SUMMARYThis paper presents a generalized formulation for multi-input multi-output (MIMO) quantitative feedback theory (QFT) based controller design and analysis, and its application to the control of the X-29 aircraft. The formulation is based on a more general control structure, where input and output transfer function matrices are included to provide additional degrees of freedom in the decentralized MIMO QFT feedback structure, that facilitates the exploitation of directions in MIMO QFT designs. The formulation captures existing design approaches for fully populated MIMO QFT controller design and provides for a directional design logic involving the plant and controller alignment and the directional properties of their multivariable poles and zeros. Horowitz's Singular-G design methodology is placed in the context of the generalized formulation and the Singular-G design for the X-29 is analysed and redesigned using nonsequential MIMO QFT and the formulation, demonstrating its utility. The results highlight a fundamental trade-off between multivariable controller directions for stability and performance in classically formulated design methodologies, elucidate the properties of Singular-G designed controllers for the X-29 and validate recent contributions to the theory for non-sequential MIMO QFT.
This paper presents a model-based scheme for permanent magnet synchronous motor (PMSM) driving transmission fault detection and identification (FDI) in a steady-state condition. The proposed framework utilizes a PMSM state-space model and an approximated transmission model to construct the regression models for parameter estimation using the Recursive Least-Square (RLS) algorithm. The FDI are accomplished by the residual current spectrum thresholding method to assess the fault characteristic frequency magnitude and also by parameter clustering. Two types of mechanical transmission with three different fault conditions are tested in the experiments. As a preliminary effort in the condition monitoring of PMSM driving transmission, the study results demonstrate a promising approach by considering both residual current spectrum and parameter cluster, which achieved a satisfactory decision making in detecting and identifying the faulty condition.
A novel concept of wind farm fault detection by monitoring the wind speed in the wake region is proposed in this study. A wind energy dissipation model was coupled with a computational fluid dynamics solver to simulate the fluid field of a wind turbine array, and the wind velocity and direction in the simulation were exported for identifying wind turbine faults. The 3D steady Navier–Stokes equations were solved by using the cell center finite volume method with a second order upwind scheme and a k−ε turbulence model. In addition, the wind energy dissipation model, derived from energy balance and Betz’s law, was added to the Navier–Stokes equations’ source term. The simulation results indicate that the wind speed distribution in the wake region contains significant information regarding multiple wind turbine faults. A feature selection algorithm specifically designed for the analysis of wind flow was proposed to reduce the number of features. This algorithm proved to have better performance than fuzzy entropy measures and recursive feature elimination methods under a limited number of features. As a result, faults in the wind turbine array could be detected and identified by machine learning algorithms.
Multi-rotor vehicles have demonstrated great potential in many applications, from goods delivery to military service. Its simple structure has drawn much attention in control research, with various feedback control design methods being tested on it. In this study, a quadrotor is constructed with National Instrument (NI) myRIO for its flight controller. Linear controllers are synthesized with a simple model and with a more detailed model, which also considers the actuator dynamic and system time delay, to exploit the limitations and trade-offs posted by hardware and its influence on linear control design and implementation. The simulation results match the real response better if the detailed model is used in the control design. In addition, the linear–quadratic–Gaussian (LQG) control provides the best response in the control design in this study. The constraints posted by the actuator and time delay are clearly observed in the control synthesis process and the experiment result. These constraints also led to poor control performance or even instability if not considered in the control implementation in advance.
A novel framework of model-based fault detection and identification (MFDI) for induction motor (IM)-driven rotating machinery (RM) is proposed in this study. A data-driven subspace identification (SID) algorithm is employed to obtain the IM state-space model from the voltage and current signals in a quasi-steady-state condition. This study aims to improve the frequency–domain fault detection and identification (FDI) by replacing the current signal with a residual signal where a thresholding method is applied to the residual signal. Through the residual spectrum and threshold comparison, a binary decision is made to find fault signatures in the spectrum. The statistical Q-function is used to generate the fault frequency band to distinguish between the fault signature and the noise signature. The experiment in this study is performed on a wastewater pump in an existing industrial facility to verify the proposed FDI. Two faulty conditions with mathematically known and mathematically unknown faulty signatures are experimented with and diagnosed. The study results present that the residual spectrum demonstrated to be more sensitive to fault signatures compare to the current spectrum. The proposed FDI has successfully shown to identify the fault signatures even for the mathematically unknown faulty signatures.
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