Ansgar Trächtler

Integrated vehicle dynamics control using active brake, steering and suspension systems

Active chassis systems like brake, steering, suspension and propulsion systems are increasingly entering the market. In addition to their basic functions, these systems may be used for functions of integrated vehicle dynamics control. A global architecture is required to prevent negative interference, for an optimised functionality and for managing system complexity. Several approaches are known under names like Integrated or Global Chassis Control and Integrated Vehicle Dynamics Control. Vehicle Dynamics Management (VDM) is the Bosch approach for co-ordinating vehicle dynamics functions by integrated control of active chassis systems. Its essential features are a clearly structured, extensible functional architecture with appropriate control structures and system interfaces with physical meaning.

Discrete Mechanics and Optimal Control and its Application to a Double Pendulum on a Cart

Abstract In this paper we present a new approach to determine trajectories for changing the state of the double pendulum on a cart from one equilibrium to another and show the experimental realization on a test bench. The control of these transitions is accomplished by a two-degrees-of-freedom control scheme. For the design of the feedforward and feedback control of the system two models of the double pendulum on a cart are introduced. The feedforward control is achieved by the optimal control method Discrete Mechanics and Optimal Control (DMOC). The trajectories can be optimized with respect to energy consumption and transition time. Additionally, for the applicatory design, the implementation of a feedback control by means of gain-scheduling is explained. As experimental result the realization of a trajectory on the test bench is presented.

Self-optimization of an active suspension system regarding energy requirements

Within the collaborative research center 614: ldquoself-optimizing concepts and structures in mechanical engineeringrdquo methods are developed that enable mechatronic systems to adapt to varying environment and system conditions. An important application example is the innovative railcab system that features autonomously driving rail-bound vehicles. These vehicles contain several subsystems to fulfill specific tasks. The subsystems mainly are coupled via their energy demands. Here an approach for optimizing the main task of one subsystem - the active suspension system - is presented. Based on a given global plan for an entire travel a local model-based optimization is done for each track section. The optimization makes use of data about the excitation that is stored locally at the track and can be accessed by the vehicle before arriving at the specific track section. In this way controller configurations can be calculated that maximize the riding comfort while keeping energy constraints from the superposed system.

Multiobjective Optimization of Control Trajectories for the Guidance of a Rail-Bound Vehicle

Self-optimization refers to the ability of a mechatronic system to autonomously adapt the way it performs its functions to changing environmental and operational conditions or user demands. In this work we propose to use multiobjective optimal control to enable the self-optimization of the guidance of a rail-bound vehicle. We consider different strategies to reduce the computational cost of the optimization. Most importantly, a two-degree-of-freedom controller is used to separate optimal trajectory generation from disturbance compensation. Also, in order to solve the multiobjective optimization problem, an approximation of the entire set of optimal compromises of the objectives, the so-called Pareto set, is computed offline at design time. From this, we can derive a collection of weighting vectors that capture the best trade-off between the objectives for different situations. Given this set of preselected weights, for the online optimization, the objective function can be taken to be a weighted sum that best matches the situation at hand. For the guidance system we consider three objectives. Preliminary offline simulation results are presented.

Hybrid planning and hierarchical optimization of mechatronic systems

In the presented work, a hybrid planner is used to adjust the active suspension system of a novel railway vehicle to upcoming situations. The hybrid planner uses the results from a model based multi objective optimization as a basis for discrete planning in continuous domains. The optimization works on a hierarchically organized set of mechatronic function modules which are represented as a hierarchical model. A centralistic as well as a distributed optimization mode are presented along with a resulting Pareto set.

Parametric Model-Order Reduction in Hierarchical Multiobjective Optimization of Mechatronic Systems

For complex mechatronic systems it is convenient to modularize the system into a hierarchical structure. Especially for self-optimizing systems hierarchies can be used to reduce the complexity. Such systems have an extensive information processing, because they adapt their behavior to varying system and environment conditions in an autonomous way. In this paper we present a parametric model-order reduction based on multi-moment matching that is used to simplify hierarchical models. By this procedure the execution of a hierarchical multiobjective optimization is fastened. We compare our hierarchical approach with a multiobjective optimization of a non-reduced model for an active suspension system. A good approximation of both the Pareto sets and Pareto fronts is obtained by our approach.

