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Advanced combustion modes offer promising solutions for both emission reduction and efficiency improvement. The lower local equivalence ratio and lower peak temperature characterized by the advanced combustion mode significantly reduce the generation of the engine-out emissions (especially the soot and NOx). Although the advanced combustion mode enjoys extra-low emissions, some technical challenges prevent it from being widely applied in real practice. Combustion phasing control as auto-ignition and narrow load range are two main challenges to be addressed. The estimation and control techniques for Diesel engine targeting these two challenges are presented in four papers in this thesis. Accessing to the in-cylinder conditions is essential for a more detailed combustion estimation and further combustion control. Paper 1 and Paper 2 (Chapter II and Chapter III) introduce methods of estimating two critical in-cylinder conditions, the in-cylinder temperature and oxygen concentration. The system dynamic models are derived and the Extended Kalman filter (EKF) and smooth variable structure filter (SVSF) are utilized for the in-cylinder temperature and in-cylinder oxygen concentration estimation, respectively. The method of coordinated control for the intake conditions and the combustion process aiming at a fast and accurate combustion process response is proposed in paper 3 (Chapter IV). Disturbance rejection control in conjunction with sliding mode method is proposed to control the air- and fuel-path loop simultaneously. As an indicator to show the combustion quality and to avoid significant incomplete combustion, the unburned fuel is estimated in paper 4 (Chapter V) based on the oxygen concentration. Three filters are designed to estimate the trapped unburned fuel and their robustness against modeling errors are analyzed and compared theoretically. / Dissertation / Doctor of Philosophy (PhD) / To ultimately reduce the engine-out emissions and increase the thermal efficiency, advanced combustion modes provide promising solutions. However, several obstacles, including the narrow load range and difficulty of the combustion phasing control, prevent the advanced combustion from being widely applied in practice. To address these obstacles, detail estimation of in-cylinder gas conditions and robust control for air- and fuel-path are critical. This thesis focuses on the states estimation and control for Diesel engines aiming to address the obstacles laid by the advanced combustion modes. Four journal papers with different objectives compose this thesis. Paper 1 and Paper 2 (Chapter II and III, respectively) propose methods of estimation of the in-cylinder temperature and oxygen concentration. Paper 3 (Chapter IV) introduces the method of coordinated control of the intake conditions and the combustion process. The unburned fuel is estimated in paper 4 (Chapter V). The techniques introduced in the 4 papers are either validated through calibrated GT-Power simulations or experiments in a Diesel engine.

Design of nonlinear observers has received considerable attention since the early development of methods for linear state estimation. The most popular approach is the extended Kalman filter (EKF), that goes through significant degradation in the presence of nonlinearities, particularly if unmodeled dynamics are coupled to the process and the measurement. For uncertain nonlinear systems, adaptive observers have been introduced to estimate the unknown state variables where no priori information about the unknown parameters is available. While establishing global results, these approaches are applicable only to systems transformable to output feedback form. Over the recent years, neural network (NN) based identification and estimation schemes have been proposed that relax the assumptions on the system at the price of sacrificing on the global nature of the results. However, most of the NN based adaptive observer approaches in the literature require knowledge of the full dimension of the system, therefore may not be suitable for systems with unmodeled dynamics. We first propose a novel approach to nonlinear state estimation from the perspective of augmenting a linear time invariant observer with an adaptive element. The class of nonlinear systems treated here are finite but of otherwise unknown dimension. The objective is to improve the performance of the linear observer when applied to a nonlinear system. The approach relies on the ability of the NNs to approximate the unknown dynamics from finite time histories of available measurements. Next we investigate nonlinear state estimation from the perspective of adaptively augmenting an existing time varying observer, such as an EKF. EKFs find their applications mostly in target tracking problems. The proposed approaches are robust to unmodeled dynamics, including unmodeled disturbances. Lastly, we consider the problem of adaptive estimation in the presence of feedback control for a class of uncertain nonlinear systems with unmodeled dynamics and disturbances coupled to the process. The states from the adaptive EKF are used as inputs to the control law, which in target tracking usually takes the form of a guidance law. The applications of this approach lie in the areas of missile-target tracking, formation flight control and obstacle avoidance.

