Browsing by Subject "Robust control"
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Item Analysis, modeling, and control of highly-efficient hybrid dc-dc conversion systems(2012-12) Zhao, Ruichen; Kwasinski, Alexis; Aristotle, Arapostathis; Grady, William; Akella, Maruthi; Driga, MirceaThis dissertation studies hybrid dc-dc power conversion systems based on multiple-input converters (MICs), or more generally, multiport converters. MICs allow for the integration of multiple distributed generation sources and loads. Thanks to the modular design, an MIC yields a scalable system with independent control in all sources. Additional characteristics of MICs include the improved reliability and reduced cost. This dissertation mainly studies three issues of MICs: efficiency improvement, modeling, and control. First, this work develops a cost-effective design of a highly-efficient non-isolated MIC without additional components. Time-multiplexing (TM) MICs, which are driven by a time-multiplexing switching control scheme, contain forward-conducting-bidirectional-blocking (FCBB) switches. TM-MICs are considered to be subject to low efficiency because of high power loss introduced by FCBB switches. In order to reduce the power loss in FCBB switches, this work adopts a modified realization of the FCBB switch and proposes a novel switching control strategy. The design and experimental verifications are motivated through a multiple-input (MI) SEPIC converter. Through the design modifications, the switching transients are improved (comparing to the switching transients in a conventional MI-SEPIC) and the power loss is significantly reduced. Moreover, this design maintains a low parts-count because of the absence of additional components. Experimental results show that for output power ranging from 1 W to 220 W, the modified MIC presents high efficiency (96 % optimally). The design can be readily extended to a general n-input SEPIC. The same modifications can be applied to an MI-Ćuk converter. Second, this dissertation examines the modeling of TM-MICs. In the dynamic equations of a TM-MIC, a state variable from one input leg is possible to be affected by state variables and switching functions associated with other input legs. In this way, inputs are coupled both topologically and in terms of control actions through switching functions. Coupling among the state variable and the time-multiplexing switching functions complicate TM-MICs’ behavior. Consequently, substantial modeling errors may occur when a classical averaging approach is used to model an MIC even with moderately high switching frequencies or small ripples. The errors may increase with incremental number of input legs. In addition to demonstrating the special features on MIC modeling, this dissertation uses the generalized averaging approach to generate a more accurate model, which is also used to derive a small-signal model. The proposed model is an important tool that yields better results when analyzing power budgeting, performing large-signal simulations, and designing controllers for TM-MICs via a more precise representation than classical averaging methods. Analyses are supported by simulations and experimental results. Third, this dissertation studies application of a decentralized controller on an MI-SEPIC. For an MIC, a multiple-input-multiple-output (MIMO) state-space representation can be derived by an averaging method. Based on the averaged MIMO model, an MIMO small-signal model can be generated. Both conventional method and modern multivariable frequency analysis are applied to the small-signal model of an MI-SEPIC to evaluate open-loop and closed-loop characteristics. In addition to verifying the nominal stability and nominal performance, this work evaluates robust stability and robust performance with the structured singular value. The robust performance test shows that a compromised performance may be expected under the decentralized control. Simulations and experimental results verify the theoretical analysis on stability and demonstrate that the decentralized PI controller could be effective to regulate the output of an MIC under uncertainties. Finally, this work studies the control of the MIMO dc-dc converter serving as an active distribution node in an intelligent dc distribution grid. The unified model of a MIMO converter is derived, enabling a systematical analysis and control design that allows this converter to control power flow in all its ports and to act as a power buffer that compensates for mismatches between power generation and consumption. Based on the derived high-order multivariable model, a robust controller is designed with disturbance-attenuation and pole-placement constraints via the linear matrix inequality (LMI) synthesis. The closed-loop robust stability and robust performance are tested through the structured singular value synthesis. Again, the desirable stability and performance are verified by simulations and experimental results.Item Compensation-oriented quality control in multistage manufacturing processes(2012-08) Jiao, Yibo; Djurdjanovic, Dragan; Morton, David P.; Barnes, John W.; Edgar, Thomas F.; Ding, YuSignificant research has been initiated recently to devise control strategies that could predict and compensate manufacturing errors using so called explicit Stream-of-Variation(SoV) models that relate process parameters in a Multistage Manufacturing Process (MMP) with product quality. This doctoral dissertation addresses several important scientific and engineering problems that will significantly advance the model-based, active control of quality in MMPs. First, we will formally introduce and study the new concept of compensability in MMPs, analogous to the concept of controllability in the traditional control theory. The compensability in an MMP is introduced as the property denoting one’s ability to compensate the errors in quality characteristics of the workpiece, given the allocation and character of measurements and controllable tooling. The notions of “within-station” and “between-station” compensability are also introduced to describe the ability to compensate upstream product errors within a given operation or between arbitrarily selected operations, respectively. The previous research also failed to concurrently utilize the historical and on-line measurements of product key characteristics for active model-based quality control. This dissertation will explore the possibilities of merging the well-known Run-to-Run (RtR) quality control methods with the model-based feed-forward process control methods. The novel method is applied to the problem of control of multi-layer overlay errors in lithography processes in semiconductor manufacturing. In this work, we first devised a multi-layer overlay model