Adaptive Tracking Method of Distorted Voltage Using IMM Algorithm under Grid Frequency Fluctuation Conditions

This paper newly proposes an interactive multiple model (IMM) algorithm to adaptively track distorted AC voltage with the grid frequency fluctuation. The usual tracking methods are Kalman filter (KF) algorithm with a fixed frequency and KF algorithm with frequency identifier. The KF algorithm with a fixed frequency has a larger covariance parameter to guarantee the tracking robustness. However, it has a large tracking error. The KF algorithm with frequency identifier overly depends on the accuracy and stability of frequency identifier. The advantage of the proposed method is that it is decoupled from frequency detection and does not depend on frequency detection accuracy. First, the orthogonal vector dynamic (OVD) tracking model of the sine wave is established. Then, a model set covering the grid frequency fluctuation range is formed, and a new OVD-IMM tracking algorithm is proposed in combination with IMM algorithm. In the end, the effectiveness and accuracy of the proposed OVD-IMM algorithm are verified through simulations and experiments.

A Model-Based FDD Approach for an EHA Using Updated Interactive Multiple Model SVSF

Ubiquitous applications, especially in harsh environments and with strict safety requirements, make Fault Detection and Diagnosis (FDD) in hydraulic actuators an imperative concern for the industry. Model-based FDD uses estimation strategies, including observers and filters as estimation tools. In these methods, observability is a limiting factor in information extraction and parameter estimation for most applications such as in fluid power systems. To address the observability problem, adaptive strategies like Interactive Multiple Model (IMM) estimation have proven to be effective. In this paper a computationally efficient form of IMM referred to as the Updated IMM (UIMM) is used and applied to an Electro-Hydrostatic Actuator (EHA) for FDD. The UIMM is suited to fault conditions that are irreversible, meaning that if a fault happens it will persist in the system. In essence the UIMM follows through a progression of models that in line with the progression of the fault condition in lieu of having all models being considered at the same time (as is the case for IMM). Hence, UIMM significantly reduces the number of models running in parallel and at the same time. This has two major advantages which are higher computational efficiency and avoiding combinatorial explosion. The state and parameters estimation strategies that is used in conjunction with UIMM is the Variable Boundary Layer Smooth Variable Structure Filter (VBL-SVSF). The VBL SVSF is a robust optimal estimation strategy that is more stable than the Kalman Filter in relation to system and modeling uncertainties. The UIMM method is validated by simulation of fault conditions on an EHA.

Adaptive estimations of tyre–road friction coefficient and body’s sideslip angle based on strong tracking and interactive multiple model theories

Vehicle dynamic states and parameters, such as the tyre–road friction coefficient and body’s sideslip angle especially, are crucial for vehicle dynamics control with close-loop feedback laws. Autonomous vehicles also have strict demands on real-time knowledge of those information to make reliable decisions. With consideration of the cost saving, some estimation methods employing high-resolution vision and position devices are not for the production vehicles. Meanwhile, the bad adaptability of traditional Kalman filters to variable system structure restricts their practical applications. This paper introduces a cost-efficient estimation scheme using on-board sensors. Improved Strong Tracking Unscented Kalman filter is constructed to estimate the friction coefficient with fast convergence rate on time-variant road surfaces. On the basis of previous step, an estimator based on interactive multiple model is built to tolerant biased noise covariance matrices and observe body’s sideslip angle. After the vehicle modelling errors are considered, a Self-Correction Data Fusion algorithm is developed to integrate results of the estimator and direct integral method with error correction theory. Some simulations and experiments are also implemented, and their results verify the high accuracy and good robustness of the cooperative estimation scheme.