Bias compensation‐based parameter and state estimation for a class of time‐delay non‐linear state‐space models

Gu, Yu; Zhu, Quanmin; Nouri, Hassan

doi:10.1049/iet-cta.2020.0104

Cited by 41 publications

(30 citation statements)

References 63 publications

(63 reference statements)

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“…We will also replace the MLP with other fusion methods to reduce the network model’s parameters for the fusion results. The proposed approaches in the paper can combine other parameter estimation algorithms [ 32 , 64 , 65 , 66 , 67 ] to study the parameter identification problems of linear and nonlinear systems with different disturbances [ 68 , 69 , 70 , 71 , 72 ], and to build the soft sensor models and prediction models and can be applied to other fields [ 73 , 74 , 75 , 76 , 77 ] such as signal processing and process control systems.…”

Section: Discussionmentioning

confidence: 99%

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

Jin

et al. 2021

Entropy

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Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement’s causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network’s over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system’s big measurement data to improve prediction performance.

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Section: Discussionmentioning

confidence: 99%

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

Jin

et al. 2021

Entropy

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“…The simultaneous update of the state and model parameters may also make the system parameter optimization process unable to converge. These methods can combine identification approaches [ 71 , 72 , 73 , 74 , 75 ] for studying the modeling of dynamic time series and stochastic systems with colored noises [ 76 , 77 , 78 , 79 , 80 ] and can be applied to other fields such as signal modeling, tracking and control systems [ 81 , 82 , 83 ].…”

Section: State Estimation Based On a Blurry Modelmentioning

confidence: 99%

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Jin

Jeremiah

et al. 2021

Sensors

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State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

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“…3) Compute the gain vector L s (t) and the covariance matrix P s (t) by (45) and (46). Compute the gain vector L n (t) and the covariance matrix P n (t) by (47) and (48). 4) Update the parameter estimatesθ s (t) andθ n (t) by (43) and (44), respectively.…”

Section: Algorithmmentioning

confidence: 99%

A Wave Peak Frequency Tracking Method Based on Two-Stage Recursive Extended Least Squares Identification Algorithm

Yuan

Pan

et al. 2021

IEEE Access

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This paper proposes the wave peak frequency tracking methods based on the least squares identification algorithm. The wave disturbance model is transformed into an autoregressive moving average (ARMA) model and a recursive extended least squares (RELS) algorithm is derived to identify the model parameters by using the auxiliary model identification idea. Furthermore, a two-stage recursive extended least squares (2S-RELS) algorithm is presented to improve the convergence speed by using the hierarchical identification principle. A ship heading control system with the wave peak frequency tracker is built to evaluate the effectiveness of the proposed algorithms. Finally, simulation results show that the proposed algorithms can estimate the wave peak frequency accurately and the 2S-RELS algorithm can improve the convergence speed effectively.

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Bias compensation‐based parameter and state estimation for a class of time‐delay non‐linear state‐space models

Cited by 41 publications

References 63 publications

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

A Wave Peak Frequency Tracking Method Based on Two-Stage Recursive Extended Least Squares Identification Algorithm

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