Adaptive Anti-noise Least-Squares Algorithm for Parameter Identification of Unmanned Marine Vehicles: Theory, Simulation, and Experiment

Zhong, Yiming; Yu, Caoyang; Wang, Rui; Pei, Tianqi; Lian, Lian

doi:10.1007/s40815-022-01424-7

Cited by 9 publications

(2 citation statements)

References 31 publications

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“…To prevent the randomness of result brought about by the initial parameters in network, for each structure we run 20 trials with different initial weights, and the best-preforming one on the validation set is chosen and presented below. Hammerstein is a non-linear identifier with a linear dynamic and static nonlinearity cascading together, and extended least square [33,34] can be used for parameter estimation. Based on the performance on the validation set, the final model structure for above models is summarized in Table 2.…”

Section: Case Studymentioning

confidence: 99%

Dynamic Modeling of Flue Gas Desulfurization Process via Bivariate EMD-Based Temporal Convolutional Network

Liu

Wang

2023

Applied Sciences

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Sulfur dioxide (SO2) can cause detrimental impacts on the ecosystem. It is well known that coal-fired power plants play a dominant role in SO2 emissions, and consequently industrial flue gas desulfurization (IFGD) systems are widely used in coal-fired power plants. To remove SO2 effectively such that ultra-low emission standard can be satisfied, IFGD modeling has become urgently necessary. IFGD is a chemical process with long-term dependencies between time steps, and it typically exhibits strong non-linear behavior. Furthermore, the process is rendered non-stationary due to frequent changes in boiler loads. The above-mentioned properties make IFGD process modeling a truly formidable problem, since the chosen model should have the capability of learning long-term dependencies, non-linear dynamics and non-stationary processes simultaneously. Previous research in this area fails to take all the above points into account at a time, and this calls for a novel modeling approach so that satisfactory modeling performance can be achieved. In this work, a novel bivariate empirical mode decomposition (BEMD)-based temporal convolutional network (TCN) approach is proposed. In our approach, BEMD is employed to generate relatively stationary processes, while TCN, which possesses long-term memory ability and uses dilated causal convolutions, serves to model each subprocess. Our method was validated using the operating data from the desulfurization system of a coal-fired power station in China. Simulation results show that our approach yields desirable performance, which demonstrates its effectiveness in the IFGD dynamic modeling problem.

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Section: Case Studymentioning

confidence: 99%

Dynamic Modeling of Flue Gas Desulfurization Process via Bivariate EMD-Based Temporal Convolutional Network

Liu

Wang

2023

Applied Sciences

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“…It is well known that many classical algorithms, such as the least square (LS) method, have always been employed in system identification [17,18]. Caccia et al [19] used the LS method to estimate the model parameters for unmanned underwater vehicles through the onboard sensor data.…”

Section: Introductionmentioning

confidence: 99%

Advanced Marine Craft Model Identification via Multi-Kernel Weighted Least Square Support Vector Machine and Characteristic Model Techniques

Pei

Zhong

et al. 2023

JMSE

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This paper combines the piecewise Cubic Hermite (CH) interpolation algorithm and the weighted least square support vector machine (WLS-SVM) to improve identification accuracy for marine crafts built based on the characteristic model. The characteristic model is first used to describe the heading dynamics of marine crafts and is a superior model to the traditional response model in both accuracy and complexity. Especially in order to improve identification accuracy, a CH-based data preprocessing strategy is utilized to densify and smooth data for further accurate identification. Subsequently, the combination of the linear kernel function and the Gaussian kernel function is introduced in the conventional WLS-SVM method, which renders global and local performance improvements compared with the conventional WLS-SVM method. Finally, informative maneuvers composed of Zigzag and Sine are carried out to test the performance of the improved identification method. Compared to the conventional LS-SVM method based on the response model, the root mean square error of the proposed CH-MK-WLS-SVM method based on the characteristic model is reduced by an order of magnitude in the presence of sensor noise.

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Dynamics identification‐driven diving control for unmanned underwater vehicles

Zhong,

Yu,

Xiang

et al. 2024

Journal of Field Robotics

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This paper presents a comprehensive study of dynamics identification‐driven diving control for unmanned underwater vehicles (UUVs). Initially, a diving dynamics model of UUVs is established, serving as the foundation for the development of subsequent algorithms. A noise‐reduction least squares (NRLS) algorithm is then derived for parameter identification, demonstrating convergence under measurement noise from a probabilistic perspective. A notable feature of this algorithm is its skill in correcting raw data, thereby improving parameter identification accuracy. Based on the identified model, an improved fast terminal sliding mode control (FTSMC) algorithm is introduced for diving control, consistently ensuring rapid convergence under 16 scenarios. Importantly, the proposed diving control algorithm effectively mitigates chattering by incorporating a dedicated filter, adaptively adjusting the switching gain, and substituting saturation function for sign function. Through experimental validation, the NRLS algorithm's advantage over the traditional least squares method becomes evident, with depth errors consistently below 3.5 cm. This indicates that the identified model closely aligns with the actual model, showcasing a commendable fit. Additionally, when compared to the traditional sliding mode controller and the proportional‐integral‐derivative algorithm, the FTSMC algorithm has superior performance, as indicated by a mean absolute percentage error consistently below 4%.

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Adaptive Anti-noise Least-Squares Algorithm for Parameter Identification of Unmanned Marine Vehicles: Theory, Simulation, and Experiment

Cited by 9 publications

References 31 publications

Dynamic Modeling of Flue Gas Desulfurization Process via Bivariate EMD-Based Temporal Convolutional Network

Dynamic Modeling of Flue Gas Desulfurization Process via Bivariate EMD-Based Temporal Convolutional Network

Advanced Marine Craft Model Identification via Multi-Kernel Weighted Least Square Support Vector Machine and Characteristic Model Techniques

Dynamics identification‐driven diving control for unmanned underwater vehicles

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