Robust controller design for active trailer steering systems of articulated vehicles using multi-objective optimization

This thesis presents and evaluates an approach to the robust controller design for active trailer steering (ATS) systems to increase the safety of articulated vehicles. By applying a multi-objective evolutionary algorithm (MOEA) to the design optimization of the robust ATS controller, a series of optimal gain values can be obtained in a single run. This allows for posteriori decision making along with flexibility to select appropriate gain for different operating conditions. The algorithm creates Pareto optimal gain values for various speeds, thereby resulting in the robust ATS controller with an optimized gain scheduling scheme. The research elucidates the advantages of multi-objective algorithms over mono-objective or single-objective algorithms. For the design optimization of the ATS controller, a benchmark investigation is conducted to select an effective algorithm from the multi-objective algorithms, including GDE3, NSGA-II, NSGA-III, SPEA2 and MOPSO. A modular framework is introduced for co-simulations conducted in the CarSim-Simulink/Matlab environment, with which the vehicle and controller parameters can be optimized. The method ensures that a robust ATS controller with optimized feedback control gains, as well as satisfaction of design criteria and constraints. This research proposes a framework to generate a multi-dimensional look-up table using the multi-objective evolutionary algorithm for a general dynamic system controlled by a feedback controller. The optimized look-up system can be used to improve the robustness of control systems in real-world applications