Ensuring the reliable operation of engineering systems has long been a subject of great practical and academic interest. This interest is clearly demonstrated by the preponderance of literature in the area of Fault Detection and Diagnosis (FDD) and Fault Tolerant Control (FTC), spanning the past three decades. However, increasingly stringent performance and safety requirements have led to engineering systems with progressively increasing complexity. This complexity has rendered many traditional FDD and FTC methods exceedingly cumbersome, often to the point of infeasibility. This thesis aims to enable FDD and FTC for complex engineering systems of interacting dynamic subsystems. For such systems, generic FDD/FTC methods have remained elusive. Effects caused by nonlinearities, interactions between subsystems and varying usage patterns complicate FDD and FTC. The goal of this thesis is to develop methods for FDD and FTC that will allow decoupling of anomalies occurred inside the monitored system from those occurred in the systems affecting the monitored system, as well as enabling performance recovery of the monitored system. In pursuit of these goals, FDD and FTC methods are explored that can account for operating regime-dependent effects in monitoring, diagnosis, prognosis and performance recovery for two classes of machines: those that operate in modes that can change only at distinct times (which often occur in manufacturing opera- tions such as drilling, milling, turning) and for those that operate in regimes that are continuously varying (such as automotive systems or electric motors). For machines that operate in modes that can change only at distinct times, a degradation model is postulated which describes how the system degrades over time for each operating regime. Using the framework of Hidden Markov Models (HMMs), modeling and identification tools are developed that enable identification a HMM of degradation for each machine operation. In the sequel, monitoring and prognosis methods that naturally follow from the framework of HMMs are also presented. The modeling and monitoring methodology is then applied to a real-world semiconductor manufacturing process using data provided by a major manufacturer. For machines that operate in regimes that are continuously varying, a behavioral model is postulated that describes the input-output dynamics of the nor- mal system in different operating regimes. Monitoring methods are presented that have the capability to account for operating regime-dependent modeling accuracies and isolate faults that have not been anticipated and for which no fault models are available. By conducting fault detection in a regime-dependent fashion, changes in modeling errors that are due to operating regime changes can be successfully distinguished from changes that are due to truly faulty operation caused by changes in the system dynamics. Enabled by this, unanticipated faults can be isolated through proliferation of the fault detection through the various subsystems of the anoma- lous system. The FDD methodology is applied to detect and diagnose faults for a multiple-input multiple-output Exhaust Gas Recirculation system in a diesel engine. Finally, methods to facilitate the recovery of normal system behavior are detailed. Using the same local model structure that was pursued for behavioral models, it is envisioned that the nominal controller will be reconfigured to attempt to recover nominal behavior as much as possible. To enable this reconfiguration, methods for automated design of closed-loop controllers for the local modeling structure are presented. Using a model-predictive approach with rigorous stability considerations, it is shown that the controllers can track a reference trajectory. Such a trajectory could be generated by any model that satisfies the control objectives, for normal or faulty systems. The controllers are then demonstrated on a benchmark nonlinear system that is nonlinear in the control. / text

Mobile systems, such as ground robots and electric vehicles, inherently operate in stochastic environments where load demands are largely unknown. Onboard energy storage, most commonly an electrochemical battery system, can significantly constrain operation. As such, mission planning and control of mobile systems can benefit from a priori knowledge about battery dynamics and constraints, especially the rate-capacity and recovery effects. To help overcome overly conservative predictions common with most existing battery remaining run-time algorithms, a prediction scheme was proposed. For characterization of a priori unknown power loads, an unsupervised Gaussian mixture routine identifies/clusters the measured power loads, and a jump-Markov chain characterizes the load transients. With the jump-Markov load forecasts, a model-based particle filter scheme predicts battery remaining run-time. Monte Carlo simulation studies demonstrate the marked improvement of the proposed technique. It was found that the increase in computational complexity from using a particle filter was justified for power load transient jumps greater than 13.4% of total system power. A multivariable reliability method was developed to assess the feasibility of a planned mission. The probability of mission completion is computed as the reliability integral of mission time exceeding the battery run-time. Because these random variables are inherently dependent, a bivariate characterization was necessary and a method is presented for online estimation of the process correlation via Bayesian updating. Finally, to abate transient shutdown of mobile systems, a model predictive control scheme is proposed that enforces battery terminal voltage constraints under stochastic loading conditions. A Monte Carlo simulation study of a small ground vehicle indicated significant improvement in both time and distance traveled as a result. For evaluation of the proposed methodologies, a laboratory terrain environment was designed and constructed for repeated mobile system discharge studies. The test environment consists of three distinct terrains. For each discharge study, a small unmanned ground vehicle traversed the stochastic terrain environment until battery exhaustion. Results from field tests with a Packbot ground vehicle in generic desert terrain were also used. Evaluation of the proposed prediction algorithms using the experimental studies, via relative accuracy and [alpha]-[lambda] prognostic metrics, indicated significant gains over existing methods. / text

