A proper real-time system identification method is of great importance in order to acquire an analytical model that sufficiently represents the characteristics of the monitored system. While the use of different time-domain online identification techniques has been widely recognized as a powerful approach to system diagnostics, the frequency-domain identification techniques have primarily been considered for offline commissioning purposes. This paper addresses issues in the online frequency-domain identification of a mechanical system with varying dynamics; particular attention is paid to detect the changes in the system dynamics. A closed-loop online identification method is presented that is based on a sliding discrete Fourier transform at a selected set of frequencies. The method is experimentally validated by a closed-loop controlled servomechanism with a limited stroke and time-varying parameters.

Active magnetic bearings (AMBs) offer frictionless suspension, vibration insulation, programmable stiffness, and damping, among other advantages, in levitated rotor applications. However, AMBs are inherently unstable and require accurate system models for the high-performance model-based multi-input multi-output control of rotor position. Control electronics with high calculation capacity and accurate sensors of AMBs provide an opportunity to implement various identification schemes. A variety of artificial excitation signal-based identification methods can thus be achieved with no additional hardware. In this paper, a selection of excitation signals, namely the pseudorandom binary sequence (PRBS), chirp signal, multisine, and stepped sine are presented, applied, and compared with the AMB system identification. From the identification experiments, the rotor-bearing system, the inner current control loop, and values of position and current stiffness are identified. Unlike recently published works considering excitation-based identification of AMB rotor systems, it is demonstrated that identification of the rotor system dynamics can be carried out using various well-established excitation signals. Application and feasibility of these excitation signals in AMB rotor systems are analyzed based on experimental results

Driveline resonances are one of the major reasons for poor vehicle drivability, passenger discomfort, and premature wear of the mechanical parts of the driveline. Therefore, adequate models generated by system identification are essential in order to analyze resonances, and especially to design solutions to mitigate these adverse effects by means of control. In this paper, the resonating mechanical driveline of a hybrid city bus is identified by feeding different excitation signals, such as a pseudo-random binary signal, stepped sine, and chirp to the driveline, and by measuring the frequency response at a standstill and during on-road experiments. The identification experiments are conducted by operating the bus in the series electric hybrid mode, that is, in an operating mode that corresponds to a full electric mode powered by energy storages. Resonating two-mass system models are parametrized based on the obtained frequency responses and used to validate the identification experiments.

Tower cranes are needed to move heavy objects safely around construction sites. In tower cranes, payload oscillations are a typical problem that can cause safety issues, especially if the crane is not operated by an experienced user. Depending on the system, there are different causes for oscillations, e.g., inertial forces from the crane movement or external forces, such as weather conditions. Hence, the selected control law for input tracking plays an important role to limit the oscillatory motion and to help the crane operator to prevent unwanted operations. In this paper, we study the slew control of a tower crane application from the viewpoint of reducing payload oscillations. Two different approaches are studied: open-loop control based on extrainsensitive input shaping and closed-loop swing angle control, based on the estimation of the hoist cable angle. The proposed control approaches are validated by running the developed control program against a multibody mechanics simulator containing a model of a Liebherr tower crane. The studied control laws are also evaluated using an experimental setup, which consists of a two-axis manipulator, inverters, and a programmable logic controller in which the studied control methods are implemented. The results from the multibody dynamics simulations and from the experimental setup are presented and evaluated from the viewpoint of crane operation.