by Ka-lun Tung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 168-[175]). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Control with Uncertainties --- p.1 / Chapter 1.1.1 --- Adaptive Control --- p.2 / Chapter 1.1.2 --- H∞ Robust Control --- p.3 / Chapter 1.2 --- A Unified Framework: Adaptive Robust Control --- p.4 / Chapter 1.3 --- System Identification for Robust Control --- p.6 / Chapter 1.3.1 --- Choice of input signal --- p.7 / Chapter 1.4 --- Objectives and Contributions --- p.8 / Chapter 1.5 --- Thesis Outline --- p.9 / Chapter 2 --- Background on Robust Control --- p.11 / Chapter 2.1 --- Notation and Terminology --- p.12 / Chapter 2.1.1 --- Notation --- p.12 / Chapter 2.1.2 --- Linear System Terminology --- p.13 / Chapter 2.1.3 --- Norms --- p.15 / Chapter 2.1.4 --- More Terminology: A Standard Feedback Configuration --- p.17 / Chapter 2.2 --- Norms and Power for Signals and Systems --- p.18 / Chapter 2.3 --- Plant Uncertainty Model --- p.20 / Chapter 2.3.1 --- Multiplicative Unstructured Uncertainty --- p.21 / Chapter 2.3.2 --- Additive Unstructured Uncertainty --- p.22 / Chapter 2.3.3 --- Structured Uncertainty --- p.23 / Chapter 2.4 --- Motivation for H∞ Control Design --- p.23 / Chapter 2.4.1 --- Robust stabilization: Multiplicative Uncertainty and Weight- ing function W3 --- p.24 / Chapter 2.4.2 --- Robust stabilization: Additive Uncertainty and Weighting function W2 --- p.25 / Chapter 2.4.3 --- Tracking Problem --- p.26 / Chapter 2.4.4 --- Disturbance Rejection (or Sensitivity Minimization) --- p.27 / Chapter 2.5 --- The Robust Control Problem Statement --- p.28 / Chapter 2.5.1 --- The Mixed-Sensitivity Approach --- p.29 / Chapter 2.6 --- An Augmented Generalized Plant --- p.30 / Chapter 2.6.1 --- The Augmented Plant --- p.30 / Chapter 2.6.2 --- Adaptation of Augmented Plant to Sensitivity Minimiza- tion Problem --- p.32 / Chapter 2.6.3 --- Adaptation of Augmented Plant to Mixed-Sensitivity Prob- lem --- p.33 / Chapter 2.7 --- Using MATLAB Robust Control Toolbox --- p.34 / Chapter 3 --- Statistical Plant Set Estimation for Robust Control --- p.36 / Chapter 3.1 --- An Overview --- p.37 / Chapter 3.2 --- The Schroeder-phased Input Design --- p.39 / Chapter 3.3 --- The Statistical Additive Uncertainty Bounds --- p.40 / Chapter 3.4 --- Additive Uncertainty Characterization --- p.45 / Chapter 3.4.1 --- "Linear Programming Spectral Overbounding and Factor- ization Algorithm (LPSOF) [20,21]" --- p.45 / Chapter 4 --- Basic System Identification and Model Reduction Algorithms --- p.48 / Chapter 4.1 --- The Eigensystem Realization Algorithm --- p.49 / Chapter 4.1.1 --- Basic Algorithm --- p.49 / Chapter 4.1.2 --- Estimating Markov Parameters from Input/Output data: Observer/Kalman Filter Identification (OKID) --- p.51 / Chapter 4.2 --- The Frequency-Domain Identification via 2-norm Minimization --- p.54 / Chapter 4.3 --- Balanced Realization and Truncation --- p.55 / Chapter 4.4 --- Frequency Weighted Balanced Truncation --- p.56 / Chapter 5 --- Plant Model Reduction and Robust Control Design --- p.59 / Chapter 5.1 --- Problem Formulation --- p.59 / Chapter 5.2 --- Iterative Reweighting Scheme --- p.60 / Chapter 5.2.1 --- Rationale Behind the Scheme --- p.62 / Chapter 5.3 --- Integrated Model Reduction/ Robust Control Design with Iter- ated Reweighting --- p.63 / Chapter 5.4 --- A Design Example --- p.64 / Chapter 5.4.1 --- The Plant and Specification --- p.64 / Chapter 5.4.2 --- First Iteration --- p.65 / Chapter 5.4.3 --- Second Iteration --- p.67 / Chapter 5.5 --- Approximate Fractional Frequency Weighting --- p.69 / Chapter 5.5.1 --- Summary of Past Results --- p.69 / Chapter 5.5.2 --- Approximate Fractional Frequency Weighting Approach [40] --- p.70 / Chapter 