pwc-archive/papers-with-abstracts huggingface dataset:
A Linear Parameter-Varying Approach to Data Predictive Control
Authors: Chris Verhoek, Julian Berberich, Sofie Haesaert, Roland Tóth, Hossam S. Abbas
Date: 2023-11-13 00:00:00
Tasks: LEMMA

By means of the linear parameter-varying (LPV) Fundamental Lemma, we derive novel data-driven predictive control (DPC) methods for LPV systems. In particular, we present output-feedback and state-feedback-based LPV-DPC methods with terminal ingredients, which guarantee exponential stability and recursive feasibility. We provide methods for the data-based computation of these terminal ingredients. Furthermore, an in-depth analysis of the application and implementation aspects of the LPV-DPC schemes is given, including application for nonlinear systems and handling noisy data. We compare and demonstrate the performance of the proposed methods in a detailed simulation example involving a nonlinear unbalanced disc system.
Recursive and non-recursive filters for sequential smoothing and prediction with instantaneous phase and frequency estimation applications (extended version)
Authors: Hugh Lachlan Kennedy
Date: 2023-11-13 00:00:00

A simple procedure for the design of recursive digital filters with an infinite impulse response (IIR) and non-recursive digital filters with a finite impulse response (FIR) is described. The fixed-lag smoothing filters are designed to track an approximately polynomial signal of specified degree without bias at steady state, while minimizing the gain of high-frequency (coloured) noise with a specified power spectral density. For the IIR variant, the procedure determines the optimal lag (i.e. the passband group delay) yielding a recursive low-complexity smoother of low order, with a specified bandwidth, and excellent passband phase linearity. The filters are applied to the problem of instantaneous frequency estimation, e.g. for Doppler-shift measurement, for a complex exponential with polynomial phase progression in additive white noise. For this classical problem, simulations show that the incorporation of a prediction filter (with a one-sample lead) reduces the incidence of (phase or frequency) angle unwrapping errors, particularly for signals with high rates of angle change, which are known to limit the performance of standard FIR estimators at low SNR. This improvement allows the instantaneous phase of low-frequency signals to be estimated, e.g. for time-delay measurement, and/or the instantaneous frequency of frequency-modulated signals, down to a lower SNR. In the absence of unwrapping errors, the error variance of the IIR estimators (with the optimal phase lag) reaches the FIR lower bound, at a significantly lower computational cost. Guidelines for configuring and tuning both FIR and IIR filters are provided.
Clifford Algebra-Based Iterated Extended Kalman Filter with Application to Low-Cost INS/GNSS Navigation
Authors: Wei Ouyang, Yutian Wang, Yuanxin Wu
Date: 2023-11-13 00:00:00
Tasks: State Estimation

The traditional GNSS-aided inertial navigation system (INS) usually exploits the extended Kalman filter (EKF) for state estimation, and the initial attitude accuracy is key to the filtering performance. To spare the reliance on the initial attitude, this work generalizes the previously proposed trident quaternion within the framework of Clifford algebra to represent the extended pose, IMU biases and lever arms on the Lie group. Consequently, a quasi-group-affine system is established for the low-cost INS/GNSS integrated navigation system, and the right-error Clifford algebra-based EKF (Clifford-RQEKF) is accordingly developed. The iterated filtering approach is further applied to significantly improve the performances of the Clifford-RQEKF and the previously proposed trident quaternion-based EKFs. Numerical simulations and experiments show that all iterated filtering approaches fulfill the fast and global convergence without the prior attitude information, whereas the iterated Clifford-RQEKF performs much better than the others under especially large IMU biases.
Time-Optimal Control for High-Order Chain-of-Integrators Systems with Full State Constraints and Arbitrary Terminal States (Extended Version)
Authors: Yunan Wang, Chuxiong Hu, Zeyang Li, Shize Lin, Suqin He, Yu Zhu
Date: 2023-11-13 00:00:00
Tasks: Trajectory Planning

