Christian Klauer

Control Systems Theory and Application -- Neuroprosthetic Systems -- Robotics


I am a control scientist for application and theory of (non) linear control systems currently focusing on autonomous driving and biomedical engineering. Working as a research scientist in the Control Systems Group at Technische Universität Berlin, I received my Ph.D. Herein, I investigated novel control schemes for neuroprosthesis based on Functional Electrical Stimulation to restore lost motor functions, e.g., in case of stroke and Spinal Cord Injury (SCI).

After my Ph.D., I continued as a postdoctoral research fellow in the Neuroengineering and Robotics Lab (NearLab), Politecnico di Milano on the restoration of paralyzed hand functions. Today, I am involved in control for autonomous vehicles with TomTom Location Technology Germany GmbH., RG
>> Thesis <<


Autonomous Driving

Nonlinear path following control based on velocity decoupling, and motion planning for TomTom's autonomous vehicle. [IFAC 2020]

Control of a Car-Like Vehicle

A simulation of a (simplified) closed-loop path-tracking controller as presented in [IFAC 2020]. In this demonstration, a steering disturbance can be applied to illustrate the impact of road inclination, side-wind, side-slip, and other effects. In the absence of a disturbing influence, the controller-internal velocity decoupling allows exact path tracking independent of the driving velocity.
In vehicle tests (Volvo XC-90), the controller achieves a lateral tracking accuracy of around 8 cm.


Electrical stimulation and control to restore paralyzed hand functions. DAAD-funded, [REF]

FES Hand Neuroprosthesis

This project considers paralyzed hand functions as a typical cause of stroke. Electrical pulses applied to the motor nerves induce artificial muscle contractions (Functional Electrical Stimulation, FES). Three muscles are involved to control arbitrary angles of wrist extension and to realize grasping. A feedback of the electrical muscle responses (Electromyography, EMG) is used to estimate the muscle-internal states and to ensure the desired reaction of the muscles to FES. Only one EMG-channel is required to observe all three muscles.

In an earlier stage, open-loop control was investigated, which was then superseded by automatic, feedback-controlled approaches (see video). Herein, with feedback being active, the operator manually issued movement commands, e.g., to extend the wrist or grasp.

The control system was tested in ten healthy subjects (not all shown in the video). They were asked to not volitionally participate in the hand movements. Placing the stimulation electrodes involves a trial-and-error procedure before activating the system to find good positions. However, with an increasing number of tested participants, a better, more general understanding of the placement could be found.

Arm Elevation Support

Support of the arm elevation in presence of a paresis / weak residual muscle activity (BMBF (German Federal ministry of Education and Research), BeMobil,TU-Berlin, 2014-2017)

FES Arm Neuroprosthesis

This is a system intended for persons who have only weak residual activity in the shoulder deltoid muscle. By Functional Electrical Stimulation (FES) an artificial muscle contraction is added such that the full extent of arm movements is restored. Behind the scenes, a positive-feedback control system measures the elevation-angle to amplify volitional movements. Electromyography (EMG) is used to compensate for the effects of rapidly progressing muscle fatigue under FES.

In effect, the participant (stroke, paresis in shoulder deltoid muscle) shown in this video could elevate his arm much higher than without the arm neuroprosthesis. The entire movement is still under his control, as the system detects his intention to move.

Restoration of Reaching Functions

Restoration of reaching functions (EU-FP7, MUNDUS,TU-Berlin, 2010-2013)

FES Arm Neuroprosthesis and Exoskelleton

Within the European project MUNDUS, an assistive framework was developed for the support of arm and hand functions during daily life activities in severely impaired people. This contribution aims at designing a feedback control system for Neuro-Muscular Electrical Stimulation (NMES) to enable reaching functions in people with no residual voluntary control of the arm and shoulder due to high level spinal cord injury. NMES is applied to the deltoids and the biceps muscles and integrated with a three degrees of freedom (DoFs) passive exoskeleton, which partially compensates gravitational forces and allows to lock each DOF. The user is able to choose the target hand position and to trigger actions using an eyetracker system. The target position is selected by using the eyetracker and determined by a marker-based tracking system using Microsoft Kinect. A central controller, i.e., a finite state machine, issues a sequence of basic movement commands to the real-time arm controller. The NMES control algorithm sequentially controls each joint angle while locking the other DoFs. Daily activities, such as drinking, brushing hair, pushing an alarm button, etc., can be supported by the system. The robust and easily tunable control approach was evaluated with five healthy subjects during a drinking task. Subjects were asked to remain passive and to allow NMES to induce the movements. In all of them, the controller was able to perform the task, and a mean hand positioning error of less than five centimeters was achieved. The average total time duration for moving the hand from a rest position to a drinking cup, for moving the cup to the mouth and back, and for finally returning the arm to the rest position was 71 s.

Research on feedback controlled Neuro-Prosthetic Systems

Neuro-prosthetic systems using Functional Electrical Stimulation (FES) to restore or support motor functions in neurologically impaired patients are considered. FES stimulated muscles usually behave highly non-linear and time variant. Hence, an often encountered difficulty is the precise control of limb motion by adjusting the stimulation intensity.
The considered topics include:

Recruitment Control

A novel control method (Lambda, λ-control) was developed which linearises the static input non-linearity (recruitment curve) of FES-activated muscles by fast feedback of the detected muscle activity caused by FES. This activity is estimated by the FES evoked electromyogram (eEMG). It could be shown that the developed approach robustly compensates the unwanted effects related to uncertainties and time-variances of the highly nonlinear muscular recruitment behavior. The Lambda-control method allows the precise adjustment of the muscular recruitment. Therefore, the feedback-controlled muscles are much easier to model and to control in Neuro-prosthetic systems.

Brain Computer Interface (BCI)

Cooperation with Berlin Brain-computer interface Group to investigate EEG-based BCI for the control of an upper-limb neuroprosthesis (EU-FP7,, TU-Berlin, 2010-2013)

FES Arm Neuroprosthesis controlled by a Brain-computer Interface

A two-class brain-computer interface (Berlin-BCI) allows the user to select a target position. This position is then continuously realized by a feedback-controlled FES-system attached to the shoulder deltoid to produce horizontal arm movements.

Research on Discrete-Time Control Systems

Because of the pulse-based nature present in Functional Electrical Stimulation, the framework of discrete-time control perfectly suitable for control systems' design. Therefore, the following topics were addressed:

Inertial Measurement Units (IMU)

Feedback control of limb movements requires a measurement of motion information e.g. inter-segment joint angles or velocities. Because of their small size, Inertial Motion Units are a perfect choice. Singal processing employing kinematic models allows obtaining the needed information in real-time.

Open-Source Software Projects (ORTD): A framework for the implementation of advanced real-time control systems aiming to be an open-source alternative to Simulink Coder. It provides features including state machines and, as a real-time capable interpreter is used, sub-simulations that can be exchanged during runtime. Besides, this framework properly handles multiple threads, their communication, allows to synchronize control systems to external events (e.g. even irregular timers or incoming network packages) and provides many other nice features. Because of a high-level schematic-description language -- based on Scilab commands provided by a toolbox -- lower implementation-effort results compared to C/C++.

This framework has grown since 2008 along with the needs for the implementation of the feedback-control schemes in the area of neuroprosthetics.

An axample application, ready to launch in your browser (at least chrome and firefox): The ORTD-interpreter (C++) is running a control system inside a docker container along with node.js (Javascript) to provide a html-based user interface. Source Code


Refered Articles

Conference and Workshop Papers