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By offering good ride safety and ride comfort to passenger cars, active suspensions have attracted more and more attentions of investigators. Numerous approaches of designing controller for active suspension systems have been introduced mostly restricted to linear time-invariant systems. In this dissertation, optimization methods are applied to a special three degree-of-freedom spatial car model with active suspensions to define an optimal controller. The optimal control law with state-feedback and disturbance-feed forward parts is derived by extending the linear-quadratic regulator (LQR) control to linear systems with measurable disturbances and combining it with a multi-criterion optimization (MCO) procedure. This allows to reduce the number of design variables of the MCO problem significantly. The approach is applied also to gain-scheduling control of the linear-parameter varying spatial car model. The effectiveness of the designed controller with respect to ride safety and ride comfort of the car in yaw motion is demonstrated through the simulation of double-lane-change maneuvers, where the paths are found by an optimization procedure.
Nowadays industrial aerodynamic compressor design is based on mature computer programs developed during several decades. State of the art is to split the complex design process into subsequent design subtasks which are solved by different experts via time-consuming parameter studies. Isolated design of subproblems based on human intuition, however, will result in sub-optimal solutions only. Due to the increasing demand on higher aero engine performance and design cycle time reduction the aspects of process integration and automation as well as numerical optimization become more and more important in today’s aerodynamic compressor design. The intention of this work is to show how process integration and optimization can be used efficiently to support engineering design work in optimal solution finding. Since the aerodynamic compressor design is characterized by many design parameters, multiple constraints and contradicting objectives, multi-objective optimization is used to find Pareto-optimal solutions from which the design engineer can choose trade-offs for his particular design problem. The improvements in terms of process acceleration and design optimization are demonstrated for three selected, but typical industrial engineering design tasks required in three different design phases of the aerodynamic compressor design process, namely preliminary design, throughflow off-design, and blading procedure.