Online identification of a two-mass system in frequency domain using a Kalman filter

Some of the most widely recognized online parameter estimation techniques used in different servomechanism are the extended Kalman filter (EKF) and recursive least squares (RLS) methods. Without loss of generality, these methods are based on a prior knowledge of the model structure of the system to be identified, and thus, they can be regarded as parametric identification methods. This paper proposes an on-line non-parametric frequency response identification routine that is based on a fixed-coefficient Kalman filter, which is configured to perform like a Fourier transform. The approach exploits the knowledge of the excitation signal by updating the Kalman filter gains with the known time-varying frequency of chirp signal. The experimental results demonstrate the effectiveness of the proposed online identification method to estimate a non-parametric model of the closed loop controlled servomechanism in a selected band of frequencies

Control System Commissioning of Fully Levitated Bearingless Machine

The bearingless permanent magnet synchronous motor (BPMSM) is a compact motor structure that combines the motoring and bearing functions based on well-designed integrated windings for generating both torque and magnetic suspension force. In order to achieve a successful high-performance control design for the BPMSM, an adequate model of the rotor dynamics is essential. This paper proposes simplified multiple-input and multiple-output (MIMO) control approaches, namely the pole placement and the linear-quadratic regulator (LQR), that allow to carry out identification experiments in full levitation. Additionally, the stability of the MIMO levitation controller is verified with the rotation tests. Compared with other recently published works, the novelty of this paper is to experimentally demonstrate that a stable fully levitated five-degrees-of-freedom (5-DOF) operation of a bearingless machine can be achieved by the proposed approach, and thereby, options for commissioning of such a system are obtained

Semantic-Functional Communications in Cyber-Physical Systems

This paper explores the use of semantic knowledge inherent in the cyber-physical system (CPS) under study in order to minimize the use of explicit communication, which refers to the use of physical radio resources to transmit potentially informative data. It is assumed that the acquired data have a function in the system, usually related to its state estimation, which may trigger control actions. We propose that a semantic-functional approach can leverage the semantic-enabled implicit communication while guaranteeing that the system maintains functionality under the required performance. We illustrate the potential of this proposal through simulations of a swarm of drones jointly performing remote sensing in a given area. Our numerical results demonstrate that the proposed method offers the best design option regarding the ability to accomplish a previously established task -- remote sensing in the addressed case -- while minimising the use of radio resources by controlling the trade-offs that jointly determine the CPS performance and its effectiveness in the use of resources. In this sense, we establish a fundamental relationship between energy, communication, and functionality considering a given end application

Comparison of Excitation Signals in Active Magnetic Bearing System Identification

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

Online Identification of a Mechanical System in the Frequency Domain with Short-Time DFT

A proper system identification method is of great importance in the process of acquiring an analytical model that adequately 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 for 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 flexible two-mass mechanical system with varying dynamics, and a particular attention is paid to detect the changes in the system dynamics. An online identification method is presented that is based on a recursive Kalman filter configured to perform like a discrete Fourier transform (DFT) at a selected set of frequencies. The experimental online identification results are compared with the corresponding values obtained from the offline-identified frequency responses. The results show an acceptable agreement and demonstrate the feasibility of the proposed identification method

Online tracking of varying inertia using a SDFT approach

The mechanical dynamics of modern machines very often depend on the angular position of the driven axis. To obtain optimal control, such applications typically require an advanced control structure such as an adaptive controller. Moreover, the variation in the dynamics like changing inertia, load torque, and viscous friction limits the performance and reduces the energy efficiency. Energy savings can be obtained by using so-called trajectory optimization techniques combined with feedforward control. However, both optimization and adaptive control require the knowledge of the position dependency of the mechanical parameters. In the case of reciprocating mechanisms, for instance, this position dependency is significant. Consequently, the mechanical parameters change rapidly at high operating speed of the machine. This paper thus contributes towards fast and accurate estimation of rapidly varying mechanical parameters. A sliding discrete Fourier transform (SDFT) approach is proposed to track the inertia variation of a reciprocating mechanism online. The feasibility is verified with experiments on an industrial pick and place unit. Both the results on the real machine and its CAD equivalent, modelled in a multibody dynamics software package, are considered. In addition, the developed inertia tracking algorithm is proven to be implementable in standard commercial drive components