publications

2022

1. 

   Control and Optimization of Lattice Converters

   Zhiting Mei, Jingyang Fang, and Stefan Goetz

   Electronics, Jan 2022

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   Multilevel converters continue their upward trend in renewable generation, electric vehicles, and power quality conditioning applications. Despite having satisfactory voltage capabilities, mainstream multilevel converters suffer from poor current sharing performances, thereby leading to the development of lattice converters, i.e., a strong and versatile type of future multilevel power converters. This article addresses two problems faced by lattice converters. First, we propose and detail how to optimize the efficiency of a given lattice converter by controlling the on/off states of H-bridge submodules. Second, we introduce the method that determines the voltage at each node of the converter in order to satisfy output voltage and current requirements. Design and analysis of lattice converters need a different mathematical toolbox than routinely exercised in power electronics. By use of graph theory, this article provides control methods of 3 × 3 and 4 × 4 lattice converters, satisfying various control objectives such as input/output terminals and output voltages. We further validate the methods with simulation results. The methodologies, algorithms, and special cases described in the article will aid further design and refinement of more efficient and easy-to-control lattice converters.

2023

1. 

   Fundamental Limits for Sensor-Based Robot Control

   Anirudha Majumdar, Zhiting Mei, and Vincent Pacelli

   The International Journal of Robotics Research (IJRR), Aug 2023

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   Our goal is to develop theory and algorithms for establishing fundamental limits on performance for a given task imposed by a robot’s sensors. In order to achieve this, we define a quantity that captures the amount of task-relevant information provided by a sensor. Using a novel version of the generalized Fano inequality from information theory, we demonstrate that this quantity provides an upper bound on the highest achievable expected reward for one-step decision making tasks. We then extend this bound to multi-step problems via a dynamic programming approach. We present algorithms for numerically computing the resulting bounds, and demonstrate our approach on three examples: (i) the lava problem from the literature on partially observable Markov decision processes, (ii) an example with continuous state and observation spaces corresponding to a robot catching a freely-falling object, and (iii) obstacle avoidance using a depth sensor with non-Gaussian noise. We demonstrate the ability of our approach to establish strong limits on achievable performance for these problems by comparing our upper bounds with achievable lower bounds (computed by synthesizing or learning concrete control policies).