An improved direct torque controlled interior permanent magnet synchronous machine drive without a speed sensor

Some essential and important improvements of the direct torque controlled interior permanent magnet (IPM) synchronous machine drive are presented in this thesis. These studies, including analysis, modeling and experimental implementations confirm the possibility of a high performance direct torque controlled IPM synchronous motor drive without any continuous rotor position and speed sensor and without any current controller. The direct torque control technique, the comparison between DTC and FOC, and compensation methods for the problems/limitations associated with DTC have been investigated in this thesis. A number of important problems that affect the accuracy of the estimated machine flux linkage on which the DTC technique is built are thoroughly examined. Estimation of stator resistance variation, analysis and compensation of the non-linear effects of the inverter such as forward voltage drop and dead-time, speed sensorless control, and torque and flux ripple minimization for a direct torque controlled IPM motor drive are of major concern in this thesis. A Proportional-Integral stator resistance estimator based on stator current has been investigated for the compensation of any variation in stator resistance. It is shown that the estimator can track the variation of the stator resistance adequately. The scheme utilizes the error between the actual current and the reference current and requires no position signal. Modeling and experimental results will be shown. The non-linear effects of the inverter affect flux estimation greatly, especially at low speed. The effects such as forward voltage drop, dead-time and switching delay is analyzed, they degrade the system performance by introducing error between the estimated values and the actual values. The effects of the forward voltage drop and deadtime can be compensated by using a look-up table. The performance improvement of the drive has been shown in experiments. A speed estimation scheme based on stator flux linkage estimation is adopted and investigated experimentally. Furthermore, the possibility of fielding-weakening operation of the speed sensorless control is also investigated by modeling. The torque and flux ripples are significant problems of the DTC, and are mentioned widely. In order to solve this problem, the changes of torque and flux linkage over a sampling period are derived. Based on the analysis, a modified DTC is proposed to overcome these significant problems. Modeling and experimental results confirm the effectiveness of the proposed scheme. The field weakening control and speed sensorless control scheme is also combined with the proposed scheme. The experimental results show the new DTC scheme can achieve wider range operation and speed sensorless control successfully. The torque and flux ripples are reduced greatly under the new scheme in all experimental results. These abovementioned studies have clearly established that the DTC technique for the IPM machine is now much closer to being a viable and cost-effective candidate for a sensorless PM synchronous motor drive

Top Income Inequality, Aggregate Saving and the Gains from Trade

Canonical studies of trade liberalization focus on its effects on aggregate income and on the distribution of income. The interaction between these two effects of trade liberalization has been less studied. In this dissertation, I study this interaction. More precisely, I study the relationship between international trade and income inequality, with a focus on the implications for aggregate saving and the gains from trade. I argue that accounting for the effects of international trade on income inequality among entrepreneurs can imply higher gains from trade for workers. In the second chapter, I construct a dynamic model of international trade with incomplete markets. In the model, entrepreneurs face uninsurable idiosyncratic productivity risk, and thus save. Since the most productive entrepreneurs have the highest saving rate and are the ones that export, a reduction in trade costs increases their share of total profits and their savings, which leads to a large increase in the aggregate supply of capital and increased capital accumulation. I calibrate the model using US data and examine the effects of international trade on aggregate output, the consumption of workers, and the consumption of entrepreneurs with heterogeneous productivity. In the model, international trade increases aggregate output by 2.5% and the wage of workers by 3.4%. On the other hand, while the aggregate consumption of entrepreneurs is unchanged by international trade, the increase in inequality of profits among entrepreneurs implies that the certainty-equivalent consumption of a typical entrepreneur actually decreases by 4.0%. Capital accumulation plays an important role in the model, accounting for 51.9% of the output gains from trade. To isolate the effects of the proposed mechanism, I construct a benchmark model with complete markets, in which firms with heterogeneous productivity are owned by a single entrepreneur. In this complete markets benchmark, the increase in aggregate output due to international trade is 1.8% while the increase in the wage of workers from trade is 2.7%. Therefore, the novel mechanism in my model increases the wage gains for workers by 25.9%, and the gains in aggregate output by 38.9%, compared to the complete markets benchmark. In the third chapter, I test the key predictions of the model using country-level data. Using fixed-effects (FE) regressions in a large panel of countries, I find a significant and positive correlation between trade openness and the aggregate saving rate. I find a much weaker relationship between trade openness and the investment rate. Furthermore, I show that greater trade openness has a stronger effect on the aggregate saving rate in a country where the initial top 10% share of total income (before any changes in trade openness) is higher. This is in line with my model where the increase in the aggregate saving is driven by top income earners. Additionally, I build on the gravity-based instrumental-variable (IV) approach pioneered by Frankel and Romer (1999) and extend it to a panel setting. I find a larger effect of trade openness on the aggregate saving rate in the fixed-effects panel regressions with IV than without IV. The results provide strong evidence that a supply-side channel of increased capital accumulation is operative following an increase in trade openness. In the fourth chapter, I study the relationship between the household saving rate and openness in China through the lens of the framework outlined in the second chapter. I show that there has been a large increase in top income shares both among entrepreneurs and workers over the past 30 years in China. Additionally, there is a very significant and positive correlation between top income shares and the household saving rate across Chinese counties. Using the setting of the 1992 liberalization episode, I find that provinces with a greater increase in openness experienced a larger increase in the household saving rate during the period. Taken together, the evidence is supportive of the hypothesis that greater openness increases the household saving rate in China, by increasing the share of total income received by the highest-income households who also have the highest saving rate