Time-optimal control for high-order chain-of-integrators systems with full state constraints and arbitrarily given terminal states remains a challenging problem in the optimal control theory domain, yet to be resolved. To enhance further comprehension of the problem, this paper establishes a novel notation system and theoretical framework, providing the switching manifold for high-order problems in the form of switching laws. Through deriving properties of switching laws regarding signs and dimension, this paper proposes a definite condition for time-optimal control. Guided by the developed theory, a trajectory planning method named the manifold-intercept method (MIM) is developed. The proposed MIM can plan time-optimal jerk-limited trajectories with full state constraints, and can also plan near-optimal non-chattering higher-order trajectories with negligible extra motion time compared to optimal profiles. Numerical results indicate that the proposed MIM outperforms all baselines in computational time, computational accuracy, and trajectory quality by a large gap.
An Event-Based Synchronization Framework for Controller Hardware-in-the-loop Simulation of Electric Railway Power Electronics Systems
Authors: Jialin Zheng, Yangbin Zeng, Han Xu, Weicheng Liu, Di Mou, Zhengming Zhao
Date: 2023-11-13 00:00:00

The Controller Hardware_in_the_loop (CHIL) simulation is gaining popularity as a cost_effective, efficient, and reliable tool in the design and development process of fast_growing electrified transportation power converters. However, it is challenging to implement the conventional CHIL simulations on the railway power converters with complex topologies and high switching frequencies due to strict real_time constraints. Therefore, this paper proposes an event-based synchronization CHIL (ES_CHIL) framework for high_fidelity simulation of these electrified railway power converters. Different from conventional CHIL simulations synchronized through the time axis, the ES_CHIL framework is synchronized through the event axis. Therefore, it can ease the real_time constraint and broaden the upper bound on the system size and switching frequency. Besides, models and algorithms with higher accuracy, such as the diode model with natural commutation processes, can be used in the ES-CHIL framework. The proposed framework is validated for a 350 kW wireless power transformer system containing 24 fully controlled devices and 36 diodes by comparing it with Simulink and physical experiments. This research improves the fidelity and application range of the power converters CHIL simulation. Thus, it helps to accelerate the prototype design and performance evaluation process for electrified railways and other applications with such complex converters.
Accurate Time-segmented Loss Model for SiC MOSFETs in Electro-thermal Multi-Rate Simulation
Authors: Jialin Zheng, Zhengming Zhao, Han Xu, Weicheng Liu, Yangbin Zeng
Date: 2023-11-13 00:00:00

Compared with silicon (Si) power devices, Silicon carbide (SiC) devices have the advantages of fast switching speed and low on-resistance. However, the effects of non-ideal characteristics of SiC MOSFETs and stray parameters (especially parasitic inductance) on switching losses need to be further evaluated. In this paper, a transient loss model based on SiC MOSFET and SiC Schottky barrier diode (SBD) switching pairs is proposed. The transient process analysis is simplified by time segmentation of the transient process of power switching devices. The electro-thermal simulation calculates the junction temperature and updates the temperature-related parameters with the proposed loss model and the thermal network model. A multi-rate data exchange strategy is proposed to solve the problem of disparity in timescales between circuit simulation and thermal network simulation. The CREE CMF20120D SiC MOSFET device is used for the experimental verification. The experimental results verify the accuracy of the model which provides guidance for the circuit design of SiC MOSFETs. All the parameters of the loss model can be extracted from the datasheet, which is practical in power electronics design.
Modeling the impact of extreme summer drought on conventional and renewable generation capacity: methods and a case study on the Eastern U.S. power system
Authors: Hang Shuai, Fangxing Li, Jinxiang Zhu, William Jerome Tingen II, Srijib Mukherjee
Date: 2023-11-13 00:00:00

