1,720,988 research outputs found
Adaptive dead-time compensation
No full textIn this work a new paradigm for designing controllers for poorly modeled plants with significant dead time is proposed. We show how minimum variance control can be interpreted as dead-time compensation with infinite gain proportional feedback. Based on this interpretation, we develop more general control laws that do not suffer from the drawbacks of high gain feedback. These new controls are compared with the more traditional dead-time compensator, the Smith predictor, and the following directions of research are pursued: (1) The use of adaptation to achieve set-point matching without the use of integration. (2) The stability and convergence of these adaptive dead-time compensators are investigated under a variety of situations including an ideal stochastic framework and a time-varying deterministic framework. (3) Two methods for selecting controller parameters in an optimal manner are presented, and alternative strategies for adaptively generating d-step predictions are discussed. (4) The control laws are applied to a gas metal arc welder, and the resulting closed-loop performance is found to be superior to that for related controllers.Made available in DSpace on 2011-05-07T13:37:20Z (GMT). No. of bitstreams: 2
license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5)
9702468.pdf: 4597746 bytes, checksum: 03bf2b20e93c58427d94c1726cdf0d41 (MD5)
Previous issue date: 1996Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding ([email protected]) on 2011-05-07T14:57:05Z
Item is restricted indefinitely.Restriction data tranferred 2014-07-01T11:26:44-05:00
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Reason: ETDs are only available to UIUC Users without author permissionETDs are only available to UIUC Users without author permissionU of I Onl
Stability of queueing networks
No full textIn this thesis, the stability of queueing networks is studied. The use of test functions is a unifying thread. Tools are provided to construct appropriate test functions for complex networks, and the structure of such test functions is examined for specific network models. The analysis of queueing networks is performed in a manner that progresses in increasing complexity for increasingly complex networks.Single class networks are considered first. The particular form studied is open generalized Jackson networks with general arrival streams and general service time distributions. Assuming that the arrival rate does not exceed the network capacity and that the service times possess conditionally bounded second moments, stability is deduced by bounding the expected waiting time for a customer entering the network. For Markovian networks convergence of the total work in the system is obtained, as well as convergence of the mean queue size and mean customer delay, to a unique finite steady state value.Acyclic multiclass networks are the next topic. Once again, assuming that the arrival rate does not exceed the network capacity, stability of the network is deduced using the tools of ergodic theory. The distributions of the process are shown to converge to a unique steady state value, and under appropriate moment conditions, the convergence takes place at an exponential rate.The final topic is general re-entrant lines. In this case, piecewise linear test functions are developed for the analysis of both queueing networks and their associated fluid models. It is found that if an associated LP admits a positive solution, then a Lyapunov function exists. This implies that the fluid model is stable and, hence, that the network model is positive Harris recurrent with a finite polynomial moment. Also, it is found that if a different appropriate LP admits a solution, then the network model is transient.Made available in DSpace on 2011-05-07T12:53:09Z (GMT). No. of bitstreams: 2
license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5)
9522105.pdf: 4186508 bytes, checksum: 8a600f1790f6b337324a8a313ba77dc9 (MD5)
Previous issue date: 1995Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding ([email protected]) on 2011-05-07T14:47:12Z
Item is restricted indefinitely.Restriction data tranferred 2014-07-01T11:21:19-05:00
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Reason: ETDs are only available to UIUC Users without author permissionETDs are only available to UIUC Users without author permissionU of I Onl
Stability and performance of time-varying adaptive systems: Analysis and applications
No full textIn this thesis we consider the problems of identification, prediction and adaptive control of systems with unknown time-varying parameters, with an emphasis on obtaining performance bounds. While these issues have been extensively studied in the case of time invariant systems, time-varying systems have not received much attention. This thesis is divided into two parts, the first of which deals with the analytical derivation of performance bounds, and the second, which proposes solutions to real-life vibration control problems.Using a stochastic model for plant variation, it is shown that it is possible to obtain tight bounds on parameter identification performance. These results require minimal assumptions on the statistics of the regression vector and directly lead to a performance bound for adaptive predictors. In the case of adaptive control, it is shown that a family of adaptive controllers is mean square stable. This stability result allows the application of the identification and prediction bounds to obtain bounds on the variance of the closed-loop system output. For each of the cases described, simulation results are presented, which verify that the bounds are indeed tight.The second part of this thesis deals with the problem of vibration cancellation which is an important problem in numerous places such as in the aerospace industry and in the design of disk drives. In this part of the thesis, a new adaptive control solution is proposed for this problem. The algorithm is analyzed and compared with other existing solutions. Computer simulations using real-life helicopter models show the effectiveness of the adaptive control schemes. Implementations of this algorithm on some laboratory test beds also bear out its effectiveness.Made available in DSpace on 2011-05-07T12:35:33Z (GMT). No. of bitstreams: 2