Model-Based Design of Mechatronic Systems by Means of Semantic Web Ontologies and Reusable Solution Elements

When designing complex mechatronic systems, a team of developers will be facing many challenges that can impede progress and innovation if not tackled properly. In meeting them simulation tools play a central role. Yet it is often impossible for a single developer to foresee the overall impact a design decision will have on the system and on the other domains involved. For this task multi-domain simulation tools exists, but because of its complexity and the different levels of detail that are needed, the effort to specify a complete system from scratch is very high. Another challenge is the selection of the most suitable solution elements provided by the manufacturers. Currently they are often chosen manually from catalogues. The development engineer is therefore usually inclined to employ well-known solution elements and suppliers. To tackle both challenges our aim is an increase in efficiency and innovation by means of generally available solution knowledge, such as well-proven solution patterns, ready-to-use solution elements, and established simulation models [1].Our paper presents a tool-supported, sequential design process. From the outset, the comprehensive functional capability of the designed system is supervised by means of multi-domain simulation. At significant points in the design process, solution knowledge can be accessed as it is stored in ontologies and therefore available via Semantic Web [2]. Thus, one can overcome barriers resulting from different terminologies or referential systems and furthermore infer further knowledge from the stored knowledge. The paper focuses on an early testing in the conceptual design stage and on the subsequent semantic search for suitable solution elements. After the specification of a principle solution for the mechatronic system by combining solution patterns, an initial multi-domain model of the system is created. This is done on the basis of the active structure and of idealized simulation models which are part of a free library and associated with the chosen solution patterns via the ontologies. In further designing the controlled system and its parameters with the completed model, the developer defines additional criteria to be fed into the subsequent semantic search for solution elements. Information on the latter is provided by the manufacturers as well as detailed simulation models, which are used to analyze the functional capability of the concretized system. Therefore, the corresponding idealized models are replaced automatically with the parameterized models of the solution elements containing for example the specific friction model for the chosen motor. We show this process using the concrete example of a dough-production system. In particular, we focus on its transport system. Resulting requirements for the simulation models and their level of detail are expound, as well as the architecture and benefits of the ontologies.Copyright

Optimal control inputs to affect vehicle dynamics in various driving states

Abstract This paper explores vehicle dynamics in skid maneuvers. First, modeling of vehicle dynamics in skid maneuvers is discussed and a vehicle model suited for the situation is developed. The model is used for analyzing the theoretical maximum stabilizing yaw moment, which can be generated in skidding by vehicle control systems. The maximum yaw moment is found by optimization in an exemplary skid maneuver. From analysis of the results, different underlying physical principles that govern yaw moment generation can be revealed. The sensitivity of the optimization results is analyzed by varying the vehicle geometry and tire force characteristics.

Iterative Learning and Self-Optimization Techniques for the Innovative Railcab-System

In this paper, we propose a new concept for information processing of networked vision sensors for surveillance. The networked sensor technology has a potential capability to solve some of our most important scientific and societal problems. But, difficulties of processing are always big problems in case of such huge amount of information acquired by the distributed vision systems. The proposed concept gets a hint from information processing of human hearing organs and compound eyes of insects. By a basic experiment, we confirmed that the proposed concept can be utilized to detect human behavior

Control strategies on stable manifolds for energy-efficient swing-ups of double pendula

Optimal control problems for mechanical systems often arise in technical applications. To find solutions with minimal control effort, the system’s natural, uncontrolled dynamics can be used. Promising candidates to be considered for energy-efficient trajectories are highly dynamic, but uncontrolled motions on (un)stable manifolds of equilibria. In this contribution, we propose a control strategy for mechanical systems which sequences uncontrolled trajectories on (un)stable manifolds with short control manoeuvres to design a feedforward control. In particular, we present optimal swing-up solutions for a double pendulum which are based on trajectories on the stable manifold of the pendulum’s up–up equilibrium. To demonstrate the advantages of our approach compared to a black-box optimisation, we perform a post-optimisation with the optimal control sequence as an initial guess. The numerical results are evaluated in a simulation environment for the double pendulum on a cart and applied to a real test rig.