Switching Control for Parameter Identifiability of Uncertain Systems

This paper considers the problem of identifying the parameters of an uncertain linear system by means of feedback control. The problem is approached by considering time-varying controllers. It is shown that even when the uncertainty set is not finite, parameter identifiability can be generically ensured by switching among a finite number of linear time-invariant controllers. The results are shown to have several implications, ranging from fault detection and isolation to adaptive and supervisory control. Practical aspects of the problem are also discussed in details

Tradeoffs between AC power quality and DC bus ripple for 3-phase 3-wire inverter-connected devices within microgrids

Visions of future power systems contain high penetrations of inverters which are used to convert power from dc (direct current) to ac (alternating current) or vice versa. The behavior of these devices is dependent upon the choice and implementation of the control algorithms. In particular, there is a tradeoff between dc bus ripple and ac power quality. This study examines the tradeoffs. Four control modes are examined. Mathematical derivations are used to predict the key implications of each control mode. Then, an inverter is studied both in simulation and in hardware at the 10 kVA scale, in different microgrid environments of grid impedance and power quality. It is found that voltage-drive mode provides the best ac power quality, but at the expense of high dc bus ripple. Sinusoidal current generation and dual-sequence controllers provide relatively low dc bus ripple and relatively small effects on power quality. High-bandwidth dc bus ripple minimization mode works well in environments of low grid impedance, but is highly unsuitable within higher impedance microgrid environments and/or at low switching frequencies. The findings also suggest that the certification procedures given by G5/4, P29 and IEEE 1547 are potentially not adequate to cover all applications and scenarios

Robot Introspection with Bayesian Nonparametric Vector Autoregressive Hidden Markov Models

Robot introspection, as opposed to anomaly detection typical in process monitoring, helps a robot understand what it is doing at all times. A robot should be able to identify its actions not only when failure or novelty occurs, but also as it executes any number of sub-tasks. As robots continue their quest of functioning in unstructured environments, it is imperative they understand what is it that they are actually doing to render them more robust. This work investigates the modeling ability of Bayesian nonparametric techniques on Markov Switching Process to learn complex dynamics typical in robot contact tasks. We study whether the Markov switching process, together with Bayesian priors can outperform the modeling ability of its counterparts: an HMM with Bayesian priors and without. The work was tested in a snap assembly task characterized by high elastic forces. The task consists of an insertion subtask with very complex dynamics. Our approach showed a stronger ability to generalize and was able to better model the subtask with complex dynamics in a computationally efficient way. The modeling technique is also used to learn a growing library of robot skills, one that when integrated with low-level control allows for robot online decision making.Comment: final version submitted to humanoids 201

Detecting Topology Variations in Dynamical Networks

This paper considers the problem of detecting topology variations in dynamical networks. We consider a network whose behavior can be represented via a linear dynamical system. The problem of interest is then that of finding conditions under which it is possible to detect node or link disconnections from prior knowledge of the nominal network behavior and on-line measurements. The considered approach makes use of analysis tools from switching systems theory. A number of results are presented along with examples

Design and realization of a smart battery management system

Battery management system (BMS) emerges a decisive system component in battery-powered applications, such as (hybrid) electric vehicles and portable devices. However, due to the inaccurate parameter estimation of aged battery cells and multi-cell batteries, current BMSs cannot control batteries optimally, and therefore affect the usability of products. In this paper, we proposed a smart management system for multi-cell batteries, and discussed the development of our research study in three directions: i) improving the effectiveness of battery monitoring and current sensing, ii) modeling the battery aging process, and iii) designing a self-healing circuit system to compensate performance variations due to aging and other variations.published_or_final_versio

Dynamic Hand Gesture-Featured Human Motor Adaptation in Tool Delivery using Voice Recognition