to describe the introduction and flow of overlay errors from one layer to the next, which was then used to pursue a unified approach to RtR and feedforward compensation of overlay errors in the wafer. At last, we extended the existing methodologies by considering inaccurately indentified noise characteristics in the underlying error flow model. This is also a very common situation, since noise characteristics are rarely known with absolute accuracy. We formulated the uncertainty in process noise characteristics using Linear Fractional Transformation (LFT) representation and solved the problem by deriving a robust control law that guaranties the product quality even under the worst case scenario of parametric uncertainties. Theoretical results have been evaluated and demonstrated using a linear state-space model of an actual industrial process for automotive cylinder head machining.Item Optimally-robust nonlinear control of a class of robotic underwater vehicles(2006) Josserand, Timothy Matthew; Fernandez, Benito R.The subject of this dissertation is the optimally-robust nonlinear control of a class of robotic underwater vehicles (RUVs). The RUV class is characterized by high fineness ratios (length-to-diameter), axial symmetry, and passive roll stability. These vehicles are optimized for robotic applications needing power efficiency for long-range autonomous operations and motion stability for sensor performance improvement. A familiar example is the REMUS vehicle. The particular robot class is further identified by an inconsistent actuator arrangement where the number of inputs is fewer than the number of degrees of freedom, by the loss of controllability at low surge speeds due to the use of fin-based control actuation, and by an inherent heading instability. Therefore, this important type of RUV comprises an interesting and challenging class of systems to study from a control theoretic perspective. The optimally-robust nonlinear control method combines sliding mode control with stochastic state and model uncertainty estimation. First a regular form sliding mode control law is developed for the heading and depth control of the RUV class. The Particle Filter algorithm is then modified and applied to the particular case of estimating not only the RUV state for control feedback but also the functional uncertainty associated with partially modeled shallow water wave disturbances. The functional uncertainty estimate is used to dynamically adjust the sliding mode controller performance term gain according to the estimate of the wave phase and the RUV’s orientation with respect to the predominate wave direction. As a result, the RUV experiences increased performance over constant gain and Kalman Filter methods in terms of heading stability which increases effectiveness and decreased actuator power consumption which increases the RUV mission time. The proposed technique is general enough to be applied to other systems. An experimental RUV was designed and constructed to compare the performance of the regular form sliding mode controller with the conventional PID-type controller. It is demonstrated that the more complicated formulas of the regular form sliding mode controller can still be implemented real-time in an embedded system and that the controller’s performance with regard to modeling uncertainty justifies the added complexity.Item Phase space planning for robust locomotion(2013-08) Zhao, Ye, active 2013; Sentis, LuisManeuvering through 3D structures nimbly is pivotal to the advancement of legged locomotion. However, few methods have been developed that can generate 3D gaits in those terrains and fewer if none can be generalized to control dynamic maneuvers. In this thesis, foot placement planning for dynamic locomotion traversing irregular terrains is explored in three dimensional space. Given boundary values of the center of mass' apexes during the gait, sagittal and lateral Phase Plane trajectories are predicted based on multi-contact and inverted pendulum dynamics. To deal with the nonlinear dynamics of the contact motions and their dimensionality, we plan a geometric surface of motion beforehand and rely on numerical integration to solve the models. In particular, we combine multi-contact and prismatic inverted pendulum models to resolve feet transitions between steps, allowing to produce trajectory patterns similar to those observed in human locomotion. Our contributions lay in the following points: (1) the introduction of non planar surfaces to characterize the center of mass' geometric behavior; (2) an automatic gait planner that simultaneously resolves sagittal and lateral feet placements; (3) the introduction of multi-contact dynamics to smoothly transition between steps in the rough terrains. Data driven methods are powerful approaches in absence of accurate models. These methods rely on experimental data for trajectory regression and prediction. Here, we use regression tools to plan dynamic locomotion in the Phase Space of the robot's center of mass and we develop nonlinear controllers to accomplish the desired plans with accuracy and robustness. In real robotic systems, sensor noise, simplified models and external disturbances contribute to dramatic deviations of the actual closed loop dynamics with respect to the desired ones. Moreover, coming up with dynamic locomotion plans for bipedal robots and in all terrains is an unsolved problem. To tackle these challenges we propose here two robust mechanisms: support vector regression for data driven model fitting and contact planning, and trajectory based sliding mode control for accuracy and robustness. First, support vector regression is utilized to learn the data set obtained through numerical simulations, providing an analytical solution to the nonlinear locomotion dynamics. To approximate typical Phase Plane behaviors that contain infinite slopes and loops, we propose to use implicit fitting functions for the regression. Compared to mainstream explicit fitting methods, our regression method has several key advantages: 1) it models high dimensional Phase Space states by a single unified implicit function; 2) it avoids trajectory over-fitting; 3) it guarantees robustness to noisy data. Finally, based on our regression models, we develop contact switching plans and robust controllers that guarantee convergence to the desired trajectories. Overall, our methods are more robust and capable of learning complex trajectories than traditional regression methods and can be easily utilized to develop trajectory based robust controllers for locomotion. Various case studies are analyzed to validate the effectiveness of our methods including single and multi step planning in a numerical simulation and swing foot trajectory control on our Hume bipedal robot.