license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5)
9712414.pdf: 3768040 bytes, checksum: c05bc94f8f53c2c25d0ac2a7e6e2e0a2 (MD5)
Previous issue date: 1996Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding ([email protected]) on 2011-05-07T14:43:02Z
Item is restricted indefinitely.Restriction data tranferred 2014-07-01T11:18:46-05:00
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Reason: ETDs are only available to UIUC Users without author permissionETDs are only available to UIUC Users without author permissionU of I Onl
Efficiency and marginal cost pricing in dynamic competitive markets with friction
Get PDFThis paper examines a dynamic general equilibrium model with supply friction. With or without friction, the competitive equilibrium is efficient. Without friction, the market price is completely determined by the marginal production cost. If friction is present, no matter how small, then the market price fluctuates between zero and the "choke-up" price, without any tendency to converge to the marginal production cost, exhibiting considerable volatility. The distribution of the gains from trading in an efficient allocation may be skewed in favor of the supplier, although every player in the market is a price taker.Dynamic general equilibrium model with supply friction, choke-up price, marginal production cost, welfare theorems
Mismatched divergence and universal hypothesis testing
No full textItem reinstated by Sarah Shreeves ([email protected]) on 2012-01-07T11:00:14Z
Item was in collections:
Dissertations and Theses - Electrical and Computer Engineering (ID: 446)
University of Illinois Dissertations and Theses (ID: 204)
No. of bitstreams: 3
Huang_Dayu.pdf.txt: 77576 bytes, checksum: 24d221d5653b56f115a407da421bec70 (MD5)
Huang_Dayu.pdf: 327190 bytes, checksum: 1e89146aa9ef1157d97e1b29645c8502 (MD5)
license.txt: 4059 bytes, checksum: f3553b40cd35e763a5b88a902ed8d0a3 (MD5)Item released from any restrictions by Sarah Shreeves ([email protected]) on 2012-01-07T11:00:14ZAn important challenge in detection theory is that the size of the state space may be very large. In the context of universal hypothesis testing, two important problems pertaining to the large state space that have not been addressed before are: (1) What is the impact of a large state space on the performance of tests? (2) How does one design an effective test when the state space is large?
This thesis addresses these two problems by developing a generalization of Kullback-Leibler (KL) mismatched divergence, called mismatched divergence.
1. We describe a drawback of the Hoeffding test: The asymptotic bias and variance of the Hoeffding test are approximately proportional to the size of the state space; thus, it performs poorly when the number of test samples is comparable to the size of state space.
2. We develop a generalization of the Hoeffding test based on the mismatched divergence, called the mismatched universal test. We show that this test has asymptotic bias
and variance proportional to the dimension of the function class used to define the mismatched divergence. The dimension of the function class can be chosen to be much smaller than the size of the state space, and thus our proposed test has a better finite-sample performance in terms of bias and variance.
3. We demonstrate that the mismatched universal test also has an advantage when the distribution of the null hypothesis is learned from data.
4. We develop some algebraic properties and geometric interpretations of the mismatched divergence. We also show its connection to a robust test.
5. We develop a generalization of Pinsker’s inequality, which gives a lower bound of the mismatched divergence.Item withdrawn by Mark Zulauf ([email protected]) on 2009-12-03T18:58:48Z
Item was in collections:
University of Illinois Theses & Dissertations (ID: 1)
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Huang_Dayu.pdf: 327190 bytes, checksum: 1e89146aa9ef1157d97e1b29645c8502 (MD5)Made available in DSpace on 2010-01-06T17:50:05Z (GMT). No. of bitstreams: 2
Huang_Dayu.pdf: 327190 bytes, checksum: 1e89146aa9ef1157d97e1b29645c8502 (MD5)
license.txt: 4059 bytes, checksum: f3553b40cd35e763a5b88a902ed8d0a3 (MD5)Item marked as restricted to the 'Administrator' Group (id=1) by William Ingram ([email protected]) on 2010-01-06T17:50:31Z
Item is restricted until 2012-01-06T17:50:31
Robust Statistical Modeling Based on Moment Classes, With Applications to Admission Control, Large Deviations and Hypothesis Testing
No full textThe goal in the admission control problem considered here is to choose a suitable algorithm for admitting or rejecting sources on the basis of on-line measurements of packet statistics, in order to keep a certain overflow probability below a pre-specified threshold. The theory of extremal distributions developed in this thesis is applied to the design of robust algorithms for measurement-based admission control. In addition, models are developed for the evolution of flows and packets in the admission control system, and performance evaluation of the proposed algorithms is carried out through both simulations and analysis. Results show that the robust algorithms minimize the overflow probability among all moment-consistent algorithms.Made available in DSpace on 2015-09-25T20:08:38Z (GMT). No. of bitstreams: 2
license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5)
3153394.pdf: 5422653 bytes, checksum: 3f2f22f1d3fc9eea038ef39a7b65b627 (MD5)
Previous issue date: 2004Embargo set by: Seth Robbins for item 82165
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Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDsRestricted to the U of I community idenfinitely during batch ingest of legacy ETDsU of I Only104 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004
Characterization and Computation of Optimal Distributions for Channel Coding