5.5.3 --- Simulation Results --- p.71 / Chapter 5.6 --- Integrated System Identification/Control Design with Iterative Reweighting Scheme --- p.74 / Chapter 6 --- Controller Reduction and Robust Control Design --- p.82 / Chapter 6.1 --- Motivation for Controller Reduction --- p.83 / Chapter 6.2 --- Choice of Frequency Weightings for Controller Reduction --- p.84 / Chapter 6.2.1 --- Stability Margin Considerations --- p.84 / Chapter 6.2.2 --- Closed-Loop Transfer Function Considerations --- p.85 / Chapter 6.2.3 --- A New Way to Determine Frequency Weighting --- p.86 / Chapter 6.3 --- A Scheme for Iterative Frequency Weighted Controller Reduction (IFWCR) --- p.87 / Chapter 7 --- A Comparative Design Example --- p.90 / Chapter 7.1 --- Plant Model Reduction Approach --- p.90 / Chapter 7.2 --- Weighted Controller Reduction Approach --- p.94 / Chapter 7.2.1 --- A Full Order Controller --- p.94 / Chapter 7.2.2 --- Weighted Controller Reduction with Stability Considera- tions --- p.94 / Chapter 7.2.3 --- Iterative Weighted Controller Reduction --- p.96 / Chapter 7.3 --- Summary of Results --- p.101 / Chapter 7.4 --- Discussions of Results --- p.101 / Chapter 8 --- A Comparative Example on a Benchmark problem --- p.105 / Chapter 8.1 --- The Benchmark plant [54] --- p.106 / Chapter 8.1.1 --- Benchmark Format and Design Information --- p.106 / Chapter 8.1.2 --- Control Design Specifications --- p.107 / Chapter 8.2 --- Selection of Performance Weighting function --- p.108 / Chapter 8.2.1 --- Reciprocal Principle --- p.109 / Chapter 8.2.2 --- Selection of W1 --- p.110 / Chapter 8.2.3 --- Selection of W2 --- p.110 / Chapter 8.3 --- System Identification by ERA --- p.112 / Chapter 8.4 --- System Identification by Curve Fitting --- p.114 / Chapter 8.4.1 --- Spectral Estimate --- p.114 / Chapter 8.4.2 --- Curve Fitting Results --- p.114 / Chapter 8.5 --- Robust Control Design --- p.115 / Chapter 8.5.1 --- The selection of W1 weighting function --- p.115 / Chapter 8.5.2 --- Summary of Design Results --- p.116 / Chapter 8.6 --- Stress Level 1 --- p.117 / Chapter 8.6.1 --- System Identification Results --- p.117 / Chapter 8.6.2 --- Design Results --- p.119 / Chapter 8.6.3 --- Step Response --- p.121 / Chapter 8.7 --- Stress Level 2 --- p.124 / Chapter 8.7.1 --- System Identification Results --- p.124 / Chapter 8.7.2 --- Step Response --- p.125 / Chapter 8.8 --- Stress Level 3 --- p.128 / Chapter 8.8.1 --- System Identification Results --- p.128 / Chapter 8.8.2 --- Step Response --- p.129 / Chapter 8.9 --- Comparisons with Other Designs --- p.132 / Chapter 9 --- Conclusions and Recommendations for Further Research --- p.133 / Chapter 9.1 --- Conclusions --- p.133 / Chapter 9.2 --- Recommendations for Further Research --- p.135 / Chapter A --- Design Results of Stress Levels 2 and3 --- p.137 / Chapter A.1 --- Stress Level 2 --- p.137 / Chapter A.2 --- Stress Level 3 --- p.140 / Chapter B --- Step Responses with Reduced Order Controller --- p.142 / Chapter C --- Summary of Results of Other Groups on the Benchmark Prob- lem --- p.145 / Chapter C.1 --- Indirect and implicit adaptive predictive control [45] --- p.146 / Chapter C.2 --- H∞ Robust Control [51] --- p.150 / Chapter C.3 --- Robust Stability Degree Assignment [53] --- p.152 / Chapter C.4 --- Model Reference Adaptive Control [46] --- p.154 / Chapter C.5 --- Robust Pole Placement using ACSYDE (Automatic Control Sys- tem Design) [47] --- p.156 / Chapter C.6 --- Adaptive PI Control [48] --- p.157 / Chapter C.7 --- Adaptive Control with supervision [49] --- p.160 / Chapter C.8 --- Partial State Model Reference (PSRM) Control [50] --- p.162 / Chapter C.9 --- Contstrainted Receding Horizon Predictive Control (CRHPC) [52] --- p.165 / Bibliography --- p.168

