A self-searching optimal ADRC for the pitch angle control of an underwater thermal glider in the vertical plane motion

Huang, Zhijian; Liu, Yanji; Zheng, Huan; Wang, Shengtang; Ma, Ji; Liu, Yihua

doi:10.1016/j.oceaneng.2018.04.010

Cited by 40 publications

(28 citation statements)

References 38 publications

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“…Furthermore, it is multiplied by the total deceleration to become the rudder deflection radians, which needs to be converted to an angle, finally. The friction torque equation and the hinge torque equation are as shown in Equations (14) and (15). The driver's transfer function is Equation 16.…”

Section: Adrc Control Model For the Emamentioning

confidence: 99%

“…Additionally, the ADRC has been widely used in steering the controller such as automatic vehicles, the AHV model-based autopilot controller, spacecraft's attitude and position synchronization control and so on [12][13][14][15][16][17]. There was no doubting that the application on the ADRC extended to the field of the controlling missile or guided artillery rocket's EMA.…”

Section: Introductionmentioning

confidence: 99%

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Research on Active Disturbance Rejection Control Technology of Electromechanical Actuators

et al. 2018

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It has been proven that the properties of non-linearity, multi-disturbance, and time-variation are the main factors affecting the performance of the electromechanical actuator (EMA) for guided artillery rockets. In particular, its controller should have good dynamic characteristics and strong anti-disturbance features when the projectile is maneuvered at the end of its guidance. For these reasons, an active disturbance rejection controller (ADRC) was designed for a guided artillery rocket’s EMA with a ball screw drive. Compared to other control methods, it has been shown that the ADRC had a stronger disturbance rejection ability than PID, Fuzzy-PID, and BP-PID under the condition of 20% maximum control torque disturbance, and it also had a large stability margin and bandwidth.

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Section: Adrc Control Model For the Emamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Active Disturbance Rejection Control Technology of Electromechanical Actuators

et al. 2018

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“…Hyunsoon Kim et al studied the optimal operation of a thermal‐recharging float in the ocean . Zhijian Huang et al developed a self‐searching optimal ADRC for the pitch Angle control for underwater thermal glider . Guohui Wang et al provided efficiency analysis of the OTEC with PCM for underwater vehicle, and a sea trial was arranged to verify the design principles of his underwater thermal vehicle …”

Section: Introductionmentioning

confidence: 99%

“…17 Zhijian Huang et al developed a self-searching optimal ADRC for the pitch Angle control for underwater thermal glider. 18 Guohui Wang et al provided efficiency analysis of the OTEC with PCM for underwater vehicle, and a sea trial was arranged to verify the design principles of his underwater thermal vehicle. 19 However, there are still several inadequacies in the articles, 13,14,16 needing further study.…”

Section: Introductionmentioning

confidence: 99%

Ocean thermal energy utilization process in underwater vehicles: Modelling, temperature boundary analysis, and sea trail

et al. 2020

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Summary Autonomous underwater vehicles (AUVs) play an important role in the ocean observation and marine scientific research. The utilization of marine environmental energy is a desirable way to enhance the sailing range and endurance of AUVs. In this paper, a detailed model with four sub‐models is established to describe the ocean thermal energy utilization process in AUVs, where the factors of system pressure, material's thermal‐pressure‐physical properties and grid nodes are considered. Based on the model, performance of the utilization system under constant and variable temperature boundaries is analyzed. At last, an AUV driven by ocean thermal energy is deployed in South China Sea to test the utilization performance, and the model is validated by the practical trail data. It is proved that thermal‐driven AUV saves about 50% energy of buoyancy‐driven process in sea trail. The simulation results are in good agreement with the trial data and prove the correctness and effectiveness of the model. The utilization system shows a better performance in the temperature condition with a higher surface temperature and a greater gradient of thermocline. When reaching the designed depth, AUVs can obtain plenty of time for phase change. The conclusions drawn in the paper provide some guidance for development of the thermal‐driven AUV.

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“…However, MFAC can only inhibit the unknown disturbance within a definite range. When the disturbance exceeds a certain range, MFAC cannot implement effective control [10]. To deal with this problem, ADRC is introduced to MFAC.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control

Yin

et al. 2019

Electronics

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The control issue of Autonomous Underwater Vehicles (AUV) is very challenging since the precise mathematical model of AUV is hard to establish due to its strong coupling and time-varying features. Meanwhile, AUV movement is easily interfered with by ocean currents and waves, causing anti-interference performance of traditional Proportional-Integral-Derivative (PID) control to be unsatisfactory. Aiming to solve those problems, an algorithm of fuzzy optimized model-free adaptive control (MFAC) based on auto-disturbance rejection control (ADRC) was proposed and used in AUV heading control. The MFAC is used to overcome the difficulty with establishing a precise mathematical model, and the ADRC is introduced to handle the interference of currents and waves. In this paper, MFAC and ADRC are combined. First, the MFAC is performed based only on the I/O data of the controlled object, which is simple to implement with low calculation complexity and strong robustness. Then, a tracking differentiator (TD) is employed to track the input signal to overcome the antinomy of rapidity and hypertonicity in MFAC. After that, an extended-state observer (ESO) is added to control the variables of MFAC to estimate all the disturbances, which can greatly improve the anti-interference ability of the system. Due to the complexity and diversity of the marine environment, a fuzzy optimized MFAC based on ADRC is proposed to improve the adaptability of AUV to the marine environment. Simulations and experiments were carried out to verify the control effect of this algorithm in complex sea conditions.

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A self-searching optimal ADRC for the pitch angle control of an underwater thermal glider in the vertical plane motion

Cited by 40 publications

References 38 publications

Research on Active Disturbance Rejection Control Technology of Electromechanical Actuators

Research on Active Disturbance Rejection Control Technology of Electromechanical Actuators

Ocean thermal energy utilization process in underwater vehicles: Modelling, temperature boundary analysis, and sea trail

Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control

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