Human-robot collaboration has benefited users with higher efficiency towards interactive tasks. Nevertheless, most collaborative schemes rely on complicated human-machine interfaces, which might lack the requisite intuitiveness compared with natural limb control. We also expect to understand human intent with low training data requirements. In response to these challenges, this paper introduces an innovative human-robot collaborative framework that seamlessly integrates hand gesture and dynamic movement recognition, voice recognition, and a switchable control adaptation strategy. These modules provide a user-friendly approach that enables the robot to deliver the tools as per user need, especially when the user is working with both hands. Therefore, users can focus on their task execution without additional training in the use of human-machine interfaces, while the robot interprets their intuitive gestures. The proposed multimodal interaction framework is executed in the UR5e robot platform equipped with a RealSense D435i camera, and the effectiveness is assessed through a soldering circuit board task. The experiment results have demonstrated superior performance in hand gesture recognition, where the static hand gesture recognition module achieves an accuracy of 94.3\%, while the dynamic motion recognition module reaches 97.6\% accuracy. Compared with human solo manipulation, the proposed approach facilitates higher efficiency tool delivery, without significantly distracting from human intents.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Time-Delay Switch Attack on Networked Control Systems, Effects and Countermeasures

In recent years, the security of networked control systems (NCSs) has been an important challenge for many researchers. Although the security schemes for networked control systems have advanced in the past several years, there have been many acknowledged cyber attacks. As a result, this dissertation proposes the use of a novel time-delay switch (TDS) attack by introducing time delays into the dynamics of NCSs. Such an attack has devastating effects on NCSs if prevention techniques and countermeasures are not considered in the design of these systems. To overcome the stability issue caused by TDS attacks, this dissertation proposes a new detector to track TDS attacks in real time. This method relies on an estimator that will estimate and track time delays introduced by a hacker. Once a detector obtains the maximum tolerable time delay of a plant’s optimal controller (for which the plant remains secure and stable), it issues an alarm signal and directs the system to its alarm state. In the alarm state, the plant operates under the control of an emergency controller that can be local or networked to the plant and remains in this stable mode until the networked control system state is restored. In another effort, this dissertation evaluates different control methods to find out which one is more stable when under a TDS attack than others. Also, a novel, simple and effective controller is proposed to thwart TDS attacks on the sensing loop (SL). The modified controller controls the system under a TDS attack. Also, the time-delay estimator will track time delays introduced by a hacker using a modified model reference-based control with an indirect supervisor and a modified least mean square (LMS) minimization technique. Furthermore, here, the demonstration proves that the cryptographic solutions are ineffective in the recovery from TDS attacks. A cryptography-free TDS recovery (CF-TDSR) communication protocol enhancement is introduced to leverage the adaptive channel redundancy techniques, along with a novel state estimator to detect and assist in the recovery of the destabilizing effects of TDS attacks. The conclusion shows how the CF-TDSR ensures the control stability of linear time invariant systems

Mechatronic Tools for the Modeling and Design of Servo Motor Actuated Belt Driven Motion Systems

Mechatronics is defined as the synergistic integration of physical systems, electronics, controls, and computers through the design process, from the very start of the design process, thus enabling complex decision making. This definition reveals the elements involved yet it eludes to the complexity and the constant balance of tradeoffs which are prevalent in the context of applying Mechatronics to a successful design process. This work pursues the use of various tools for the application of Mechatronics to the modeling and design of a servo motor driven motion system. The use of Mechatronics is pervasive in and among today\u27s highly integrated devices and systems. By virtue of the fact that the phrase Mechatronics may carry different meaning depending upon ones discipline or industry, the most general definition is sought and embodied within the work. An overview of the relevant discipline specific perspectives is offered; as sufficient background for the systems modeling and analysis presented. In the course of developing and applying a Mechatronics design process for servo motor actuated motion systems, the use of frequency response analysis and alternative modeling techniques is emphasized, not only as a tool for understanding and applying the matter but, also for the purposes of model verification. These efforts culminate in the design and testing of a physical realization of one of the models presented; the servo motor actuated compliant belt system with compliance and friction. The results of this work underscore the notion that using a Mechatronics design process while devising a servo motor driven motion system enables optimization and functionality not otherwise realizable. These results are supported with experimental verification and comparison. The implications of this work are threefold: the work equips the Mechatronics practitioner with the tools required for verification of the results of modeling and analysis, the work provides an upgrade to the tools and equipment available in the College of Engineering at Marquette University, and the work will likely inspire additional related projects