<p>The aim of this thesis is to develop the knowledge about nonlinear and/or adaptive solid fuel boiler control at Vattenfall Utveckling AB. The aim is also to make a study of implemented and published control strategies.</p><p>A solid fuel boiler is a large-scale heat (and power) generating plant. The Idbäcken boiler studied in this work, is a one hundred MW furnace mainly fired with wood chips. The control system consists of several linear PID controllers working together, and the furnace is a nonlinear system. That, and the fact that the fuel-flow is not monitored, are the main reasons for the control problems. The system fluctuates periodically and the CO outlets sometimes rise high above the permitted level.</p><p>There is little work done in the area of advanced boiler control, but some interesting approaches are described in scientific articles. MPC (Model Predictive Control), nonlinear system identification using ANN (Artificial Neural Network), fuzzy logic, Hµ loop shaping and MIMO (Multiple Input Multiple Output) PID tuning methods have been tested with good results.</p><p>Both linear and nonlinear system identification is performed in the thesis. The linear models are able to explain about forty percent of the system behavior and the nonlinear models explain about sixty to eighty percent. The main result is that nonlinear models improve the performance and that there are considerable disturbances complicating the identification. Another identification issue was the feedback during the data collection.</p>

Two different types of switching mechanism are considered in this thesis; one is featured with abrupt/sudden switching while the other one shows gradual changing behavior in its dynamics. It is shown that, through the comparison of the identification results from the proposed method and a benchmark method, the proposed robust identification method can achieve better performance when dealing with the data set mixed with outliers. To model the switched systems exhibiting gradual or smooth transition among different local models, in addition to estimating the local sub-systems parameters, a smooth validity (an exponential function) function is introduced to combine all the local models so that throughout the working range of the gradual switched system, the dynamics of the nonlinear process can be appropriately approximated. Verification results on a simulated numerical example and CSTR process confirm the effectiveness of the proposed Linear Parameter Varying (LPV) identification algorithm. / Process Control

The aim of this thesis is to develop the knowledge about nonlinear and/or adaptive solid fuel boiler control at Vattenfall Utveckling AB. The aim is also to make a study of implemented and published control strategies. A solid fuel boiler is a large-scale heat (and power) generating plant. The Idbäcken boiler studied in this work, is a one hundred MW furnace mainly fired with wood chips. The control system consists of several linear PID controllers working together, and the furnace is a nonlinear system. That, and the fact that the fuel-flow is not monitored, are the main reasons for the control problems. The system fluctuates periodically and the CO outlets sometimes rise high above the permitted level. There is little work done in the area of advanced boiler control, but some interesting approaches are described in scientific articles. MPC (Model Predictive Control), nonlinear system identification using ANN (Artificial Neural Network), fuzzy logic, Hµ loop shaping and MIMO (Multiple Input Multiple Output) PID tuning methods have been tested with good results. Both linear and nonlinear system identification is performed in the thesis. The linear models are able to explain about forty percent of the system behavior and the nonlinear models explain about sixty to eighty percent. The main result is that nonlinear models improve the performance and that there are considerable disturbances complicating the identification. Another identification issue was the feedback during the data collection.

