeed)–decreases maximum truck separation by 24% on a moderately challenging grade route and 40% on a heavy grade route.</div><div><br></div><div>Model Predictive Control (MPC) of the follow truck is considered as a means to reduce the distance the follow truck falls behind during uncoordinated shifting events. The result in simulation is a reduction in maximum truck separation of 1% on a moderately challenging grade route and 19% on a heavy grade route. However, simultaneous shifting largely alleviates the need for MPC for the sake of tracking for the follow truck.</div><div><br></div><div>A different MPC formulation is considered to dynamically change the desired set point for truck separation for routes through a strategy called Route Optimized Gap Growth (ROGG). The result in simulation is 1% greater fuel savings on a moderately challenging grade route and 7% greater fuel savings on a route with heavy grade for the follow truck.</div>

As ondulações na corrente do estator e o forte impacto de erros em parâmetros são apontados como desvantagens do controle preditivo finite control-set (FCS) quando aplicado no controle de máquinas elétricas trifásicas. Neste contexto, o objetivo deste trabalho é propor métodos para aprimorar tanto o desempenho em regime permanente quanto à robustez frente a erros paramétricos do controle preditivo de corrente, conhecido como model predictive current control (MPCC). No presente trabalho são propostas as abordagens MPCC baseada na aplicação de dois e três vetores de tensão durante o mesmo período de controle com intuito de aprimorar o desempenho do controle preditivo de corrente, sem a necessidade de elevar a frequência de amostragem do sistema. Os princípios para escolha dos vetores ótimos e seus respectivos tempos de aplicação são baseados nas expressões para controle desacoplado de torque e fluxo do FOC e no princípio de funcionamento do deadbeat. A robustez paramétrica é garantida por meio de um observador de distúrbios discreto incorporado ao método de controle MPCC. Com a estimação dos impactos causados por variações de parâmetros e dinâmicas não modeladas é possível corrigir os erros dos sistema de controle, mas mantendo-se as suas características transitórias e de regime permanente. Resultados de simulação computacional bem como resultados obtidos em bancada de ensaios experimentais são apresentados para avaliação e comprovação das propostas. / Ripples in the stator current and the strong impact of errors in parameters are pointed as disadvantages of the predictive control when applied in the control of three-phase electric machines. In this context, the objective of this work is to propose methods to improve both the steady-state performance and the robustness against parametric errors of the current predictive control, known as MPCC. In the present work the MPCC approaches are proposed based on the application of two and three voltage vectors during the same control period in order to improve the performance of the current predictive control, without the need to raise the sampling frequency of the system. The principles for choosing the optimal vectors and their respective duration are based on the expressions for FOC method and on the concept of deadbeat operation. In addition, the impact of errors on parameters is minimized by means of a discrete disturbance observer embedded in the MPCC control method. With the estimation of the impacts caused by changes of parameters and not modeled dynamics it is possible to correct the errors of the control system, but keeping their transient and steady state characteristics. Results of computational simulation as well as results obtained in experimental tests are presented for evaluation and proof of the proposals.