The United States has witnessed a growing prevalence of droughts in recent years, posing significant challenges to water supplies and power generation. The resulting impacts on power systems, including reduced capacity and the potential for power outages, underscore the need for accurate assessment methods to ensure the reliable operation of the nation's energy infrastructure. A critical step is to evaluate the usable capacity of a regional power system's generation fleet, which is a complex undertaking and requires precise modeling of the effects of hydrological and meteorological conditions on diverse generating technologies. This paper proposes a systematic, analytical approach for assessing the impacts of extreme summer drought events on the available capacity of hydro, thermal, and renewable energy generators. More specifically, the systematic framework provides plant-level capacity derating models for hydroelectric, once-through cooling thermoelectric, recirculating cooling thermoelectric, combustion turbine, solar PV, and wind turbine systems. Application of the proposed impact assessment framework to the 2025 generation fleet of the real-world power system in the PJM and SERC regions yields insightful results. By examining the daily usable capacity of 6,055 at-risk generators throughout the study region, we find that in the event of the recurrence of the 2007 southeastern summer drought in the near future, the usable capacity of all at-risk power plants may experience a substantial decrease compared to a typical summer, falling within the range of 71% to 81%. The sensitivity analysis reveals that the usable capacity would experience a more pronounced decline under more severe drought conditions. The findings of this study offer valuable insights, enabling stakeholders to enhance the resilience of power systems against the potential effects of extreme drought in the future.
A Different View of Sigma-Delta Modulators Under the Lens of Pulse Frequency Modulation
Authors: Victor Medina, Pieter Rombouts, Luis Hernandez
Date: 2023-11-12 00:00:00
Tasks: Quantization

The fact that VCO-ADCs produce noise-shaped quantization noise suggests that a link between frequency modulation and Sigma-Delta modulation should exist. The connection between a VCO-ADC and a first-order Sigma-Delta modulator has been already explained using Pulse Frequency Modulation. In this paper, we attempt to extend the theory based on Pulse Frequency Modulation to a generic Sigma-Delta modulator. We show that this link between Sigma-Delta modulation and Pulse Frequency Modulation relies in a sampling invariance property that defines the equivalence between both entities. This novel point of view, allows to go beyond the white quantization noise model of a Sigma-Delta modulator, revealing the origin of some nonlinear phenomena. We first predict spurious tones which cannot be explained by circuit non linearity. Multi-bit and single bit modulators are shown to belong to a same generic class of systems. Finally, quantizer overload is analyzed using our model. The results are applied to Continuous-Time Sigma-Delta modulators of orders one, two and three and then extended to a generic case.
Distributed Sequential Receding Horizon Control of Multi-Agent Systems under Recurring Signal Temporal Logic
Authors: Eleftherios E. Vlahakis, Lars Lindemann, Dimos V. Dimarogonas
Date: 2023-11-12 00:00:00
Tasks: Scheduling

We consider the synthesis problem of a multi-agent system under signal temporal logic (STL) specifications representing bounded-time tasks that need to be satisfied recurrently over an infinite horizon. Motivated by the limited approaches to handling recurring STL systematically, we tackle the infinite-horizon control problem with a receding horizon scheme equipped with additional STL constraints that introduce minimal complexity and a backward-reachability-based terminal condition that is straightforward to construct and ensures recursive feasibility. Subsequently, we decompose the global receding horizon optimization problem into agent-level programs the objectives of which are to minimize local cost functions subject to local and joint STL constraints. We propose a scheduling policy that allows individual agents to sequentially optimize their control actions while maintaining recursive feasibility. This results in a distributed strategy that can operate online as a model predictive controller. Last, we illustrate the effectiveness of our method via a multi-agent system example assigned a surveillance task.
Distributed Charging Coordination of Electric Trucks with Limited Charging Resources
Authors: Ting Bai, Yuchao Li, Karl Henrik Johansson, Jonas Mårtensson
Date: 2023-11-12 00:00:00

Electric trucks usually need to charge their batteries during long-range delivery missions, and the charging times are often nontrivial. As charging resources are limited, waiting times for some trucks can be prolonged at certain stations. To facilitate the efficient operation of electric trucks, we propose a distributed charging coordination framework. Within the scheme, the charging stations provide waiting estimates to incoming trucks upon request and assign charging ports according to the first-come, first-served rule. Based on the updated information, the individual trucks compute where and how long to charge whenever approaching a charging station in order to complete their delivery missions timely and cost-effectively. We perform empirical studies for trucks traveling over the Swedish road network and compare our scheme with the one where charging plans are computed offline, assuming unlimited charging facilities. It is shown that the proposed scheme outperforms the offline approach at the expense of little communication overhead.
Mass Loss and Displacement Modeling for Multi-Axis Milling
Authors: Adrian Karl Rüppel, Patrick Ochudlo, Mathias Bickel, Sebastian Stemmler, Thomas Bergs, Dirk Abel
Date: 2023-11-12 00:00:00

