Retrospective-Cost Adaptive Control of Uncertain Hammerstein Systems Using a NARMAX Controller Structure

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97107/1/AIAA2012-4448.pd

Networked Control System Design and Parameter Estimation

Networked control systems (NCSs) are a kind of distributed control systems in which the data between control components are exchanged via communication networks. Because of the attractive advantages of NCSs such as reduced system wiring, low weight, and ease of system diagnosis and maintenance, the research on NCSs has received much attention in recent years. The first part (Chapter 2 - Chapter 4) of the thesis is devoted to designing new controllers for NCSs by incorporating the network-induced delays. The thesis also conducts research on filtering of multirate systems and identification of Hammerstein systems in the second part (Chapter 5 - Chapter 6). Network-induced delays exist in both sensor-to-controller (S-C) and controller-to-actuator (C-A) links. A novel two-mode-dependent control scheme is proposed, in which the to-be-designed controller depends on both S-C and C-A delays. The resulting closed-loop system is a special jump linear system. Then, the conditions for stochastic stability are obtained in terms of a set of linear matrix inequalities (LMIs) with nonconvex constraints, which can be efficiently solved by a sequential LMI optimization algorithm. Further, the control synthesis problem for the NCSs is considered. The definitions of H₂ and H∞ norms for the special system are first proposed. Also, the plant uncertainties are considered in the design. Finally, the robust mixed H₂/H∞ control problem is solved under the framework of LMIs. To compensate for both S-C and C-A delays modeled by Markov chains, the generalized predictive control method is modified to choose certain predicted future control signal as the current control effort on the actuator node, whenever the control signal is delayed. Further, stability criteria in terms of LMIs are provided to check the system stability. The proposed method is also tested on an experimental hydraulic position control system. Multirate systems exist in many practical applications where different sampling rates co-exist in the same system. The l₂-l∞ filtering problem for multirate systems is considered in the thesis. By using the lifting technique, the system is first transformed to a linear time-invariant one, and then the filter design is formulated as an optimization problem which can be solved by using LMI techniques. Hammerstein model consists of a static nonlinear block followed in series by a linear dynamic system, which can find many applications in different areas. New switching sequences to handle the two-segment nonlinearities are proposed in this thesis. This leads to less parameters to be estimated and thus reduces the computational cost. Further, a stochastic gradient algorithm based on the idea of replacing the unmeasurable terms with their estimates is developed to identify the Hammerstein model with two-segment nonlinearities. Finally, several open problems are listed as the future research directions

Two Identification Methods for Dual-Rate Sampled-Data Nonlinear Output-Error Systems

This paper presents two methods for dual-rate sampled-data nonlinear output-error systems. One method is the missing output estimation based stochastic gradient identification algorithm and the other method is the auxiliary model based stochastic gradient identification algorithm. Different from the polynomial transformation based identification methods, the two methods in this paper can estimate the unknown parameters directly. A numerical example is provided to confirm the effectiveness of the proposed methods

A Simplified All-ZVS Strategy for High-Frequency Triple Active Bridge Converters with Designed Magnetizing Inductance

The triple active bridge (TAB) converters that integrates the on-board charger and the auxiliary power module is ideally suited for producing a high-power density electric vehicle (EV) charger. As the power coupling among each port complicates the TAB's mode analysis, it is challenging to avoid a compromise with high-efficient operation and the wide-applicability of soft-switching operation in the TAB modulation technique. In this work, the influence of the transformer's magnetizing inductance is introduced into the analysis of the TAB converter for simplifying the realization of zero voltage switching (ZVS), and in this way, the power conversion efficiency of TAB operating in high-frequency can be improved. Drawing on the working principles of a single dual active bridge (DAB) converter and the linear superposition theorem, a simplified four-phase modulation (FPM) scheme for the TAB converter is proposed in this article, which is computationally stress-free, featuring low switching and conduction loss characteristics. By combining the FPM scheme with the tuning of the magnetizing inductance value, the ZVS operation of all switches can be readily achieved without imposing extra computational burden. This is particularly advantageous for time-intensive scenarios such as those found in the application of EVs. In addition, the ZVS process of the TAB converter is thoroughly studied and unified to simplify the calculation of ZVS current and required dead time, enabling the identification of the optimal magnetizing inductance value. The proposed optimization solution is introduced, studied, validated, and benchmarked in a 2.5 kW/100 kHz SiC-based TAB prototype, whose operating parameters are tailored to EVs application and peak efficiency reaches 96.3% at a partial load.</p