Thesis (M.Eng.(Control Engineering))--University of Pretoria, 2008. / Summary in English. Includes bibliographical references.

This thesis focuses on developing algorithms for brushless AC motor control. First part of the thesis contains derivation of mathematical model and overview of selected sensor and sensorless control methods. Second part introduces simulation model of the motor, developed in Matlab/Simulink environment, with usage of SimPowerSystems toolbox. Following chapter describes realization of control algorithm, utilizing Hall sensors and position estimation. After that, sensorless rotor position estimation module is developed, and its implementation into the model is mentioned. Last chapters deal with development of graphical user interface, meant for changing selected motor and control parameters, and they also summarize and compare achieved results.

The use of visual sensors in providing the necessary information for the autonomous guidance and navigation of the unmanned-air vehicles (UAV) or micro-air vehicles (MAV) applications is inspired by biological systems and is motivated first of all by the reduction of the navigational sensor cost. Also, visual sensors can be more advantageous in military operations since they are difficult to detect. However, the design of a reliable guidance, navigation and control system for aerial vehicles based only on visual information has many unsolved problems, ranging from hardware/software development to pure control-theoretical issues, which are even more complicated when applied to the tracking of maneuvering unknown targets. This dissertation describes guidance law design and implementation algorithms for autonomous tracking of a flying target, when the information about the target's current position is obtained via a monocular camera mounted on the tracking UAV (follower). The visual information is related to the target's relative position in the follower's body frame via the target's apparent size, which is assumed to be constant, but otherwise unknown to the follower. The formulation of the relative dynamics in the inertial frame requires the knowledge of the follower's orientation angles, which are assumed to be known. No information is assumed to be available about the target's dynamics. The follower's objective is to maintain a desired relative position irrespective of the target's motion. Two types of guidance laws are designed and implemented in the dissertation. The first one is a smooth guidance law that guarantees asymptotic tracking of a target, the velocity of which is viewed as a time-varying disturbance, the change in magnitude of which has a bounded integral. The second one is a smooth approximation of a discontinuous guidance law that guarantees bounded tracking with adjustable bounds when the target's acceleration is viewed as a bounded but otherwise unknown time-varying disturbance. In both cases, in order to meet the objective, an intelligent excitation signal is added to the reference commands. These guidance laws are modified to accommodate measurement noise, which is inherently available when using visual sensors and image processing algorithms associated with them. They are implemented on a full scale non-linear aircraft model using conventional block backstepping technique augmented with a neural network for approximation of modeling uncertainties and atmospheric turbulence resulting from the closed-coupled flight of two aerial vehicles. / Ph. D.

In industrial drives market, requirements related to control quality and price of drives are important. In low cost drives, one of the aims is achieving speed estimation accuracy. Since motor parameters are required to estimate speed and sometimes it is impractical to do no-load and locked rotor tests, it is necessary to estimate motor parameters from motor label or by simple measurements. Throughout this study, some of parameter estimation and speed estimation methods found in literature are investigated and some new methods are proposed. These methods are applied to three induction motors and estimation results are compared with test results. Advantages and disadvantages of these methods are investigated. As a result of this study, the most suitable parameter and speed estimation methods amongst these methods are obtained for low cost motor drives.

