Cooperative Adaptive Control for Cloud-Based Robotics

This paper studies collaboration through the cloud in the context of cooperative adaptive control for robot manipulators. We first consider the case of multiple robots manipulating a common object through synchronous centralized update laws to identify unknown inertial parameters. Through this development, we introduce a notion of Collective Sufficient Richness, wherein parameter convergence can be enabled through teamwork in the group. The introduction of this property and the analysis of stable adaptive controllers that benefit from it constitute the main new contributions of this work. Building on this original example, we then consider decentralized update laws, time-varying network topologies, and the influence of communication delays on this process. Perhaps surprisingly, these nonidealized networked conditions inherit the same benefits of convergence being determined through collective effects for the group. Simple simulations of a planar manipulator identifying an unknown load are provided to illustrate the central idea and benefits of Collective Sufficient Richness.Comment: ICRA 201

Wide-Area Composite Load Parameter Identification Based on Multi-Residual Deep Neural Network

Accurate and practical load modeling plays a critical role in the power system studies including stability, control, and protection. Recently, wide-area measurement systems (WAMSs) are utilized to model the static and dynamic behavior of the load consumption pattern in real-time, simultaneously. In this article, a WAMS-based load modeling method is established based on a multi-residual deep learning structure. To do so, a comprehensive and efficient load model founded on combination of impedance–current–power and induction motor (IM) is constructed at the first step. Then, a deep learning-based framework is developed to understand the time-varying and complex behavior of the composite load model (CLM). To do so, a residual convolutional neural network (ResCNN) is developed to capture the spatial features of the load at different location of the large-scale power system. Then, gated recurrent unit (GRU) is used to fully understand the temporal features from highly variant time-domain signals. It is essential to provide a balance between fast and slow variant parameters. Thus, the designed structure is implemented in a parallel manner to fulfill the balance and moreover, weighted fusion method is used to estimate the parameters, as well. Consequently, an error-based loss function is reformulated to improve the training process as well as robustness in the noisy conditions. The numerical experiments on IEEE 68-bus and Iranian 95-bus systems verify the effectiveness and robustness of the proposed load modeling approach. Furthermore, a comparative study with some relevant methods demonstrates the superiority of the proposed structure. The obtained results in the worst-case scenario show error lower than 0.055% considering noisy condition and at least 50% improvement comparing the several state-of-art methods.©2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Assessment of dynamic and long-term performance of an innovative multi-story timber building via structural monitoring and dynamic testing

Peer reviewedPostprin

Analysis And Mitigation Of The Impacts Of Delays In Control Of Power Systems With Renewable Energy Sources

ABSTRACT Analysis and Mitigation of the Impacts of Delays in Control of Power Systems with Renewable Energy Sources by Chang Fu Apr. 2019 Advisor : Dr. Caisheng Wang Major : Electrical and Computer Engineering Degree : Doctor of Philosophy With the integration of renewable resources, electric vehicles and other uncertain resources into power grid, varieties of control topology and algorithms have been proposed to increase the stability and reliability of the operation system. Load modeling is an critical part in such analysis since it significantly impacts the accuracy of the simulation in power system, as well as stability and reliability analysis. Traditional power system composite load model parameter identification problems can be essentially ascribed to optimization problems, and the identied parameters are point estimations subject to dierent constraints. These conventional point estimation based composite load modeling approaches suer from disturbances and noises and provide limited information of the system dynamics. In this thesis, a statistic (Bayesian Estimation) based distribution estimation approach is proposed for composite load models, including static (ZIP) and dynamic (Induction Motor) parts, by implementing Gibbs sampling. The proposed method provides a distribution estimation of coecients for load models and is robust to measurement errors. The overvoltage issue is another urgent issues need to be addressed, especially in a high PV penetration level system. Various approaches including the real power control through photovoltaic (PV) inverters have been proposed to mitigate such impact, however, most of the existing methods did not include communication delays in the control loop. Communication delays, short or long, are inevitable in the PV voltage regulation loop and can not only deteriorate the system performance with undesired voltage quality but also cause system instability. In this thesis, a method is presented to convert the overvoltage control problem via PV inverters for multiple PVs into a problem of single-input-single-output (SISO) systems. The method can handle multiple PVs and dierent communication delays. The impact of communication delays is also systematically analyzed and the maximum tolerable delay is rigorously obtained. Dierent from linear matrix inequality (LMI) techniques that have been extensively studied in handling systems with communication delays, the proposed method gives the necessary and sucient condition for obtaining a controller and the design procedure is explicitly and constructively given in the paper. The effectiveness of the proposed method is veried by simulation studies on a distribution feeder and the widely-used 33-bus distribution test system. The similar design strategy can be utilized to mitigate delay impacts in Load frequency control (LFC) as well. LFC has been considered as one of the most important frequency regulation mechanisms in modern power system. One of the inevitable problems involved in LFC over a wide area is communication delay. In this thesis, an alternative design method is proposed to devise delay compensators for LFC in one or multiple control areas. For one-area LFC, a sucient and necessary condition is given for designing a delay compensator. For multiarea LFC with area control errors (ACEs), it is demonstrated that each control area can have its delay controller designed as that in a one-area system if the index of coupling among the areas is below the threshold value determined by the small gain theorem. Effectiveness of the proposed method is veried by simulation studies on LFCs with communication delays in one and multiple interconnected areas with and without time-varying delays, respectively

A dynamic Bayesian nonlinear mixed-effects model of HIV response incorporating medication adherence, drug resistance and covariates

HIV dynamic studies have contributed significantly to the understanding of HIV pathogenesis and antiviral treatment strategies for AIDS patients. Establishing the relationship of virologic responses with clinical factors and covariates during long-term antiretroviral (ARV) therapy is important to the development of effective treatments. Medication adherence is an important predictor of the effectiveness of ARV treatment, but an appropriate determinant of adherence rate based on medication event monitoring system (MEMS) data is critical to predict virologic outcomes. The primary objective of this paper is to investigate the effects of a number of summary determinants of MEMS adherence rates on virologic response measured repeatedly over time in HIV-infected patients. We developed a mechanism-based differential equation model with consideration of drug adherence, interacted by virus susceptibility to drug and baseline characteristics, to characterize the long-term virologic responses after initiation of therapy. This model fully integrates viral load, MEMS adherence, drug resistance and baseline covariates into the data analysis. In this study we employed the proposed model and associated Bayesian nonlinear mixed-effects modeling approach to assess how to efficiently use the MEMS adherence data for prediction of virologic response, and to evaluate the predicting power of each summary metric of the MEMS adherence rates.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS376 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org