This work considers how various integrated energy systems can be managed in order to provide economic or energetic benefits. Energy systems can gain additional degrees of freedom by incorporating some form of energy storage (in this work, thermal energy storage), and the increasing penetration of smart grid technologies provides a wealth of data for both modeling and management. Data used for the system models here come primarily from the Pecan Street Smart Grid Demonstration Project in Austin, Texas, USA. Other data are from the Austin Energy Mueller Energy Center and the University of Texas Hal C. Weaver combined heat and power plant. Systems considered in this work include thermal energy storage, chiller plants, combined heat and power plants, turbine inlet cooling, residential air conditioning, and solar photovoltaics. These systems are modeled and controlled in integrated environments in order to provide system benefits. In a district cooling system with thermal energy storage, combined heat and power, and turbine inlet cooling, model-based optimization strategies are able to reduce peak demand and decrease cooling electricity costs by 79%. Smart grid data are employed to consider a system of 900 residential homes in Austin. In order to make the system model tractable for a model predictive controller, a reduced-order home modeling strategy is developed that maps thermostat set points to air conditioner electricity consumption. When the model predictive controller is developed for the system, the system is able to reduce total peak demand by 9%. Further work with the model of 900 residential homes presents a modified dual formulation for determining the optimal prices that produce a desired result in the residential homes. By using the modified dual formulation, it is found that the optimal pricing strategy for peak demand reduction is a critical peak pricing rate structure, and that those prices can be used in place of centralized control strategies to achieve peak reduction goals. / text

Human-machine cooperative or co-robotics has been recognized as the next generation of robotics. In contrast to current systems that use limited-reasoning strategies or address problems in narrow contexts, new co-robot systems will be characterized by their flexibility, resourcefulness, varied modeling or reasoning approaches, and use of real-world data in real time, demonstrating a level of intelligence and adaptability seen in humans and animals. The research I focused is in the two sub-field of co-robotics: teleoperation and telepresence. We firstly explore the ways of teleoperation using mixed reality techniques. I proposed a new type of display: hybrid-reality display (HRD) system, which utilizes commodity projection device to project captured video frame onto 3D replica of the actual target surface. It provides a direct alignment between the frame of reference for the human subject and that of the displayed image. The advantage of this approach lies in the fact that no wearing device needed for the users, providing minimal intrusiveness and accommodating users eyes during focusing. The field-of-view is also significantly increased. From a user-centered design standpoint, the HRD is motivated by teleoperation accidents, incidents, and user research in military reconnaissance etc. Teleoperation in these environments is compromised by the Keyhole Effect, which results from the limited field of view of reference. The technique contribution of the proposed HRD system is the multi-system calibration which mainly involves motion sensor, projector, cameras and robotic arm. Due to the purpose of the system, the accuracy of calibration should also be restricted within millimeter level. The followed up research of HRD is focused on high accuracy 3D reconstruction of the replica via commodity devices for better alignment of video frame. Conventional 3D scanner lacks either depth resolution or be very expensive. We proposed a structured light scanning based 3D sensing system with accuracy within 1 millimeter while robust to global illumination and surface reflection. Extensive user study prove the performance of our proposed algorithm. In order to compensate the unsynchronization between the local station and remote station due to latency introduced during data sensing and communication, 1-step-ahead predictive control algorithm is presented. The latency between human control and robot movement can be formulated as a linear equation group with a smooth coefficient ranging from 0 to 1. This predictive control algorithm can be further formulated by optimizing a cost function. We then explore the aspect of telepresence. Many hardware designs have been developed to allow a camera to be placed optically directly behind the screen. The purpose of such setups is to enable two-way video teleconferencing that maintains eye-contact. However, the image from the see-through camera usually exhibits a number of imaging artifacts such as low signal to noise ratio, incorrect color balance, and lost of details. Thus we develop a novel image enhancement framework that utilizes an auxiliary color+depth camera that is mounted on the side of the screen. By fusing the information from both cameras, we are able to significantly improve the quality of the see-through image. Experimental results have demonstrated that our fusion method compares favorably against traditional image enhancement/warping methods that uses only a single image.