Subsea development and production of hydrocarbons is challenging due to remote andharsh conditions. Recent technology development with high speed communication to subsea anddownhole equipment has created a new opportunity to both monitor and control abnormal or undesirableevents with a proactive and preventative approach rather than a reactive approach. Twospecific technology developments are high speed, long-distance fiber optic sensing for productionand completion systems and wired pipe for drilling communications. Both of these communicationsystems offer unprecedented high speed and accurate sensing of equipment and processes that aresusceptible to uncontrolled well situations, leaks, issues with flow assurance, structural integrity,and platform stability, as well as other critical monitoring and control issues. The scope of thisdissertation is to design monitoring and control systems with new theoretical developments andpractical applications. For estimators, a novel `1-norm method is proposed that is less sensitiveto data with outliers, noise, and drift in recovering the true value of unmeasured parameters. Forcontrollers, a similar `1-norm strategy is used to design optimal control strategies that utilize a comprehensivedesign with multivariate control and nonlinear dynamic optimization. A framework forsolving large scale dynamic optimization problems with differential and algebraic equations is detailedfor estimation and control. A first area of application is in fiber optic sensing and automationfor subsea equipment. A post-installable fiber optic clamp is used to transmit structural informationfor a tension leg platform. A proposed controller automatically performs ballast operationsthat both stabilize the floating structure and minimize fatigue damage to the tendons that hold thestructure in place. A second area of application is with managed pressure drilling with movinghorizon estimation and nonlinear model predictive control. The purpose of this application is tomaximize rate of drilling penetration, maintain pressure in the borehole, respond to unexpected gasinflux, detect cuttings loading and pack-off, and better manage abnormal events with the drillingprocess through automation. The benefit of high speed data accessibility is quantified as well asthe potential benefit from a combined control strategy versus separate controllers.

In this dissertation, we focus on two fundamental problems related to the navigation of ground robots and small Unmanned Aerial Vehicle (UAVs): cooperative localization and path planning. The theme running through in all of the work is the use of bearing only sensors, with a focus on monocular video cameras mounted on ground robots and UAVs. To begin with, we derive the conditions for the complete observability of the bearing-only cooperative localization problem. The key element of this analysis is the Relative Position Measurement Graph (RPMG). The nodes of an RPMG represent vehicle states and the edges represent bearing measurements between nodes. We show that graph theoretic properties like the connectivity and the existence of a path between two nodes can be used to explain the observability of the system. We obtain the maximum rank of the observability matrix without global information and derive conditions under which the maximum rank can be achieved. Furthermore, we show that for the complete observability, all of the nodes in the graph must have a path to at least two different landmarks of known location. The complete observability can also be obtained without landmarks if the RPMG is connected and at least one of the robots has a sensor which can measure its global pose, for example a GPS receiver. We validate these conditions by simulation and experimental results. The theoretical conditions to attain complete observability in a localization system is an important step towards reliable and efficient design of localization and path planning algorithms. With such conditions, a designer does not need to resort to exhaustive simulations and/or experimentation to verify whether a given selection of a control strategy, topology of the sensor network, and sensor measurements meets the observability requirements of the system. In turn, this leads to decreased requirements of time, cost, and effort for designing a localization algorithms. We use these observability conditions to develop a technique, for camera equipped UAVs, to cooperatively geo-localize a ground target in an urban terrain. We show that the bearing-only cooperative geo-localization technique overcomes the limitation of requiring a low-flying UAV to maintain line-of-sight while flying high enough to maintain GPS lock. We design a distributed path planning algorithm using receding horizon control that improves the localization accuracy of the target and of all of the UAVs while satisfying the observability conditions. Next, we use the observability analysis to explicitly design an active local path planning algorithm for UAVs. The algorithm minimizes the uncertainties in the time-to-collision (TTC) and bearing estimates while simultaneously avoiding obstacles. Using observability analysis we show that maximizing the observability and collision avoidance are complementary tasks. We provide sufficient conditions of the environment which maximizes the chances obstacle avoidance and UAV reaching the goal. Finally, we develop a reactive path planner for UAVs using sliding mode control such that it does not require range from the obstacle, and uses bearing to obstacle to avoid cylindrical obstacles and follow straight and curved walls. The reactive guidance strategy is fast, computationally inexpensive, and guarantees collision avoidance.

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