Ho Wang Hei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 93-94). / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Contributions --- p.1 / Chapter 1.1.1 --- Scalability of Network Capacity with Power Control --- p.1 / Chapter 1.1.2 --- Trade-off between network capacity and fairness with Power Control --- p.3 / Chapter 1.2 --- Related Work --- p.4 / Chapter 1.3 --- Organization of the Thesis --- p.6 / Chapter Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Hidden- and Exposed-node Problems --- p.8 / Chapter 2.1.1 --- HN-free Design (HFD) --- p.9 / Chapter 2.1.2 --- Non-Scalable Capacity in 802.11 caused by EN --- p.11 / Chapter 2.2 --- Shortcomings of Minimum-Transmit-Power Approach --- p.13 / Chapter Chapter 3 --- Simultaneous Transmissions Constraints with Power Control --- p.15 / Chapter 3.1 --- Physical-Collision Constraints --- p.16 / Chapter 3.1.1 --- Protocol-Independent Physical-Collision Constraints --- p.17 / Chapter 3.1.2 --- Protocol-Specific Physical-Collision Constraints --- p.17 / Chapter 3.2 --- Protocol-Collision-Prevention Constraints --- p.18 / Chapter 3.2.1 --- Transmitter-Side Carrier-Sensing Constraints --- p.18 / Chapter 3.2.2 --- Receiver-Side Carrier-Sensing Constraints --- p.19 / Chapter Chapter 4 --- Graph Models for Capturing Transmission Constraints and Hidden-node Problems --- p.20 / Chapter 4.1 --- Link-Interference Graph from Physical-Collision Constraints --- p.21 / Chapter 4.2 --- Protocol-Collision-Prevention Graphs --- p.22 / Chapter 4.3 --- Ideal Protocol-Collision-Prevention Graphs --- p.22 / Chapter 4.4 --- Definition of HN and EN and their Investigation using Graph Model --- p.23 / Chapter 4.5 --- Attacking Cases --- p.26 / Chapter Chapter 5 --- Scalability of Network Capacity with Adaptive Power Control --- p.27 / Chapter 5.1 --- Selective Disregard of NAVs (SDN) --- p.27 / Chapter 5.2 --- Scalability of Network Capacity: Analytical Discussion --- p.29 / Chapter 5.3 --- Adaptive Power Control for SDN --- p.31 / Chapter 5.3.1 --- Per-iteration Power Adjustment --- p.32 / Chapter 5.3.2 --- Power Control Scheduling Strategy --- p.35 / Chapter 5.3.3 --- Power Exchange Algorithm --- p.39 / Chapter 5.3.4 --- Comparison of Scheduling Strategies --- p.41 / Chapter 5.4 --- Scalability of Network Capacity: Numerical Results --- p.43 / Chapter Chapter 6 --- Decoupled Adaptive Power Control (DAPC) --- p.45 / Chapter 6.1 --- Per-iteration Power Adjustment --- p.45 / Chapter 6.2 --- Power Exchange Algorithm --- p.47 / Chapter 6.3 --- Implementation of DAPC --- p.48 / Chapter 6.4 --- Deadlock Problem in DAPC --- p.50 / Chapter Chapter 7 --- Progressive-Uniformly-Scaled Power Control (PUSPC): Deadlock-free Design --- p.53 / Chapter 7.1 --- Algorithm of PUSPC --- p.53 / Chapter 7.2 --- Deadlock-free property of PUSPC --- p.60 / Chapter 7.3 --- Deadlock Resolution of DAPC using PUSPC --- p.62 / Chapter Chapter 8 --- Incremental Power Adaptation --- p.65 / Chapter 8.1 --- Incremental Power Adaptation (IPA) --- p.65 / Chapter 8.2 --- Maximum Allowable Power in EPA --- p.68 / Chapter 8.3 --- Numerical Results of IPA --- p.71 / Chapter Chapter 9 --- Numerical Results and the Trade-off between EN and HN --- p.78 / Chapter Chapter 10 --- Conclusion --- p.83 / Appendix I: Proof of the Correct Operation of PE Algorithm for APC for SDN --- p.86 / Appendix II: Proof of the Correct Operation of PE Algorithm for DAPC --- p.89 / Appendix III: Scalability of the Communication Cost of PE Algorithm --- p.91 / Bibliography --- p.93

Vehicle occupants are typically exposed to unpleasant whole-body vibration (WBV) for extended period of time. It is well known that the transmission of unwanted vibration to the human body can lead to fatigue and discomfort. Moreover, the unwanted vibration normally distributed in the low-frequency range has been found as the main risk factor for lower back pain and lumbago, which seriously affect the health and working performance of occupants. Thus vibration cancellation on seats has attracted considerable interest in recent years. So far, for most vehicle seats, vibration isolation is achieved passively by using seat cushions and conventional energy absorbers, which have very limited performance in the low-frequency range. The work presented in this thesis forms a successful development and experimental study of an active seat and control algorithm for occupants’ WBV reduction under low frequency excitations. Firstly, a modelling study of the seat human subjects (SHS) and an extensive experimental measurement of the