Abstract: For the determination of a non-linear controller for a non-linear system it is proposed that a parameter set (KPID(k)) of the controller (1) is determined by means of an optimization using a multi-criteria evolutionary algorithm, in which algorithm a plurality of parameter sets (KPID(k)), which each represent a possible solution of the optimization, are determined in each evolution step and at least two quality values (fi) are determined for each parameter set (KPID(k)) and the quality values (fi) are optimized by the multi-criteria evolutionary algorithm.

Abstract: A method for obtaining data from a nonlinear dynamic real system during a test run, for instance an internal combustion engine, a drivetrain, or parts thereof, a sequence of dynamic excitation signals is generated according to an initial design and a system output is measured. To enable the quick and precise generation of experimental designs for global measurement, modeling, and optimization of a nonlinear dynamic real system, a sequence of dynamic excitation signals was generated by generating a design with a sequence of excitation signals, obtaining output data by feeding said sequence into a model for the real system, determining a criterion for the information content of the complete design of experiment sequence, varying the sequence, obtaining new output data by feeding said modified sequence into the model, determining again said criterion, iterating until said criterion improves, and using the improved sequence of excitation signals for the real system.

Abstract: For the determination of a non-linear controller for a non-linear system it is proposed that a parameter set (KPID(k)) of the controller (1) is determined by means of an optimization using a multi-criteria evolutionary algorithm, in which algorithm a plurality of parameter sets (KPIS(k)), which each represent a possible solution of the optimization, are determined in each evolution step and at least two quality values (fi) are determined for each parameter set (KPID(k)) and the quality values (fi) are optimized by the multi-criteria evolutionary algorithm.

Abstract: In a machine-implemented method for obtaining data from a nonlinear dynamic real system during a test run, for instance an internal combustion engine, a drive-train or parts thereof, of a sequence of dynamic excitation signals for at least one measurement channel is generated according to a previously generated design of experiment for said test run and the system output of at least one output channel is measured.

Abstract: In connection with generating a global model of output variables of a nonlinear dynamic real system, for example, of an internal combustion engine, a drive train, or a subsystem thereof that covers the entire space of all operating points of the system, a measurement of the system is performed for a subset of variation points that are defined by a set of parameters of the system. In order to provide rapid and precise generation of the experimental designs, and the global optimization thereof, while taking into account the test constraints and additional criteria, at least two subsets selected as a function of each other are determined in succession, a common experimental design is generated taking into account the variation points of all subsets, and the system is measured based on this experimental design.