Cyber-Physical Systems (CPS) are systems of collaborating computational elements controlling physical entities via communication. Such systems involve control processes of physical entities and computational processes. The control complexities originated from the physical dynamics and systematic constraints are difficult for traditional control approaches (e.g., PID control) to handle without an exponential increase in design/test etc. costs. Model predictive control (MPC) predicts and produces optimized control inputs based on its predictive model according to a cost function under given constraints. This control scheme has some attractive features for CPSs: it handles constraints systematically, and generates behavior prediction with respective control inputs simultaneously. However, MPC approaches are computationally intensive, and the computation burden generally grows as a predictive model more closely approximates a nonlinear plant (in order to achieve more accurate behavior). The computational burden of predictive methods can be addressed through model reduction at the cost of higher divergence between prediction and actual behavior. This work introduces a metric called uncontrollable divergence, and proposes a mechanism using the metric to select the model to use in the predictive controller (assuming that a set of predictive models are available). The metric reveals the divergence between predicted and true states caused by return time and model mismatch. More precisely, a map of uncontrollable divergence plotted over the state space gives the criterion to judge where a specific model can outperform others. With this metric and the mechanism, this work designs a controller that switches at runtime among a set of predictive controllers in which respective models are deployed. The resulting controller is a hybrid predictive controller. In addition to design and runtime tools, this work also studies stability conditions for hybrid model predictive controllers in two approaches. One is average dwell time based, and it does not rely on the offline computation that studies the system properties. The other one uses a reference Lyapunov function instead of multiple Lyapunov functions derived from multiple predictive controllers. This approach implicitly depends on the offline numerical solutions of certain systematic properties. The term "boundedness" is preferable in this context since it accepts numerical error and approximations. Two examples, vertical takeoff and landing aerial vehicle control and ground vehicle control, are used to demonstrate the approach of hybrid MPC.

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Arbetet har utförts på Saab Bofors Dynamics i Karlskoga och dess syfte var att undersöka om det är möjligt att använda modellbaserad prediktionsreglering, MPC, vid slutfasstyrning av en viss typ av robot. Som referensram används linjärkvadratisk reglering, LQ, eftersom denna reglermetod har undersökts tidigare och visat sig fungera bra vid slutfasstyrning, dock för en annan typ av robot. Anledningen till att man vill undersöka om det är möjligt att använda MPC är att styrlagen enkelt tar hand om begränsningar på systemet på ett direkt och intuitivt sätt. Styrlagarnas uppgift är att styra en robot i dess slutfas då det finns krav och önskemål på roboten som bör vara uppfyllda. Till exempel finns det begränsningar på styrsignalen samt önskemål om att träff ska ske i en viss träffpunkt och även med en viss träffvinkel. För att utvärdera resultaten undersöks och jämförs de två styrlagarnas prestanda och robusthet. För att kunna utvärdera styrlagarnas egenskaper och jämföra dem implementeras de båda i en befintlig detaljerad simuleringsmiljö, som har utvecklats på Saab Bofors Dynamics i Karlskoga. De prestanda och robusthetstester som har utförts uppvisar små skillnader på de två styrlagarna och slutsatsen blir därmed att det är möjligt att använda modellbaserad prediktionsreglering vid slutfasstyrning av en viss typ av robot eftersom det sedan tidigare är känt att linjärkvadratisk reglering är en bra styrlag att använda. För att se vilken av de två styrlagarna som är bäst vid slutfasstyrning av en viss typ av robot behöver det göras vissa ändringar och mer detaljerade undersökningar utföras.

The actualization of real-time economically optimal process operation requires proper integration of real-time optimization (RTO) and dynamic control. This dissertation addresses the integration problem and provides a formal design technique that properly integrates RTO and model predictive control (MPC) under parametric uncertainties. The task is posed as an adaptive extremum-seeking control (ESC) problem in which the controller is required to steer the system to an unknown setpoint that optimizes a user-specified objective function. The integration task is first solved for linear uncertain systems. Then a method of determining appropriate excitation conditions for nonlinear systems with uncertain reference setpoint is provided. Since the identification of the true cost surface is paramount to the success of the integration scheme, novel parameter estimation techniques with better convergence properties are developed. The estimation routine allows exact reconstruction of the system's unknown parameters in finite-time. The applicability of the identifier to improve upon the performance of existing adaptive controllers is demonstrated. Adaptive nonlinear model predictive controllers are developed for a class of constrained uncertain nonlinear systems. Rather than relying on the inherent robustness of nominal MPC, robustness features are incorporated in the MPC framework to account for the effect of the model uncertainty. The numerical complexity and/or the conservatism of the resulting adaptive controller reduces as more information becomes available and a better uncertainty description is obtained. Finally, the finite-time identification procedure and the adaptive MPC are combined to achieve the integration task. The proposed design solves the economic optimization and control problem at the same frequency. This eliminates the ensuing interval of "no-feedback" that occurs between economic optimization interval, thereby improving disturbance attenuation. / Thesis (Ph.D, Chemical Engineering) -- Queen's University, 2008-08-08 12:30:47.969

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