During the cutting process, material of the workpiece is continuously being removed by the cutting tool, which results in a reduction of mass as well as a displacement in the center of the workpiece mass. When using workpiece sided force sensors, such as table dynamometers, the total mass and the displacement of the center of mass affects the force measurement due to gravitational and inertial effects. The high flexibility of the milling process leads to a complex change of volume and mass and necessitates the consideration of the engagement conditions between tool and workpiece along the tool path in order to estimate changes in mass and center of mass. This paper proposes a method for estimating the mass loss and the displacement of the center of mass during multi-axis milling processes. In this method the tool gets numerically sliced along the tool axis and the workpiece removal for each slice along an arbitrary tool path gets calculated. To validate the mass loss model, experiments in both three-axis milling as well as multi-axis milling processes have been conducted. Since it is difficult to measure the center of mass, validation for the displacement of the center of mass was done by comparison with data extracted from CAD. The results show good agreement between the simulated and measured mass loss using the proposed approach.
A Nonlinear Negative Imaginary Systems Framework with Actuator Saturation for Control of Electrical Power Systems
Authors: Yijun Chen, Kanghong Shi, Ian R. Petersen, Elizabeth L. Ratnam
Date: 2023-11-12 00:00:00

In the transition to net zero, it has been suggested that a massive expansion of the electric power grid will be required to support emerging renewable energy zones. In this paper, we propose the use of battery-based feedback control and nonlinear negative imaginary systems theory to reduce the need for such an expansion by enabling the more complete utilization of existing grid infrastructure. By constructing a novel Lur'e-Postnikov-like Lyapunov function, a stability result is developed for the feedback interconnection of a nonlinear negative imaginary system and a nonlinear negative imaginary controller. Additionally, a new class of nonlinear negative imaginary controllers is proposed to deal with actuator saturation. We show that in this control framework, the controller eventually leaves the saturation boundary, and the feedback system is locally stable in the sense of Lyapunov. This provides theoretical support for the application of battery-based control in electrical power systems. Validation through simulation results for single-machine-infinite-bus power systems supports our results. Our approach has the potential to enable a transmission line to operate at its maximum power capacity, as stability robustness is ensured by the use of a feedback controller.
Data-Driven Moving Horizon Estimation Using Bayesian Optimization
Authors: Qing Sun, Shuai Niu, Minrui Fei
Date: 2023-11-12 00:00:00
Tasks: Bayesian Optimization, State Estimation

In this work, an innovative data-driven moving horizon state estimation is proposed for model dynamic-unknown systems based on Bayesian optimization. As long as the measurement data is received, a locally linear dynamics model can be obtained from one Bayesian optimization-based offline learning framework. Herein, the learned model is continuously updated iteratively based on the actual observed data to approximate the actual system dynamic with the intent of minimizing the cost function of the moving horizon estimator until the desired performance is achieved. Meanwhile, the characteristics of Bayesian optimization can guarantee the closest approximation of the learned model to the actual system dynamic. Thus, one effective data-driven moving horizon estimator can be designed further on the basis of this learned model. Finally, the efficiency of the proposed state estimation algorithm is demonstrated by several numerical simulations.
Challenges in Controllers on UAV Aircraft: Theory and Practice
Authors: Hans Oersted, Yudong Ma
Date: 2023-11-12 00:00:00

