Jonathan Brembeck

Human machine interface concept for interactive motion control of a highly maneuverable robotic vehicle

A vehicle dynamics human machine interface (HMI) concept for the operation of a highly maneuverable vehicle is presented. This is motivated by the research vehicle ROMO, which is being developed by the German Aerospace Center (DLR) to facilitate research on a wide spectrum of scientific questions dealing with electric and autonomous mobility. With four wheel individual large range steering and electric wheel hub motors, the vehicle dynamics variables of yaw rate, side slip angle and vehicle velocity can be decoupled, thus opening new motion possibilities. The HMI approach includes distinction of various motion operating modes, suitable reference motion parameterization and adequate filtering of the operators inputs, both depending on the operating mode. For rotating on the spot the instantaneous center of rotation may be interactively set by the operator on a touch screen.

Energy optimal control of an over actuated Robotic Electric Vehicle using enhanced control allocation approaches

In this paper an energy optimal control strategy for a highly maneuverable Robotic Electric Vehicle (ROboMObil) is presented. The ROMO is a development of the Robotics and Mechatronics Center (which is part of the German Aerospace Center) to cope with several research topics, like energy efficient, autonomous or remote controlled driving for future (electro-) mobility applications. Since saving electric energy is a primal goal when operating a battery electric vehicle (like the ROMO), we have developed a new approach for energy optimal control of an over-actuated electric car. The focus of the control strategy lies in the model based minimization of the actuator losses and power consumption for driving along a precalculated trajectory to optimize the overall efficiency. The approach is based on a real-time capable nonlinear control allocation (CA) algorithm, using quadratic programming, implemented in the object oriented modeling language Modelica. Two optimization objectives are analyzed and the performance is presented by simulation results. Finally an CA extension to nonlinear dynamic inversion is discussed, which is able to compensate the different time constants of the actuators.

Framework for the Evaluation of Wheel Torque Blending Algorithms

Electric vehicle power train concepts with wheel-hub or close-to-wheel propulsion open up a whole new area of vehicle control possibilities. The DLR robotic concept vehicle ROMO allows the evaluation of these possibilities using integrated chassis control. With its electric drive train it is possible to recuperate kinetic energy during braking process. Using only the electric motors for braking, an increased mass of electric drives & energy storage would be necessary in order to comply to legal passenger vehicle braking regulations. Therefore, the combination of friction based brake and electric motor brake can be advantageous. In this paper, object oriented models (Modelica) of a permanent magnet synchronous machine driven electro-hydraulic disc brake and an in-wheel direct driven traction motor are presented. Furthermore, two torque blending algorithms of an electro-hydraulic disc brake and an in-wheel permanent magnet synchronous machine are evaluated. One algorithm is based on a dynamic control allocation using real-time capable quadratic programming, the other is a feed forward control based on heuristic considerations. Finally, four comparison methods are presented and applied to evaluate the performance of the used torque blending.

Modellbasierte Regelungsansätze für überaktuierte planetare Rover und robotische Elektromobile

Zusammenfassung Der Beitrag beschreibt modellbasierte Regelungsstrategien für planetare Rover und innovative Elektromobile. Trotz prinzipiell gleichen Aufbaus ihrer Fahrwerke werden doch unterschiedliche Reglerlösungen bevorzugt, deren Bandbreite in diesem Beitrag vorgestellt wird. Beide Systeme sind überaktuierte robotische Fahrzeuge, deren Regler die jeweils unabhängig ansteuerbaren Rad- und Lenkantriebe koordinieren. Im Fall des planetaren Rovers wird ein Fahrzeugmodell mit Rad-Sand-Kontaktdynamik verwendet, das innerhalb eines modellprädiktiven Reglers (MPC) zum Einsatz kommt. Für das wesentlich dynamischere Straßenfahrzeug wird der MPC-Ansatz erweitert. Zur Reduzierung der Steuerungsdimension wird ein invertiertes Modell der Fahrdynamik eingeführt und das Optimierungsproblem auf das Bestimmen von Hilfsgrößen für die verbleibenden Freiheitsgrade reduziert. Abstract The paper introduces model based control strategies for planetary rovers and novel electric vehicles. Despite the same fundamental structure of their chassis different types of controllers are preferred. The bandwidth of potential solutions is shown in the paper. Both systems are overactuated robotic vehicles with controllers for coordinating the individually controlable wheel and steering drives. For planetary rovers a model predictive control (MPC) approach is implemented that considers the specific wheel-soil contact dynamics. The solution for the dynamic road vehicles goes beyond the MPC approach. For minimizing the number of controls an inverted vehicle dynamics model is introduced such that the optimization problem is reduced on computing auxiliary variables only, which are representing the remaining degrees of freedom of the system.

