Stability bounds for systems and mechanisms in linear descriptor form

Mathematical models for simulation and control of systems and mechanisms naturally arise in a descriptor form. The stability analysis of descriptor systems, involving free parameters as uncertainties or design qualifiers is subject of this paper. Two approaches for the calculation of the stability boundaries in the underlying parameter space are discussed. The first one uses a quantifier elimination method, while the second one is based on the direct solution of the Lyapunov equation. The computational methods are exemplary demonstrated on Chua’s circuit

Meeting User Needs in Vehicle Automation

This paper gives an overview of the results of the German national project AutoAkzept. The objective of the project was to develop solutions for the design of automated vehicles that promote the development of trust and thus acceptance for connected, cooperative, and automated mobility by reducing or even preventing subjective uncertainties and associated negative experiences. To this end, AutoAkzept developed technological building blocks for the assessment of activities and states of users of automated vehicles, the creation and application of individual user profiles for the optimization of system adaptation to users as well as strategies for adapting the behavior of automated vehicles in terms of information transfer, interior set-up, routing, and driving style selection. In developing these solutions, the project focused on the essential needs of users of automated systems. These needs should be considered in the conception and design of automated vehicles as well as in their operational use

What Is the Best Way to Optimally Parameterize the MPC Cost Function for Vehicle Guidance?

Model predictive control (MPC) is a promising approach to the lateral and longitudinal control of autonomous vehicles. However, the parameterization of the MPC with respect to high-level requirements such as passenger comfort, as well as lateral and longitudinal tracking, is challenging. Numerous tuning parameters and conflicting requirements need to be considered. In this paper, we formulate the MPC tuning task as a multi-objective optimization problem. Its solution is demanding for two reasons: First, MPC-parameterizations are evaluated in a computationally expensive simulation environment. As a result, the optimization algorithm needs to be as sample-efficient as possible. Second, for some poor parameterizations, the simulation cannot be completed; therefore, useful objective function values are not available (for instance, learning with crash constraints). In this work, we compare the sample efficiency of multi-objective particle swarm optimization (MOPSO), a genetic algorithm (NSGA-II), and multiple versions of Bayesian optimization (BO). We extend BO by introducing an adaptive batch size to limit the computational overhead. In addition, we devise a method to deal with crash constraints. The results show that BO works best for a small budget, NSGA-II is best for medium budgets, and none of the evaluated optimizers are superior to random search for large budgets. Both proposed BO extensions are, therefore, shown to be beneficial