Electrical & Computer Engineering Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2765
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Item ESTIMATION AND CONTROL OF NONLINEAR SYSTEMS: MODEL-BASED AND MODEL-FREE APPROACHES(2020) Goswami, Debdipta; Paley, Derek A.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)State estimation and subsequent controller design for a general nonlinear system is an important problem that have been studied over the past decades. Many applications, e.g., atmospheric and oceanic sampling or lift control of an airfoil, display strongly nonlinear dynamics with very high dimensionality. Some of these applications use smaller underwater or aerial sensing platforms with insufficient on-board computation power to use a Monte-Carlo approach of particle filters. Hence, they need a computationally efficient filtering method for state-estimation without a severe penalty on the performance. On the other hand, the difficulty of obtaining a reliable model of the underlying system, e.g., a high-dimensional fluid dynamical environment or vehicle flow in a complex traffic network, calls for the design of a data-driven estimation and controller when abundant measurements are present from a variety of sensors. This dissertation places these problems in two broad categories: model-based and model-free estimation and output feedback. In the first part of the dissertation, a semi-parametric method with Gaussian mixture model (GMM) is used to approximate the unknown density of states. Then a Kalman filter and its nonlinear variants are employed to propagate and update each Gaussian mode with a Bayesian update rule. The linear observation model permits a Kalman filter covariance update for each Gaussian mode. The estimation error is shown to be stochastically bounded and this is illustrated numerically. The estimate is used in an observer-based feedback control to stabilize a general closed-loop system. A transferoperator- based approach is then proposed for the motion update for Bayesian filtering of a nonlinear system. A finite-dimensional approximation of the Perron-Frobenius (PF) operator yields a method called constrained Ulam dynamic mode decomposition (CUDMD). This algorithm is applied for output feedback of a pitching airfoil in unsteady flow. For the second part, an echo-state network (ESN) based approach equipped with an ensemble Kalman filter is proposed for data-driven estimation of a nonlinear system from a time series. A random reservoir of recurrent neural connections with the echo-state property (ESP) is trained from a time-series data. It is then used as a model-predictor for an ensemble Kalman filter for sparse estimation. The proposed data-driven estimation method is applied to predict the traffic flow from a set of mobility data of the UMD campus. A data-driven model-identification and controller design is also developed for control-affine nonlinear systems that are ubiquitous in several aerospace applications. We seek to find an approximate linear/bilinear representation of these nonlinear systems from data using the extended dynamic mode decomposition algorithm (EDMD) and apply Liealgebraic methods to analyze the controllability and design a controller. The proposed method utilizes the Koopman canonical transform (KCT) to approximate the dynamics into a bilinear system (Koopman bilinear form) under certain assumptions. The accuracy of this approximation is then analytically justified with the universal approximation property of the Koopman eigenfunctions. The resulting bilinear system is then subjected to controllability analysis using the Myhill semigroup and Lie algebraic structures, and a fixed endpoint optimal controller is designed using the Pontryagin’s principle.Item Optimality of Event-Based Policies for Decentralized Estimation over Shared Networks(2016) Vasconcelos, Marcos Muller; Martins, Nuno C; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Cyber-physical systems often consist of multiple non-collocated components that sense, exchange information and act as a team through a network. Although this new paradigm provides convenience, flexibility and robustness to modern systems, design methods to achieve optimal performance are elusive as they must account for certain detrimental characteristics of the underlying network. These include constrained connectivity among agents, rate-limited communication links, physical noise at the antennas, packet drops and interference. We propose a new class of problems in optimal networked estimation where multiple sensors operating as a team communicate their measurements to a fusion center over an interference prone network modeled by a collision channel. Using a team decision theoretic approach, we characterize jointly optimal communication policies for one-shot problems under different performance criteria. First we study the problem of estimating two independent continuous random variables observed by two different sensors communicating with a fusion center over a collision channel. For a minimum mean squared estimation error criterion, we show that there exist team-optimal strategies where each sensor uses a threshold policy. This result is independent of the distribution of the observations and, can be extended to vector observations and to any number of sensors. Consequently, the existence of team-optimal threshold policies is a result of practical significance, because it can be applied to a wide class of systems without requiring collision avoidance