Experimental investigation of online path planning for electric vehicles

This paper describes a real-time capable online path planning on roads and its experimental investigation for the highly maneuverable robotic electric vehicle research platform ROboMObil. The path planning algorithm is based on an efficiently solvable and compact optimization problem and contributes to the autonomous driving of centralized controlled vehicles. The necessary development from a global offline problem formulation towards an online receding horizon method is shown, which is capable of taking environmental changes into account. The online planned path together with a generated velocity profile serves as an input to the ROboMObils geometric path following control, allowing for automated driving. Finally, a test drive shows the results of implementing the presented algorithms on this research vehicle and investigates the energy saving capabilities of the proposed path planning methodology.

Model based vertical dynamics estimation with Modelica and FMI

This paper analyses the performance of Modelica implemented state estimation algorithms for semi-active suspension control for the DLR ROboMObil (ROMO). In this approach the prediction model for the vertical dynamics state estimation and the tire contact force estimation is designed as a quarter vehicle model, which directly incorporates all relevant nonlinear parts. Based on this prediction model a square root unscented Kalman-filter (SR-UKF) is implemented, using the DLR Modelica Kalman-filter library and the Functional Mockup Interface (FMI). In a consecutive step this prediction model is extended by introducing an input port for road obstacle information, e. g. extracted from image data from ROMO 360 degree stereo surround view. The observer design and implementation on real-time hardware are performed in Modelica using the automated tool chain from the Modelica simulator to the Rapid Control Prototyping (RCP) hardware. Experimental results from a four post test-rig and simulations illustrate, that the estimation accuracy can be improved by the SR-UKF compared to an extended Kalman filter (EKF) based implementation.

Advanced path following control of an overactuated robotic vehicle

This work describes an advanced path following control strategy enabling overactuated robotic vehicles like the ROboMObil (ROMO) [1] to automatically follow predefined paths while all states of the vehicles planar motion are controlled. This strategy is useful for autonomous vehicles which are guided along online generated paths including severe driving maneuvers caused by e.g. obstacle avoidance. The proposed approach combines path following, i.e. tracking a plane curve without a priori time parameterization of a trajectory, with feedback based vehicle dynamics stabilization. A path interpolation method is introduced which allows to perform the path following task employing a trajectory tracking controller. Furthermore a tracking controller based on I/O linearization and quadratic programming based control allocation is proposed which allows employing the vehicles overactuation in an optimal manner. The work concludes by a simulative evaluation of the controller performance.

Inverse Model Based Torque Vectoring Control For a Rear Wheel Driven Battery Electric Vehicle

This paper presents the torque vectoring control concept for a vehicle with two powerful wheel individual electric drives at the rear axle. The direct yaw moment control which is enabled by a differential torque at the rear axle drives offers the potential for shaping the yaw motion of the car in a considerable range. The control concept introduced here is primarily oriented at practical considerations. Together with the tools presented it is conveniently adaptable to any vehicle data. The focus is on yaw dynamics control. A reference yaw rate is determined by combining conveniently tunable linear dynamics with a nonlinear steady state gain, the latter in order to establish a desired self-steering behavior. An inverse single track model is used for cancellation of the yaw dynamics induced by the yaw torque. It is a significant part of the applied feed-forward control. Moreover, it is employed by the optional yaw rate feedback control which is based on the inverse disturbance observer scheme. Both the effectiveness of the control concept and the practical ease of control parameter calibration were validated in driving experiments.

A command governor approach for platooning applications

The present work is concerned with the design of a longitudinal control strategy for platooning applications. Our aim consists in developing a longitudinal controller that can simultaneously offer string stable operation and guarantee satisfaction of physical and safety constraints. To tackle this challenge, a command governor control scheme, based on a two-loop cascade structure, is developed. The inner loop relies on a linear control method and it is able to provide string stable operation for small signals, i.e. when control constraints are inactive. The outer loop revolves around a command governor approach. It monitors the operation of the inner-loop and, when violation of constraints is imminent, changes the inner-loops setpoint in order to honor the control constraints. Simulation results demonstrate the effectiveness of the proposed approach.

Design and validation of an MPC-based torque blending and wheel slip control strategy

This article presents a braking control algorithm for electric vehicles endowed with redundant actuators, i.e. friction brakes and wheel-individual electric motors. This algorithm relies on a model predictive control framework and is able to optimally split the wheel braking torque among the redundant actuators, while providing anti-lock braking features (i.e. wheel slip regulation). It will be shown that, the integration of these two control functions together with energy metrics, actuator constraints and dynamics improves the control performance compared to state-of-art control structures. Additionally, experimental measurements recorded with our prototype vehicle demonstrate a precise wheel slip regulation and high energy efficiency of the proposed braking control methodology.