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.

The standard squirrel-cage induction machine has nearly reached its maximum efficiency. In order to further increase the energy efficiency of electrical machines, the use of permanent magnets in combination with the robust design and the line start capability of the induction machine is extensively investigated. Many experimental designs have been suggested in literature, but recently, these line-start permanent-magnet machines (LSPMMs) have become off-the-shelf products available in a power range up to 7.5 kW. The permanent magnet flux density is a function of the operating temperature. Consequently, the temperature will affect almost every electrical quantity of the machine, including current, torque, and efficiency. In this paper, the efficiency of an off-the-shelf 4-kW three-phase LSPMM is evaluated as a function of the temperature by both finite-element modeling and by practical measurements. In order to obtain stator, rotor, and permanent magnet temperatures, lumped thermal modeling is used.

Stepping motors are used in numerous applications because of their low manufacturing cost and simple open-loop position control capabilities. It is well known that their energy efficiency is low, although the actual efficiency values are generally not available. Moreover, the bulk of the stepping motors are driven in a non-optimal way, e.g., in an open loop with a maximum current to avoid step loss and, thus, with low efficiency. In this article, the impact of the control algorithm on the efficiency of the motor is analyzed, measured, and discussed. The basic open-loop full-, half-, and microstepping algorithms are considered together with a more advanced vector control algorithm. For each algorithm, the torque/current optimization is discussed.

Position-controlled systems driving repetitive tasks are of significant importance in industrial machinery. The electric actuators used in these systems are responsible for a large part of the global energy consumption, indicating that major savings can be made in this field. In this context, motion profile optimization is a very cost-effective solution as it allows for more energy-efficient machines without additional hardware investments or adaptions. In particular, mono-actuated mechanisms with position-dependent system properties have received considerable attention in literature. However, the current state-of-the-art methods often use unbounded design parameters to describe the motion profile. This both increases the computational complexity and hampers the search for a global optimum. In this paper, Chebyshev polynomials are used to describe the motion profile. Moreover, the exact bounds on the Chebyshev design parameters are derived. This both seriously reduces the computational complexity and limits the design space, allowing the application of a global optimizer such as the genetic algorithm. Experiments validate the added value of the chosen approach. In this study, it is found that the energy consumption can be reduced by 62.9% compared to a standard reference motion profile.