There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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Biomechanics And Motor Control Of Human Movement

Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. This paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.

https://ieeexplore.ieee.org/ielaam/5165411/8075182/7990560-aam.pdf

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

Event-triggered consensus of multiagent systems (MASs) has attracted tremendous attention from both theoretical and practical perspectives due to the fact that it enables all agents eventually to reach an agreement upon a common quantity of interest while significantly alleviating utilization of communication and computation resources. This paper aims to provide an overview of recent advances in event-triggered consensus of MASs. First, a basic framework of multiagent event-triggered operational mechanisms is established. Second, representative results and methodologies reported in the literature are reviewed and some in-depth analysis is made on several event-triggered schemes, including event-based sampling schemes, model-based event-triggered schemes, sampled-data-based event-triggered schemes, and self-triggered sampling schemes. Third, two examples are outlined to show applicability of event-triggered consensus in power sharing of microgrids and formation control of multirobot systems, respectively. Finally, some challenging issues on event-triggered consensus are proposed for future research.

/pdf/an-overview-of-recent-advances-in-event-triggered-consensus-3rmvpx3qkr.pdf

An Overview of Recent Advances in Event-Triggered Consensus of Multiagent Systems

This contribution is concerned with joint angle calculation based on inertial measurement data in the context of human motion analysis. Unlike most robotic devices, the human body lacks even surfaces and right angles. Therefore, we focus on methods that avoid assuming certain orientations in which the sensors are mounted with respect to the body segments. After a review of available methods that may cope with this challenge, we present a set of new methods for: (1) joint axis and position identification; and (2) flexion/extension joint angle measurement. In particular, we propose methods that use only gyroscopes and accelerometers and, therefore, do not rely on a homogeneous magnetic field. We provide results from gait trials of a transfemoral amputee in which we compare the inertial measurement unit (IMU)-based methods to an optical 3D motion capture system. Unlike most authors, we place the optical markers on anatomical landmarks instead of attaching them to the IMUs. Root mean square errors of the knee flexion/extension angles are found to be less than 1° on the prosthesis and about 3° on the human leg. For the plantar/dorsiflexion of the ankle, both deviations are about 1°.

/pdf/imu-based-joint-angle-measurement-for-gait-analysis-2qhn2z6umj.pdf

IMU-based joint angle measurement for gait analysis.

We propose a consensus-based distributed voltage control (DVC) that solves the problem of reactive power sharing in autonomous inverter-based microgrids with dominantly inductive power lines and arbitrary electrical topology. Opposed to other control strategies available thus far, the control presented here does guarantee a desired reactive power distribution in steady state while only requiring distributed communication among inverters, i.e., no central computing nor communication unit is needed. For inductive impedance loads and under the assumption of small phase angle differences between the output voltages of the inverters, we prove that the choice of the control parameters uniquely determines the corresponding equilibrium point of the closed-loop voltage and reactive power dynamics. In addition, for the case of uniform time constants of the power measurement filters, a necessary and sufficient condition for local exponential stability of that equilibrium point is given. The compatibility of the DVC with the usual frequency droop control for inverters is shown and the performance of the proposed DVC is compared with the usual voltage droop control via simulation of a microgrid based on the Conseil International des Grands Reseaux Electriques (CIGRE) benchmark medium voltage distribution network.

/pdf/voltage-stability-and-reactive-power-sharing-in-inverter-1palkyvfd8.pdf

Voltage Stability and Reactive Power Sharing in Inverter-Based Microgrids With Consensus-Based Distributed Voltage Control

We consider 6d inertial measurement units (IMU) attached to rigid bodies, e.g. human limb segments or links of a robotic manipulator, which are connected by hinge joints and spheroidal joints. Novel methods for joint axis estimation and joint position estimation are presented that exploit the kinematic constraints induced by these two types of joints. The presented methods do not require any knowledge about the sensor units' positions or orientations and do not include integration, i.e. they are insensitive to measurement bias. By means of a three-links simulation model, the estimation algorithms are validated and convergence is analyzed. Finally, the algorithms are tested using experimental data from IMU-based human gait analysis.

/pdf/joint-axis-and-position-estimation-from-inertial-measurement-5bwg9nun58.pdf

Joint axis and position estimation from inertial measurement data by exploiting kinematic constraints

Iterative learning control of a drop foot neuroprosthesis — Generating physiological foot motion in paretic gait by automatic feedback control

http://www.control.tu-berlin.de/wiki/images/e/e3/Seel2011.pdf

Thomas Seel

Papers

IMU-based joint angle measurement for gait analysis.

Voltage Stability and Reactive Power Sharing in Inverter-Based Microgrids With Consensus-Based Distributed Voltage Control

Joint axis and position estimation from inertial measurement data by exploiting kinematic constraints

Iterative learning control of a drop foot neuroprosthesis — Generating physiological foot motion in paretic gait by automatic feedback control

Iterative Learning Control for Variable Pass Length Systems