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.