No full textBased on these results, new signal constellation designs are proposed and shown to outperform the currently widely used PSK and QAM schemes at all transmission rate below the channel capacity.Made available in DSpace on 2015-09-25T20:08:36Z (GMT). No. of bitstreams: 2
license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5)
3153320.pdf: 4510894 bytes, checksum: 15c8ebf424b7d76a9d3c4e0241c765ec (MD5)
Previous issue date: 2004Embargo set by: Seth Robbins for item 82159
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Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDsRestricted to the U of I community idenfinitely during batch ingest of legacy ETDsU of I Only108 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004
Robust Statistical Modeling Based on Moment Classes, With Applications to Admission Control, Large Deviations and Hypothesis Testing
No full text104 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004.The goal in the admission control problem considered here is to choose a suitable algorithm for admitting or rejecting sources on the basis of on-line measurements of packet statistics, in order to keep a certain overflow probability below a pre-specified threshold. The theory of extremal distributions developed in this thesis is applied to the design of robust algorithms for measurement-based admission control. In addition, models are developed for the evolution of flows and packets in the admission control system, and performance evaluation of the proposed algorithms is carried out through both simulations and analysis. Results show that the robust algorithms minimize the overflow probability among all moment-consistent algorithms.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD
Modeling and Control of Complex Stochastic Networks, With Applications to Manufacturing Systems and Electric Power Transmission Networks
No full textFor power transmission networks, we characterize the optimal amount of generation capacity to hold in reserve. The optimal solution indicates precisely how reserves must be adjusted according to environmental factors including the variability of power demand, and the ramping-rate constraints on generation.Made available in DSpace on 2015-09-25T20:08:49Z (GMT). No. of bitstreams: 2
license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5)
3198942.pdf: 3237157 bytes, checksum: 6392eeef9359f173e22f627623556dfd (MD5)
Previous issue date: 2005Embargo set by: Seth Robbins for item 82200
Lift date: Forever
Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDsRestricted to the U of I community idenfinitely during batch ingest of legacy ETDsU of I Only128 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2005
Value function approximation architectures for neuro-dynamic programming
Get PDFNeuro-dynamic programming is a class of powerful techniques for approximating the solution to dynamic programming equations. In their most computationally attractive formulations, these techniques provide the approximate solution only within a prescribed finite-dimensional function class. Thus, the question that always arises is how should the function class be chosen?
In this dissertation, we first propose an approach using the solutions to associated fluid and diffusion approximations. In order to evaluate this approach, we establish bounds on the approximation errors.
Next, we propose a novel parameterized Q-learning algorithm. Q-learning is a model-free method to compute the Q-function
associated with an optimal policy, based on
observations of states and actions. If the size of a state or a policy space is too large, Q-learning is often not very practical because there are too many Q-function values to update. One way to address this problem is to approximate the Q-function within a function class. However, such methods often require an explicit model of the system, such as the split sampling method introduced by Borkar. The proposed algorithm is a reinforcement learning (RL) method, in which case the system dynamics are not known. This method is designed based on using approximations of the transition kernel of the Markov decision process (MDP).
Lastly, we apply the proposed results of value function approximation techniques to several applications. In the power management model, we focus on the processor speed control problem to balance the performance and energy usage. Then we extend the results to the load balancing and the power management problem of geographically distributed data centers with grid regulation. In the cross-layer wireless control problem, the network utility maximization (NUM) and adaptive modulation (AM) are combined to balance the network performance and transmission power. In these applications, we show how to model the real problems by using the MDP model with reasonable assumptions and necessary approximations. Approximations of the value function are obtained for specific models, and evaluated by getting bounds for the errors. These approximate solutions are then used to construct basis functions for learning algorithms in the simulations.Item withdrawn by Laura Spradlin ([email protected]) on 2013-12-02T14:33:51Z
Item was in collections:
University of Illinois Theses & Dissertations (ID: 1)
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Chen_Wei.pdf: 7488417 bytes, checksum: 8821f75e096c84203444be0e87ffaed1 (MD5)Made available in DSpace on 2014-01-16T18:26:02Z (GMT). No. of bitstreams: 2
Wei_Chen.pdf: 7488417 bytes, checksum: 8821f75e096c84203444be0e87ffaed1 (MD5)
license.txt: 4058 bytes, checksum: a1abb7c4bbdb0836afe5bb4e3873ae50 (MD5)Item marked as restricted to the 'Administrator' Group (id=1) by Seth Robbins ([email protected]) on 2014-01-16T18:27:35Z
Item is restricted until 2016-01-16T18:27:27ZRestriction data tranferred 2014-07-01T11:36:47-05:00
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Release Date: 2016-01-16 12:27:27 UTC
Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemLimited Restriction Lifted for Item 46928 on 2016-01-16T11:02:04Z
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