protocols. Next we study the problem of estimating independent discrete random variables over a collision channel. Using two different criteria involving the probability of estimation error, we show the existence of team-optimal strategies where the sensors either transmit all but the most likely observation; transmit only the second most likely observation; or remain always silent. These results are also independent of the distributions and are valid for any number of sensors. In our analysis, the proof of the structural result involves the minimization of a concave functional, which is an evidence of the inherent complexity of team decision problems with nonclassical information structure. In the last part of the dissertation, the assumption on the cooperation among sensors is relaxed, and we show that similar structural results can also be obtained for systems with one or more selfish sensors. Finally the assumption of the independence is lifted by introducing the observation of a common random variable in addition to the private observations of each sensor. The structural result obtained provides valuable insights on the characterization of team-optimal policies for a general correlation structure between the observed random variables.Item Thermal Tracking and Estimation for Microprocessors(2016) Zhang, Yufu; Srivastava, Ankur; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Due to increasing integration density and operating frequency of today's high performance processors, the temperature of a typical chip can easily exceed 100 degrees Celsius. However, the runtime thermal state of a chip is very hard to predict and manage due to the random nature in computing workloads, as well as the process, voltage and ambient temperature variability (together called PVT variability). The uneven nature (both in time and space) of the heat dissipation of the chip could lead to severe reliability issues and error-prone chip behavior (e.g. timing errors). Many dynamic power/thermal management techniques have been proposed to address this issue such as dynamic voltage and frequency scaling (DVFS), clock gating and etc. However, most of such techniques require accurate knowledge of the runtime thermal state of the chip to make efficient and effective control decisions. In this work we address the problem of tracking and managing the temperature of microprocessors which include the following sub-problems: (1) how to design an efficient sensor-based thermal tracking system on a given design that could provide accurate real-time temperature feedback; (2) what statistical techniques could be used to estimate the full-chip thermal profile based on very limited (and possibly noise-corrupted) sensor observations; (3) how do we adapt to changes in the underlying system's behavior, since such changes could impact the accuracy of our thermal estimation. The thermal tracking methodology proposed in this work is enabled by on-chip sensors which are already implemented in many modern processors. We first investigate the underlying relationship between heat distribution and power consumption, then we introduce an accurate thermal model for the chip system. Based on this model, we characterize the temperature correlation that exists among different chip modules and explore statistical approaches (such as those based on Kalman filter) that could utilize such correlation to estimate the accurate chip-level thermal profiles in real time. Such estimation is performed based on limited sensor information because sensors are usually resource constrained and noise-corrupted. We also took a further step to extend the standard Kalman filter approach to account for (1) nonlinear effects such as leakage-temperature interdependency and (2) varying statistical characteristics in the underlying system model. The proposed thermal tracking infrastructure and estimation algorithms could consistently generate accurate thermal estimates even when the system is switching among workloads that have very distinct characteristics. Through experiments, our approaches have demonstrated promising results with much higher accuracy compared to existing approaches. Such results can be used to ensure thermal reliability and improve the effectiveness of dynamic thermal management techniques.Item Precoder Detection for Cooperative Decode-and-Forward Relaying in OFDMA Systems(2016) Valluri, Abhijit Kiran; La, Richard J; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)We consider an LTE network where a secondary user acts as a relay, transmitting data to the primary user using a decode-and-forward mechanism, transparent to the base-station (eNodeB). Clearly, the relay can decode symbols more reliably if the employed precoder matrix indicators (PMIs) are known. However, for closed loop spatial multiplexing (CLSM) transmit mode, this information is not always embedded in the downlink signal, leading to a need for effective methods to determine the PMI. In this thesis, we consider 2x2 MIMO and 4x4 MIMO downlink channels corresponding to CLSM and formulate two techniques to estimate the PMI at the relay using a hypothesis testing framework. We evaluate their performance via simulations for various ITU channel models over a range of SNR and for different channel quality indicators (CQIs). We compare them to the case when the true PMI is known at the relay and show that the performance of the proposed schemes are within 2 dB at 10% block error rate (BLER) in almost all scenarios. Furthermore, the techniques add minimal