De-Xin Gao’s research while affiliated with Qingdao University of Science and Technology and other places

... Han et al. [6] proposed a fuzzy proportional-integral differential (PID) control strategy based on road surface estimation to meet the different comfort and safety requirements of active suspension under different road conditions performance requirements. Liang et al. [7] designed a vibration controller for optimal control to extend the working life of the suspension system and improve ride comfort. Wang et al. [8] designed an active immunity controller to improve driver comfort by considering suspension nonlinearity and actuator saturation combined with self-immunity control and fuzzy sliding mode control. ...

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Active suspension control strategy for vehicles based on road surface recognition

... Moreover, accident may become worse when involving external disturbances from environment such as unstructured and slippery terrain. In previous works, many different control system design approaches have been proposed to deal with uncertainty and disturbance, including computed torque control [16], adaptive control [17], pid control [18], sliding mode control [19], optimal control [20], or different control systems [21]. In general, they've employed SMC to RSV system. ...

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Robust super-twisting sliding mode controller for the lateral and longitudinal dynamics of rack steering vehicle

... J. Kim proposed an integral sliding mode controller to enhance the time-delay performance for AUV position control, given that the feedback frequency of the DVL is relatively lower than other sensors [3]. D. Gao combined sliding mode control and linear quadratic regulator, which makes the AUV robust to parameter perturbations and random disturbances [4]. In [5], H. Ban formulated the AUV tracking problem into the T-S fuzzy framework, eliminating the position and heading error with nonlinearity and model inaccuracy. ...

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Trajectory Tracking Controller Design for Underactuated AUVs based on Adaptive Robust Control

... Especially, Li et al. (2021) utilized Random Forest (RF) classifier to implement abnormal detection. However, existing studies on charging-pile fault diagnosis focus on the mechanical log data or sensor data streams (Gao et al. 2020(Gao et al. , 2018Wang et al. 2021;Yong and Ji 1650), while we concentrate on work order fault description data recorded by staff (different from mechanical log data and sensor data streams) and classify 8 types of faults, including installation error fault, charging-pile mechanical fault, charging-pile program fault, user personal fault, signal fault (offline), pile compatibility fault, charging platform fault, and other faults. Figure 1 presents a simplified workflow of our paper. ...

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Exploring best-matched embedding model and classifier for charging-pile fault diagnosis

... The traditional approach to designing an autopilot for the near-surface environment is to achieve a nominal trim condition for a given speed using water tanks, then to rely on the robustness of an autopilot to compensate both for variations in trim and for the effect of waves. Examples of attempts to address the wave rejection problem alone include the work in [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12]. All of these examples utilize models for synthesis and simulation that feature less fidelity than the LNMS model described herein. ...

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A Maneuvering Model for an Underwater Vehicle Near a Free Surface—Part III: Simulation and Control Under Waves

... While model-based methods offer physical interpretability and accuracy, they come with higher adaptation costs and development complexity. In contrast, data-driven methods such as machine learning [9], deep learning [10], and data analysis [11] can automatically learn fault patterns from historical data, reducing reliance on complex physical models and proving more suitable for real-world applications. With the successful application of deep learning in image recognition, speech recognition, and semantic analysis [12,13], its potential in intelligent fault diagnosis has garnered widespread attention. ...

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Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks

... Faried Hasbullah and Waleed F. Faris [3] proposed an active disturbance rejection control with input decoupling transformation for a half car model. Yan-Jun Liang et al. [4] designed an optimal vibration controller for vehicle active suspension systems. A. Pati et al. [5] designed a controller for a half car suspension system based on sliding mode control using proportional-integral-derivative (PID) sliding surface. ...

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Improving the Dynamic Response of Half-Car Model Using Modified PID Controller

... The near-surface shallow water disturbances are given in the following equation [61][62][63]: ...

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Disturbance rejection controller design based on adaptive nonlinear FOPID controller and chaotic WOA with a neuro-fuzzy approximation for URV robot

... Model identification and adaptive control design are performed in [8]. An optimal internal model control algorithm is presented in [9] for heading control problem of AUV systems affected by wave disturbances. In [10], motion control problems are researched based on active disturbance rejection control (ADRC) method. ...

Reference:

Design and Implementation of a Novel Hybrid Driven Underwater Vehicle

... For the CSTR control example, various control strategies, such as the exact feedback linearization control [11,12], the nonlinear backstepping control [2], the model predictive control [4,[13][14][15][16][17][18][19], different optimal control strategies [20][21][22][23], the adaptive control approaches [24][25][26][27], and the sliding mode control theory [1,[28][29][30][31][32] have been proposed among others. We can also ind several articles based on successful combinations between advanced nonlinear control theories and soft computing tools such as artiicial neural networks (ANN) [33,34], fuzzy inference systems (FIS) [3,35], and many bio-inspired optimization algorithms such as the genetic algorithm (GA) [7,36], etc. ...

Reference:

Extended State Observer Based Robust Feedback Linearization Control Applied to an Industrial CSTR