Model Based Control of Soft Robots: A Survey of the State of the Art and Open Challenges

Continuum soft robots are mechanical systems entirely made of continuously deformable elements. This design solution aims to bring robots closer to invertebrate animals and soft appendices of vertebrate animals (e.g., an elephant's trunk, a monkey's tail). This work aims to introduce the control theorist perspective to this novel development in robotics. We aim to remove the barriers to entry into this field by presenting existing results and future challenges using a unified language and within a coherent framework. Indeed, the main difficulty in entering this field is the wide variability of terminology and scientific backgrounds, making it quite hard to acquire a comprehensive view on the topic. Another limiting factor is that it is not obvious where to draw a clear line between the limitations imposed by the technology not being mature yet and the challenges intrinsic to this class of robots. In this work, we argue that the intrinsic effects are the continuum or multi-body dynamics, the presence of a non-negligible elastic potential field, and the variability in sensing and actuation strategies.Comment: 69 pages, 13 figure

Flow Simulation and Characterization of Fracture Systems Using Fast Marching Method and Novel Diagnostic Plots

Recently, the industrial trend of hydraulic fracturing is reducing the cluster spacing while increasing the fluid and proppant usage, which often generates complex fracture networks. The challenge from this trend is to understand and characterize the complex fracture networks. Recently, a novel approach has been proposed based on the high-frequency asymptotic solution of the diffusivity equation leading to the Eikonal equation. The Eikonal equation governs the pressure front propagation and can be solved by a front-tracking algorithm called Fast Marching Method (FMM). In this dissertation, we extend this method to complex fracture networks characterization and simulation, using novel diagnostic plots and FMM-based simulation. First, we develop novel diagnostic plots for complex fracture networks characterization. We directly use the field data to calculate the well drainage volume, instantaneous recovery ratio (IRR) and w(τ) function. The w(τ) function serves as a diagnostic plot to detect fracture geometry and flow regimes and the IRR plot is used to detect fracture conductivity. Second, we extend the FMM-based simulation workflow to local grid refinements (LGRs). The detailed workflow is proposed to generate the computational grid for the diffusive time of flight (DTOF) calculation. We use various models to validate the accuracy and computational efficiency of this workflow. In addition, we investigate various discretization schemes for the transition between local and global domain. Third, we extend the FMM-based simulation workflow to embedded discrete fracture model (EDFM). We utilize a novel gridding to link the embedded discrete fractures and the matrix based on Delaunay triangulation. Using the DTOF as a spatial coordinate, the FMM-based flow simulation reduces the 3D complex fracture networks simulation to an equivalent 1D simulation. Multiple examples are shown to validate the accuracy and computational efficiency of this workflow. Lastly, we investigate the impact of tighter cluster spacing of the hydraulic fractures using the Eagle Ford field data. The hydraulic fracture propagation simulator Mangrove® is used to generate the fracture patterns based on the completion data. A manual history matching is conducted to match the field injection treatment pressure. The impact of cluster spacing is examined through the calibrated fracture models

The Buffering Effect of Brands for Companies Facing Legislative Homogenization: Evidence from the Introduction of Sarbanes-Oxley

Brands not only enhance the positive impact of marketing initiatives, but also buffer the firm from the full consequences of unexpected and negative market shifts. While this protective effect has been demonstrated for firm-specific events (e.g., product recalls, public relations crises), its impact has not been observed in response to market-wide environmental shifts. Our study demonstrates the buffering properties of strong brands in exactly such a context: the passing of new legislation. By examining responses to the introduction of the Sarbanes-Oxley Act of 2002, we show that (1) firms exhibit a rapid and homogeneous response as they comply and adjust strategy to a new environmental incentive/cost structure; (2) from a marketing perspective, this homogeneity in competitive responses leads to a systemic decrease in marketing efficiency; and (3) stronger brands existing prior to this environmental shift help buffer their companies from this loss in efficiency. We further show that this advantage only holds for the strongest of brands in the market