Risk and contributing factors of ecosystem shifts over naturally vegetated land under climate change in China.

Identifying the areas at risk of ecosystem transformation and the main contributing factors to the risk is essential to assist ecological adaptation to climate change. We assessed the risk of ecosystem shifts in China using the projections of four global gridded vegetation models (GGVMs) and an aggregate metric. The results show that half of naturally vegetated land surface could be under moderate or severe risk at the end of the 21st century under the middle and high emission scenarios. The areas with high risk are the Tibetan Plateau region and an area extended northeastward from the Tibetan Plateau to northeast China. With the three major factors considered, the change in carbon stocks is the main contributing factor to the high risk of ecosystem shifts. The change in carbon fluxes is another important contributing factor under the high emission scenario. The change in water fluxes is a less dominant factor except for the Tibetan Plateau region under the high emission scenario. Although there is considerable uncertainty in the risk assessment, the geographic patterns of the risk are generally consistent across different scenarios. The results could help develop regional strategies for ecosystem conservation to cope with climate change

FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders

This paper proposes a general spectral analysis framework that thwarts a security risk in federated Learning caused by groups of malicious Byzantine attackers or colluders, who conspire to upload vicious model updates to severely debase global model performances. The proposed framework delineates the strong consistency and temporal coherence between Byzantine colluders' model updates from a spectral analysis lens, and, formulates the detection of Byzantine misbehaviours as a community detection problem in weighted graphs. The modified normalized graph cut is then utilized to discern attackers from benign participants. Moreover, the Spectral heuristics is adopted to make the detection robust against various attacks. The proposed Byzantine colluder resilient method, i.e., FedCut, is guaranteed to converge with bounded errors. Extensive experimental results under a variety of settings justify the superiority of FedCut, which demonstrates extremely robust model performance (MP) under various attacks. It was shown that FedCut's averaged MP is 2.1% to 16.5% better than that of the state of the art Byzantine-resilient methods. In terms of the worst-case model performance (MP), FedCut is 17.6% to 69.5% better than these methods

Consensus seeking in multi-agent systems with an active leader and communication delays

summary:In this paper, we consider a multi-agent consensus problem with an active leader and variable interconnection topology. The dynamics of the active leader is given in a general form of linear system. The switching interconnection topology with communication delay among the agents is taken into consideration. A neighbor-based estimator is designed for each agent to obtain the unmeasurable state variables of the dynamic leader, and then a distributed feedback control law is developed to achieve consensus. The feedback parameters are obtained by solving a Riccati equation. By constructing a common Lyapunov function, some sufficient conditions are established to guarantee that each agent can track the active leader by assumption that interconnection topology is undirected and connected. We also point out that some results can be generalized to a class of directed interaction topologies. Moreover, the input-to-state stability (ISS) is obtained for multi-agent system with variable interconnection topology and communication delays in a disturbed environment

Global method for a class of operation optimization problem in steel rolling systems

Many steel products are produced in hot or cold rolling lines with multiple stands. The steel material becomes thinner after being rolled at each stand. Steady-state parameters for controlling the rolling line need to be set so as to satisfy the final product specifications and minimize the total energy consumption. This paper develops a generalized geometric programming model for this setting problem and proposes a global method for solving it. The model can be expressed with a linear objective function and a set of constraints including nonconvex ones. Through constructing lower bounds of some components, the constraints can be converted to convex ones approximately. A sequential approximation method is proposed in a gradually reduced interval to improve accuracy and efficiency. However, the resulting convex programming model in each iteration is still complicated. To reduce the power, it is transformed into a second-order cone programming (SOCP) model and solved using alternating direction method of multipliers (ADMM). The effectiveness of the global method is tested using real data from a hot-rolling line with seven stands. The results demonstrate that the proposed global method solves the problem effectively and reduces the energy consumption