computational overhead over existent receiver structure. Finally, we also identify scenarios when using the proposed precoder detection algorithms in conjunction with the cooperative decode-and-forward relaying mechanism benefits the PUE and improves the BLER performance for the PUE. Therefore, we conclude from this that the proposed algorithms as well as the cooperative relaying mechanism at the CMR can be gainfully employed in a variety of real-life scenarios in LTE networks.Item Model Based Optimization and Design of Secure Systems(2013) Malik, Waseem Ansar; Martins, Nuno C; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)ABSTRACT Title of dissertation: MODEL BASED OPTIMIZATION AND DESIGN OF SECURE SYSTEMS Waseem Ansar Malik, Doctor of Philosophy, 2013 Dissertation directed by: Prof. Nuno C. Martins Department of Electrical and Computer Engineering University of Maryland, College Park Dr. Ananthram Swami Computational and Information Sciences Directorate Army Research Laboratory Control systems are widely used in modern industry and find wide applications in power systems, nuclear and chemical plants, the aerospace industry, robotics, communication devices, and embedded systems. All these systems typically rely on an underlying computing and networking infrastructure which has considerable security vulnerabilities. The biggest threat, in this age and time, to modern systems are cyber attacks from adversaries. Recent cyber attacks have practically shut down government websites affecting government operation, undermined financial institutions, and have even infringed on public privacy. Thus it is extremely important to conduct studies on the design and analysis of secure systems. This work is an effort in this research direction and is mainly focused on incorporating security in the design of modern control systems. In the first part of this dissertation, we present a linear quadratic optimal control problem subjected to security constraints. We consider an adversary which can make partial noisy measurements of the state. The task of the controller is to generate control sequences such that the adversary is unable to estimate the terminal state. This is done by minimizing a quadratic cost while satisfying security constraints. The resulting optimization problems are shown to be convex and the optimal solution is computed using Lagrangian based techniques. For the case when the terminal state has a discrete distribution the optimal solution is shown to be nonlinear in the terminal state. This is followed by considering the case when the terminal state has a continuous distribution. The resulting infinite dimensional optimization problems are shown to be convex and the optimal solution is proven to be affine in the terminal state. In the next part of this dissertation, we analyze several team decision problems subjected to security constraints. Specifically, we consider problem formulations where there are two decision makers each controlling a different dynamical system. Each decision maker receives information regarding the respective terminal states that it is required to reach and applies a control sequence accordingly. An adversary makes partial noisy measurements of the states and tries to estimate the respective terminal states. It is shown that the optimal solution is affine in the terminal state when it is identical for both systems. We also consider the general case where the terminal states are correlated. The resulting infinite dimensional optimization problems are shown to convex programs and we prove that the optimal solution is affine in the information available to the decision makers. Next, a stochastic receding horizon control problem is considered and analyzed. Specifically, we consider a system with bounded disturbances and hard bounds on the control inputs. Utilizing a suboptimal disturbance feedback scheme, the optimization problem is shown to be convex. The problem of minimizing the empirical mean of the cost function is analyzed. We provide bounds on the disturbance sample size to compute the empirical minimum of the problem. Further, we consider the problem where there are hard computational constraints and complex on-line optimization is not feasible. This is addressed by randomly generating both the control inputs and the additive disturbances. Bounds on sample sizes are provided which guarantee a notion of a probable near minimum. Model uncertainty is also incorporated into the framework and relevant bounds are provided which guarantee a probable near minimax value. This work finds many applications in miniature devices and miniature robotics. In the final part of this dissertation, we consider a centralized intrusion detection problem with jointly optimal sensor placement. A team of sensors make measurements regarding the presence of an intruder and report their observations to a decision maker. The decision maker solves a jointly optimal detection and sensor placement problem. For the case when the number of sensors is equal to the number of placement points, we prove that uniform placement of sensors is not strictly optimal. We introduce and utilize a majorization based partial order for the placement of sensors. For the case when the number of sensors is less than or equal to six, we show that for a fixed local probability of detection (probability of false alarm) increasing the probability of false alarm (probability of detection) results in optimal placements that are higher on a majorization based partial order.