A new system identification algorithm, multiple autoregressive exogenous (ARX) inputs-based Takagi-Sugeno (TS) fuzzy model, is developed to identify nonlinear behavior of structure-magnetorheological (MR) damper systems. It integrates a set of ARX models, clustering algorithms, and weighted least squares algorithm with a TS fuzzy model. Based on a set of input-output data that is generated from building structures equipped with MR dampers, premise parameters of the ARX-TS fuzzy model are determined by clustering algorithms. Once the premise part is constructed, consequent parameters of the ARX-TS fuzzy model are optimized by the weighted least squares algorithm. To demonstrate the effectiveness of the proposed ARX-TS fuzzy model, it is applied to a three-, an eight-, a twenty-story building structures. It is demonstrated from the numerical simulation that the proposed ARX-TS fuzzy algorithm is effective to identify nonlinear behavior of seismically excited building structures equipped with MR dampers. A new semiactive nonlinear fuzzy control (SNFC) algorithm is developed through integration of multiple Lyapunov-based state feedback gains, a Kalman filter, and a converting algorithm with TS fuzzy interpolation method. First, the nonlinear ARX-TS fuzzy model is decomposed into a set of linear dynamic models that are operated in only a local linear operating region. Based on the decomposed models, multiple Lyapunov-based state feedback controllers are formulated in terms of linear matrix inequalities (LMIs) such that the structure-MR damper system is globally asymptotically stable and the performance on transient responses is guaranteed. Then, the state feedback controllers are integrated with a Kalman filter and a converting algorithm using a TS fuzzy interpolation method to construct semiactive output feedback controllers. To demonstrate the effectiveness of the proposed SNFC algorithm, it is applied to a three-, an eight-, and a twenty-story building structures. It is demonstrated from the numerical simulation that the proposed SNFC algorithm is effective to control responses of seismically excited building structures equipped with MR dampers. In addition, it is shown that the proposed SNFC system is better than a traditional optimal algorithm, H2/linear quadratic Gaussian-based semiactive control strategy.

During unstart, the rapid upstream propagation of a hypersonic engine's inlet shock system can be clearly seen through inlet pressure measurements. Specifically, the magnitude of the pressure readings suddenly and dramatically increases as soon as the leading edge of the shock system passes the measurement location. A change detection algorithm can monitor the pressure time history at a given sensing location and determine when an abrupt pressure rise occurs. If this kind of information can be obtained at various sensing locations distributed throughout the inlet then a feedback control scheme has an improved basis upon which to make actuation decisions for preventing unstart. In this thesis a variety of change detection algorithms have been implemented and tested on multiple sources of experimental high-speed pressure transducer data. The performance of these algorithms is compared and suitability of each algorithm for the general unstart problem is discussed. Attempts to model the transient dynamics governing the unstart process have also been made through the use of system identification techniques. The result of these system identification efforts is a partially nonlinear mathematical model that describes shock motion through pressure signals. The process reveals that the nonlinear behavior can be separated from the linear with relative ease. Related attempts are then made to create a model where the nonlinear portion has been specified leaving only the linear portion to be determined by system identification. The modeling and identification process specific to the unstart data used is discussed and successful models are presented for both cases. / text

