Drive control is an important task of the Daimler-Benz subsidiary company AEG Daimler-Benz Industrie. In order to increase the performance of modern drive systems it is advantageous to exploit nonlinear control techniques, e.g. neurocontrol. Within the nonlinear control framework bounds on control, state and output variables can be taken into account. Different control objectives may also be pursued in different operating regions. This is especially important if safety requirements must be met. Furthermore, nonlinear dependencies such as friction, hysteresis or saturation can be included in the mathematical-physical modelling process and control scheme, if known. On the other hand these effects are very often neglected in drive control in order to design linear controllers, but this often results in poor control. If the mathematical structure (equations) representing a process is not known, very difficult to obtain or just too time-consuming to evaluate, learning systems may be engaged to improve the modelling process.

Inspec keywords: control system synthesis; real-time systems; neural nets; nonlinear control systems

Other keywords: output variables; linear controller design; nonlinear control techniques; state variables; real-time drive control; neural networks; neurocontrol; mathematical physical modelling process

Subjects: Nonlinear control systems