vibration transmissibility of a test dummy and vehicle seat are carried out. The biodynamic responses of SHS exposed to uncoupled vertical and fore-and-aft WBV is modelled. A comparison with the existing models is made and the results show that an improved fit with the aggregated experimental data is achieved. Secondly, an active seat is developed based upon the observations and understanding of the SHS and seat system. The characteristics of the active seat dynamics are identified through experimental tests found suitable for the development of an active seat to attenuate the vibration experienced by vehicle occupants. The vibration cancellation performance of the active seat is initially examined by feedforward plus proportional-integral (PI) control tests. Through these tests, the effectiveness of the actuators control authority is verified, but the limitations are also revealed. Because the active seat system is subject to non-linear and time-varying behaviour, a self-tuning fully adaptive algorithm is a prime requirement. The Filtered-x Least-Mean-Square (FXLMS) algorithm with the Fast-block LMS (FBLMS) system identification technique is found suitable for this application and is investigated through experimental tests. Substantial vibration reductions are achieved for a variety of input vibration profiles. An excellent capability of the active seat and control system for efficiently reducing the vibration level of seated occupants under low-frequency WBV is demonstrated.

Microgrids are local power systems of different sizes located inside the distribution systems. Each microgrid contains a group of interconnected loads and distributed energy resources that acts as a single controllable entity with respect to the grid. Their islanding operation capabilities during emergencies improve the resiliency and reliability of the electric energy supply. Due to its low kinetic energy storage capacity, maintaining microgrid stability is challenging under system contingencies and unpredictable power generation from renewable resources. This dissertation highlights the potential benefits of flexibly utilizing the battery energy storage systems to enhance the stability of microgrids. The main contribution of this research consists in the development of a storage converter controller with an additional stability margin that enables it to improve microgrid frequency and voltage regulation as well as its induction motor post-fault speed recovery. This new autonomous control technique is implemented by adaptively setting the converter controller parameters based on its estimated phase-locked loop frequency deviation and terminal voltage magnitude measurement. This work also assists in the microgrid design process by determining the normalized minimum storage converter sizing under a wide range of microgrid motor inertia, loading and fault clearing time with both symmetrical and asymmetrical fault types. This study evaluates the expandability of the proposed control methodologies under an unbalanced meshed microgrid with fault-induced feeder switching and multiple contingencies in addition to random power output from renewable generators. The favorable results demonstrate the robust storage converter controller performance under a dynamic changing microgrid environment.

"Recent works on the control of wheeled mobile robots have shifted from the use of the kinematic model to the use of the dynamic model. Since theoretical results typically treat the inputs to the dynamic model as torques, few experimental results have been provided, as torque is typically not the input to most commercially available robots. Few papers have implemented controllers based on the dynamic model, and those that have did not address the issue of identifying the parameters of the dynamic model. This work focuses on a method for identifying the parameters of the dynamic model of a wheeled mobile robot. The method is shown to be both effective and easy to implement, and requires no prior knowledge of what the parameters may be. Experimental results on two mobile robots of different scale demonstrate its effectiveness. The estimates of the parameters created by the proposed method are then used in an adaptive controller to verify their accuracy. For future work, this method should be completed autonomously in a two-part manner, onboard the mobile robot. First, the robot should perform the method proposed here to generate an initial parameter estimate, and then use adaptive control to update the estimates."