A gimbal is a pivoted device that decouples movements of a platform from its payload. The payload is a camera which must be stabilized to capture video without motion disturbances. A challenge with this type of gimbal is that a wide span of cameras with different sizes and weights can be used. The change of camera has significant effect on the dynamics of the gimbal and therefore the control system must be retuned. This tuning is inconvenient, especially for someone without knowledge of control engineering. This thesis reviews suitable methods to perform an automatic controller tuning directly on the gimbal's hardware.This tuning starts by exciting the system and then using data to estimate a model. This model is then used to control the gimbal, thus removing the need for manual tuning of the system. The foundation of this thesis is a physical model of the gimbal, derived through the Lagrange equation. The physical model has undetermined parameters such as inertias, centre of gravity and friction constants. System identification is used to determine these parameters. A problem discussed is how the system should be excited in order to achieve data with as much information as possible about the dynamics. This problem is approached by formulating an optimization problem that can be used find suitable trajectories. The identified model is then used to control the gimbal. Different methods for model based-control are discussed. By using a method called feedback linearisation all of the parameter-dependant dynamics of the gimbal can be compensated for. Apart from being independent of model parameters the new outer system is also decoupled and linear. A PID controller is used for feedback control of the outer system. The uncertainty of the feedback linearisation is analysed to find the effects of model errors.To assure robustness of the closed loop system a Lyapunov redesign controller is used to compensate for these model errors. Some experimental results are also presented. The quality of the estimated model is evaluated. Additionally, the reference tracking performance of the control system is tested and results reveal issues with the estimated model's performance.

This thesis proposes nonlinear system identification techniques for the mathematical modeling of the human smooth pursuit system (SPS) with application to motor symptom quantification in Parkinson's disease (PD). The SPS refers to the complex neuromuscular system in humans that governs the smooth pursuit eye movements (SPEM). Insight into the SPS and its operation is of importance in a wide and steadily expanding array of application areas and research fields. The ultimate purpose of the work in this thesis is to attain a deeper understanding and quantification of the SPS dynamics and thus facilitate the continued development of novel commercial products and medical devices. The main contribution of this thesis is in the derivation and evaluation of several techniques for SPS characterization. While attempts to mathematically model the SPS have been made in the literature before, several key aspects of the problem have been previously overlooked.This work is the first one to devise dynamical models intended for extended-time experiments and also to consider systematic visual stimuli design in the context of SPS modeling. The result is a handful of parametric mathematical models outperforming current State-of-the-Art models in terms of prediction accuracy for rich input signals. As a complement to the parametric dynamical models, a non-parametric technique involving the construction of individual statistical models pertaining to specific gaze trajectories is suggested. Both the parametric and non-parametric models are demonstrated to successfully distinguish between individuals or groups of individuals based on eye movements.Furthermore, a novel approach to Wiener system identification using Volterra series is proposed and analyzed. It is exploited to confirm that the SPS in healthy individuals is indeed nonlinear, but that the nonlinearity of the system is significantly stronger in PD subjects. The nonlinearity in healthy individuals appears to be well-modeled by a static output function, whereas the nonlinear behavior introduced to the SPS by PD is dynamical.

With the advent of high throughput measurement technologies, large scale gene expression data are available for analysis. Various computational methods have been introduced to analyze and predict meaningful molecular interactions from gene expression data. Such patterns can provide an understanding of the regulatory mechanisms in the cells. In the past, system identification algorithms have been extensively developed for engineering systems. These methods capture the dynamic input/output relationship of a system, provide a deterministic model of its function, and have reasonable computational requirements. In this work, two system identification methods are applied for reverse engineering of gene regulatory networks. The first method is based on an orthogonal search; it selects terms from a predefined set of gene expression profiles to best fit the expression levels of a given output gene. The second method consists of a few cascades, each of which includes a dynamic component and a static component. Multiple cascades are added in a parallel to reduce the difference of the estimated expression profiles with the actual ones. Gene regulatory networks can be constructed by defining the selected inputs as the regulators of the output. To assess the performance of the approaches, a temporal synthetic dataset is developed. Methods are then applied to this dataset as well as the Brainsim dataset, a popular simulated temporal gene expression data. Furthermore, the methods are also applied to a biological dataset in yeast Saccharomyces Cerevisiae. This dataset includes 14 cell-cycle regulated genes; their known cell cycle pathway is used as the target network structure, and the criteria sensitivity, precision, and specificity are calculated to evaluate the inferred networks through these two methods. Resulting networks are also compared with two previous studies in the literature on the same dataset. / Thesis (Master, Computing) -- Queen's University, 2010-10-18 20:47:36.458

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