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Control systems for converter-controlled rail vehicles are orders of magnitude more complex than controllers for previous generations of vehicles. While the dynamic behavior of previous generations of vehicles was to a large extent determined by its power components alone, an important part of the dynamics of modern vehicles is shaped by real-time software, distributed computing and intercontroller communication. To ensure proper operation of the vehicle on track, an integration test of the vehicle control system is performed before initial roll-out. In order to achieve a maximum test depth and to minimize risk and cost, this test is achieved by connecting the original vehicle control system to a real-time dynamic vehicle simulator in closed-loop operation. The present paper describes concept, evaluation, and operation of a digital hardware-in-the-loop simulator for testing control-relevant parts of the vehicle. Particular emphasis is put on the hybrid nature of the underlying simulation problem and its inherent causality variations due to the combination of discrete switching effects, e.g., in diodes and controlled converters, with continuous system parts, e.g., differential equations for currents or mechanical system parts.

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Harmonic oscillations as integer multiples of the fundamental frequency in a power

Most industrial batch processes are operated through open-loop application of an off-line optimized input profile, such as feed or temperature. This is because modeling accuracy is typically poor (Juba and Hamer, 19861, and direct concentration measurements that would allow to cope with the frequently encountered lack of reproducibility are rare.However, when on-line measurement information gives access to the system state, on-line reoptimization promises considerable improvement. Since industrial on-line measurements typically do not immediately reveal perfect information on the entire system state, on-line state and parameter estimators need to be used. Their estimates often contain non-negligible uncertainty due to the system state being inferred from indirect, so-called model-based measurements, which can be subject to both stochastic measurement noise and structural measurement-model mismatch.Given such estimates, it is common practice to perform on-line optimization ignoring their uncertainty. Still, this is optimal theoretically only for ideal linear systems rather than possibly strongly nonlinear batch processes. Another approach is to begin with the open-loop optimal profile and switch to on-line optimization either empirically after a given number of measurements or when estimate uncertainty is sufficiently small. This leaves the problem of finding a good compromise between waiting for good estimates and reacting sufficiently early, as sensitivity of the final operation objective to input changes usually decreases rapidly.The present contribution suggests a cautious on-line correction mechanism that replaces the switching from open-loop to closed-loop operation with a smooth transition controlled by estimate uncertainty. Its single scalar tuning parameter represents the desired degree of cautiousness or boldness with which current estimates are used for on-line correction of open-loop optimized input profiles. In the limiting cases of no information (large uncertainty) and perfect information (certainty), the corrector naturally reduces to optimal openloop and optimal feedback operation, respectively.