This review explores the theoretical foundations and experimental dynamics of modern tiltrotor aircraft. Emphasizing feedback linearization, the study delves into the distinctive constraints and angular velocity ranges shaping tiltrotor behavior. Experimental findings highlight challenges in tracking circular trajectories, with color-coded representations illustrating the impact of angular velocity. Practical implications for applications like unmanned aerial vehicles are discussed, alongside identified challenges and avenues for future research. This work contributes to both theoretical understanding and practical considerations in the evolving field of tiltrotor control.
Enhancing Control Performance through ESN-Based Model Compensation in MPC for Dynamic Systems
Authors: Shuai Niu, Qing Sun, Minrui Fei, Xuqian Ju
Date: 2023-11-12 00:00:00
Tasks: Model Predictive Control

Deriving precise system dynamic models through traditional numerical methods is often a challenging endeavor. The performance of Model Predictive Control is heavily contingent on the accuracy of the system dynamic model. Consequently, this study employs Echo State Networks to acquire knowledge of the unmodeled dynamic characteristics inherent in the system. This information is then integrated with the nominal model, functioning as a form of model compensation. The present paper introduces a control framework that combines ESN with MPC. By perpetually assimilating the disparities between the nominal and real models, control performance experiences augmentation. In a demonstrative example, a second order dynamic system is subjected to simulation. The outcomes conclusively evince that ESNbased MPC adeptly assimilates unmodeled dynamic attributes, thereby elevating the system control proficiency.
Equal Incremental Cost-Based Optimization Method to Enhance Efficiency for IPOP-Type Converters
Authors: Hanfeng Cai, Haiyang Liu, Heyang Sun, Qiao Wang
Date: 2023-11-12 00:00:00

Systematic optimization over a wide power range is often achieved through the combination of modules of different power levels. This paper addresses the issue of enhancing the efficiency of a multiple module system connected in parallel during operation and proposes an algorithm based on equal incremental cost for dynamic load allocation. Initially, a polynomial fitting technique is employed to fit efficiency test points for individual modules. Subsequently, the equal incremental cost-based optimization is utilized to formulate an efficiency optimization and allocation scheme for the multi-module system. A simulated annealing algorithm is applied to determine the optimal power output strategy for each module at given total power flow requirement. Finally, a dual active bridge (DAB) experimental prototype with two input-parallel-output-parallel (IPOP) configurations is constructed to validate the effectiveness of the proposed strategy. Experimental results demonstrate that under the 800W operating condition, the approach in this paper achieves an efficiency improvement of over 0.74\% by comparison with equal power sharing between both modules.
Contraction-based Tracking Control of Electromechanical Systems
Authors: Najmeh Javanmardi, Pablo Borja, Jacquelien M. A. Scherpen
Date: 2023-11-11 00:00:00

This paper addresses the trajectory-tracking problem for a class of electromechanical systems. To this end, the dynamics of the plants are modeled in the so-called port-Hamiltonian framework. Then, the notion of contraction is exploited to design the desired closed-loop dynamics and the corresponding tracking controller. Notably, the proposed control design method does not require solving partial differential equations or changing the coordinates of the plant, which permits preserving the physical interpretation of the controller. The applicability of the proposed approach is illustrated in several electromechanical systems via simulations.
Reconfigurable Inspection in Manufacturing: State of the Art and Taxonomy
Authors: Harshit Gupta, Ashok Kumar Madan
Date: 2023-11-11 00:00:00

This article provides an overview of the evolution of the product quality and measurement inspection procedure with emphasis on the Reconfigurable Inspection System and Machine. The major components of a reconfigurable manufacturing system have been examined, and the evolution of manufacturing processes has been briefly discussed. Different Reconfigurable Inspection Machines (RIMs) and their arrangement in an assembly line as an inspection system have been carefully studied and the modern inspection system equipped in RMS has been compared to the traditional techniques commonly used in inspection of product quality. A survey of evolving inspection techniques is offered from the standpoint of technological challenges and advancement affecting manufacturing over time. As per authors' knowledge, the review on Reconfigurable Inspection in Manufacturing and taxonomy of reconfigurable inspection systems is rare. Considering the studies done in this domain, there is still resourceful taxonomy for this paradigm. Therefore, different types of inspection procedures have been discussed, their features and applications have been compared to arrive at the taxonomy of the RIS based on the understanding of the nature of a RIS after a critical review.