Thomas Seel

IMU-based joint angle measurement for gait analysis.

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°.

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

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 Réseaux Electriques (CIGRE) benchmark medium voltage distribution network.

Iterative Learning Control for Variable Pass Length Systems

Abstract Monotonic convergence of linear iterative learning control (ILC) systems with changing pass length is considered. The maximum pass length (MPL) error is introduced as a useful concept for convergence analysis of this class of systems. Using the lifted-system framework, a both necessary and sufficient monotonic convergence criterion is found for the 1-norm of the MPL error. Further findings on 2-norm and ∞-norm convergence are added. Finally, an example system is given, i.e. the control of an electrical stimulation system for gait assistance, and simulation results are provided.

A consensus-based distributed voltage control for reactive power sharing in microgrids

We propose a consensus-based distributed voltage control (DVC), which solves the problem of reactive power sharing in autonomous meshed inverter-based microgrids with inductive power lines. Opposed to other control strategies available thus far, the DVC does guarantee reactive power sharing in steady-state while only requiring distributed communication among inverters, i.e. no central computing nor communication unit is needed. Moreover, we provide a necessary and sufficient condition for local exponential stability. In addition, the performance of the proposed control is compared to the usual voltage droop control [1] in a simulation example based on the CIGRE benchmark medium voltage distribution network.

Monotonic convergence of iterative learning control systems with variable pass length

ABSTRACT A growing number of researchers consider iterative learning control (ILC) a promising tool for numerous control problems in biomedical application systems. We will briefly discuss why classical ILC theory is technically too restrictive for some of these applications. Subsequently, we will extend the classical ILC design in the lifted systems framework to the class of repetitive trajectory tracking tasks with variable pass length. We will analyse the closed-loop dynamics for two standard learning laws, and we will discuss in which sense the tracking error can be reduced by which controller design strategies. Necessary and sufficient conditions for monotonic convergence will be derived. We then summarise all results in a set of practical controller design guidelines. Finally, a simulation study is presented, which demonstrates the usefulness of these guidelines and illustrates the special dynamics that occur in variable pass length learning.

Iterative Learning Control with variable pass length applied to trajectory tracking on a crane with output constraints

A typical application of Iterative Learning Control (ILC), namely trajectory tracking on a lab-scale gantry crane, is considered. However, the load is only allowed to move in the close proximity of the reference trajectory. Since these output constraints lead to disrupted trials, the pass length in this ILC system is not constant. In this contribution, we present new results on convergence and new combinations of methods for this class of ILC systems and apply them to the given application. Simulation and experimental results are provided which demonstrate that both maximum pass length and small tracking error can be achieved in very few iterations even in the presence of tight constraints and model uncertainties.

Alignment-Free, Self-Calibrating Elbow Angles Measurement Using Inertial Sensors

Due to their relative ease of handling and low-cost, inertial measurement unit (IMU) based joint angle measurements are used for a widespread range of applications. These include sports performance, gait analysis and rehabilitation (e.g. Parkinsons disease monitoring or post-stroke assessment). However, a major downside of current algorithms recomposing human kinematics from IMU data is that they require calibration motions and/or the careful alignment of the IMUs respective to their body segment. In this article, we propose a new method, which is alignment free and self-calibrated using the arbitrary movements of the user and an initial zero reference arm pose. The proposed method utilizes real time optimization to identify the two dominant axes of rotation of the elbow joint. Using a two degree of freedom joint mimicking the human elbow, the performance of the algorithm was assessed by comparing the angles obtained from two IMUs to the ones obtained from a marker-based optical tracking system. The self-calibration proved to converge within seconds and the RMS errors with respect to the optical reference system were below 5°. Our method can be particularly useful in the field of telerehabilitation, where precise manual sensor to segment alignment as well as precise, predefined calibration movements are impractical.

Feedback Control of Foot Eversion in the Adaptive Peroneal Stimulator

The limited ability to dorsiflex the foot, known as drop foot, can be treated by functional electrical stimulation. Therein, undesired foot eversion/inversion is a common problem which is usually corrected by tedious manual repositioning of the electrodes. We address this issue by presenting a feedback-control solution featuring three major contributions: (1) an algorithm for inertial sensor-based foot-to-ground angle measurement with periodic drift correction; (2) a three-electrode setup that allows distribution of an overall stimulation intensity to the tibialis anterior muscle and to the superficial peroneal nerve that innervates the fibularis longus muscle, thus decoupling dorsiflexion and eversion control; (3) a run-to-run controller and an iterative learning controller, both of which use step-by-step learning to achieve desired eversion foot-to-ground angles. Experiments with a chronic drop foot patient demonstrate compensation of undesired eversion/inversion within at most two steps, while dorsiflexion angle trajectories are not affected.

Iterative learning control of drop foot stimulation with array electrodes for selective muscle activation

Disorders of the central nervous system like stroke often lead to a paresis of the muscles responsible for dorsiexion and eversion of the foot during swing phase. Functional electrical stimulation (FES) is commonly used to improve the foot movement. A precise placement of two single surface electrodes on the shank as well as a manual tuning of heel- switch triggered stimulation is required when using standard drop-foot stimulators. In this work, the use of automatically tuned array electrodes for selective nerve/muscle stimulation and the application of iterative learning control for adjusting stimulation intensities are investigated with the aim to obtain an optimal foot movement. The stimulation eect is observed by means of two inertial measurement units mounted on the foot and shank, respectively. Two array electrodes are placed over the areas covering the peroneal nerve and the muscle tibialis anterior. A fast identication procedure nds three suitable sets of array elements, forming so-called virtual electrodes, in both arrays. Two stimulation channels are established over the three virtual electrodes. Both channels produce dorsiexion, but one promotes eversion and the other inversion. A decoupling matrix is applied to the stimulation intensities of both channels in order to obtain independent control over dorsiexion and eversion. Finally, two independent iterative learning controllers are employed to achieve desired angle proles. The proposed stimulation and control scheme is initially tested on a healthy person sitting on a table with the shank and foot free to swing. A tracking RMS error of less than one degree is achieved within six walking steps.

Iterative Learning Cascade Control of Continuous Noninvasive Blood Pressure Measurement

A noninvasive continuous blood pressure measurement technique that has been developed lately requires precise control of the blood flow through a superficial artery. The flow is measured using ultrasound and influenced via manipulating the pressure inside an inflatable air balloon which is placed over the artery. This contribution is concerned with the design and evaluation of a learning cascaded control structure for such measurement devices. Two feedback control loops are designed in discrete time via pole placement and then combined with an iterative learning control. The latter exploits the repetitive nature of the disturbance that is induced by the oscillating arterial pressure. Experimental results indicate that the proposed controller structure yields considerably smaller set point deviations than previous approaches.