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Charles Edward Rohrs. / Ph.D.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Henry Rolan Shomber. / M.S.

Repetitive Control (RC) seeks to converge to zero tracking error of a feedback control system performing periodic command as time progresses, or to cancel the influence of a periodic disturbance as time progresses, by observing the error in the previous period. Iterative Learning Control (ILC) is similar, it aims to converge to zero tracking error of system repeatedly performing the same task, and also adjusting the command to the feedback controller each repetition based on the error in the previous repetition. Compared to the conventional feedback control design methods, RC and ILC improve the performance over repetitions, and both aiming at zero tracking error in the real world instead of in a mathematical model. Linear Model Predictive Control (LMPC) normally does not aim for zero tracking error following a desired trajectory, but aims to minimize a quadratic cost function to the prediction horizon, and then apply the first control action. Then repeat the process each time step. The usual quadratic cost is a trade-off function between tracking accuracy and control effort and hence is not asking for zero error. It is also not specialized to periodic command or periodic disturbance as RC is, but does require that one knows the future desired command up to the prediction horizon. The objective of this dissertation is to present various design schemes of improving the tracking performance in a control system based on ILC, RC and LMPC. The dissertation contains four major chapters. The first chapter studies the optimization of the design parameters, in particular as related to measurement noise, and the need of a cutoff filter when dealing with actuator limitations, robustness to model error. The results aim to guide the user in tuning the design parameters available when creating a repetitive control system. In the second chapter, we investigate how ILC laws can be converted for use in RC to improve performance. And robustification by adding control penalty in cost function is compared to use a frequency cutoff filter. The third chapter develops a method to create desired trajectories with a zero tracking interval without involving an unstable inverse solution. An easily implementable feedback version is created to optimize the same cost every time step from the current measured position. An ILC algorithm is also created to iteratively learn to give local zero error in the real world while using an imperfect model. This approach also gives a method to apply ILC to endpoint problem without specifying an arbitrary trajectory to follow to reach the endpoint. This creates a method for ILC to apply to such problems without asking for accurate tracking of a somewhat arbitrary trajectory to accomplish learning to reach the desired endpoint. The last chapter outlines a set of uses for a stable inverse in control applications, including Linear Model Predictive Control (LMPC), and LMPC applied to Repetitive Control (RC-LMPC), and a generalized form of a one-step ahead control. An important characteristic is that this approach has the property of converging to zero tracking error in a small number of time steps, which is finite time convergence instead of asymptotic convergence as time tends to infinity.

Finally, we study a typical heterogeneous network, Wireless Biomedical Sensor Network (WBSN), as it consists of various types of biosensors to monitor different physiological parameters. WBSN will help to enhance medical services with its unique advantages in long-term monitoring, easy network deployment, wireless connections, and ambulatory capabilities. (Abstract shortened by UMI.) / Secondly, for the purpose of providing geographical information for the topology management, we investigate the problem of power adaptive localization based on received signal strength (RSS), aiming at tackling the problem of inconsistent signal strength observation caused by tuning power levels. We propose a localization algorithm based on the particle filtering technique for sensor networks assisted by multiple transmission power levels. As a result, the novel contribution in this part is to intelligently incorporate changing transmission power levels into the particle filtering process as dynamic evidences and make an accurate localization. The proposed particle filtering technique based localization algorithm effectively circumvents the inconsistent observations under different power settings. It picks up the information of RSS from the beacons or the neighboring nodes to infer position information, without requiring additional instrumentation. We then evaluate the power adaptive localization algorithm via simulation studies and the results indicate that the proposed algorithm outperforms the algorithm of iterative least-square estimation, which does not utilize multiple power levels. In addition, we proposed a particle-filtering localization based on the acoustic asymmetric patterns of the acoustic sensors. As a result, the proposed particle filter based localization algorithms can facilitate the topology management in heterogeneous sensor networks. / We start by formulating the problem of topology optimization in the context of game theory and then analyze the equilibrium resulted from the decentralized interactions between the heterogeneous sensors. Majority of the existing topology control approaches require a centralized controller to obtain a global network graph and formulate the issue as a problem of transmission range assignment. The centralized algorithms are inapplicable for large-scale sensor networks due to the heavy communication overhead. In addition, these algorithms rarely consider the cross-layer consequences of the power adjustment, such as the quality of received signals at physical layer, the network connectivity, and the spatial reuse at network layer. Considering the aforementioned cross-layer interactive effects caused by power scheduling, we study the utility function that balances the physical layer link quality characterized by the frame success rate and the network layer robustness characterized by K-connectivity, while minimizing the power consumption. We prove the existence of the Nash equilibrium for complete-information game formulation. Because the heterogeneous sensors typically react to neighboring environment based on local information and the states of sensors are evolving over time, the power-scheduling problem in WHSN is further formulated into a more realistic incomplete-information dynamic game model. We then analyze the separating equilibrium, one of the perfect Bayesian equilibriums resulted from the dynamic game, with the sensors revealing their operational states from the actions. The sufficient and necessary conditions of the separating equilibrium existence are derived for the dynamic Bayesian game, which provide theoretical basis to the proposed power scheduling algorithms. / Wireless Heterogeneous Sensor Network (WHSN) is constructed from various sensor nodes with diverse capabilities in sensing units, transmission power levels, and energy resources, among a few others. The primary objective of the research reported in this thesis is to address the problem of power efficient topology optimization in WHSN, which is a much more complicated issue for network reliability, compared with homogeneous wireless sensor network (WSN). Two fundamental problems of topology management are addressed in this thesis: power scheduling based topology control and power adaptive localization. Distributed power scheduling offers an efficient way for the dynamic construction of network topology to meet its connectivity and reliability requirements. Power adaptive localization provides geographical information for topology management during the process of power adjustment. / Ren, Hongliang. / "February 2008." / Adviser: Qing-Hu Max Meng. / Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 4944. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (p. 140-157). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Wang Dan. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (p. 128-138). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Iterative Learning Control (ILC) iterates with a real world control system repeatedly performing the same task. It adjusts the control action based on error history from the previous iteration, aiming to converge to zero tracking error. ILC has been widely used in various applications due to its high precision in trajectory tracking, e.g. semiconductor manufacturing sensors that repeatedly perform scanning maneuvers. Designing effective feedback controllers for non-minimum phase (NMP) systems can be challenging. Applying Iterative Learning Control (ILC) to NMP systems is particularly problematic. Asking for zero error at sample times usually involves inverting the control system. However, the inverse process is unstable when the system has NMP zeros. The control action will grow exponentially every time step, and the error between time steps also grows exponentially. If there are NMP zeros on the negative real axis, the control action will alternate its sign every time step. ILC must be digital to use previous run data to improve the tracking error in the current run. There are two kinds of NMP digital systems, ones having intrinsic NMP zeros as images of continuous time NMP zeros, and NMP sampling zeros introduced by discretization. Two ILC design methods have been investigated in this thesis to handle NMP sampling zeros, producing zero tracking error at addressed sample times: (1) One can simply start asking for zero error after a few initial time steps, like using multiple zero order holds for the first addressed time step only (2) Or increase the sample rate, ask for zero error at the original rate, making two or more zero order holds per addressed time step. The internal instability can be manifested by the singular value decomposition of the input-output matrix. Non-minimum phase systems have particularly small singular values which are related to the NMP zeros. The aim is to eliminate these anomalous singular values. However, when applying the second approach, there are cases that the original anomalous singular values are gone, but some new anomalous singular values appear in the system matrix that cause difficulties to the inverse problem. Not asking for zero error for a small number of initial addressed time steps is shown to eliminate all anomalous singular values. This suggests that a more accurate statement of the second approach is: using multiple zero order holds per addressed time step, and eliminating a few initial addressed time steps if there are new anomalous singular values. We also extend the use of these methods to systems having intrinsic NMP zeros. By modifying ILC laws to perform pole-zero cancellation inside the unit circle, we observe that all of the rules for sampling zeros are effective for intrinsic zeros. Hence, one can now achieve convergence to zero tracking error at addressed time steps in ILC of NMP systems with a well behaved control action. In addition, this thesis studies the robustness of the two approaches along with several other candidate approaches with respect to model parameter uncertainty. Three classes of ILC laws are used. Both approaches show great robustness. Quadratic cost ILC is seen to have substantially better robustness to parameter uncertainty than the other laws.