Diving control of Autonomous Underwater Vehicle based on improved active disturbance rejection control approach

Shen, Yuxuan; Shao, Keyong; Ren, Wei; Liu, Yurong

doi:10.1016/j.neucom.2015.09.010

Cited by 58 publications

(19 citation statements)

References 34 publications

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“…TD means tracking differentiator. () It is proposed as an important part of the active disturbance rejection control theory by Han . For a given nonlinear input signal v ( t ), the TD dynamic part can output 2 signals, ie, x 1 ( t ) and x 2 ( t ).…”

Section: The Data‐driven Td Methods and Its Extensionmentioning

confidence: 99%

“…Equation (7) also needs to calculate the second derivative of 1 variable with respect to another variable, likėx 1 anḋx 1 u . The second derivativėx 1 oḟx 1 with respect to t can be calculated by using 2 TDs in series, as shown in Equation (17). Then, thėx 1 u can be calculated again using the same method in Section 3.2, as shown in Equation (17).…”

Section: The Second Derivativementioning

confidence: 99%

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A data‐driven approximate solution to the model‐free HJB equation

Huang

Zhang

et al. 2017

Optim Control Appl Methods

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Summary It is generally impossible to analytically solve the Hamilton‐Jacobi‐Bellman (HJB) equation of an optimal control system. With the coming of the big‐data era, this paper first derives a new data‐driven and model‐free Hamilton function for the HJB equation. Then, a data‐driven tracking differentiator method is proposed to solve the Hamilton function. Finally, the simulation for a classic example shows that the optimal control policy can be approximated with the proposed method. Thus, an online data‐driven model‐free approximate solution to the HJB equation is achieved. This method is only driven by the measured system states. All other variables and derivatives can be derived from the data‐driven model‐free Hamilton function and tracking differentiator. The method has a complete mathematical support and works like a controller. It does not need neural networks and has no training or iterative convergence problem. Thus, this paper adds an online data‐driven model‐free method to the existing literature on the approximate solution to the HJB equation.

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Section: The Data‐driven Td Methods and Its Extensionmentioning

confidence: 99%

Section: The Second Derivativementioning

confidence: 99%

A data‐driven approximate solution to the model‐free HJB equation

Huang

Zhang

et al. 2017

Optim Control Appl Methods

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“…On the other hand, in [11], the DOB method was applied to the PID control of an AUV based on the frequency analysis approach. In [12], a diving ADRC has been proposed to deal with the high nonlinearity, strong coupling and time-varying features in the AUV system.…”

Section: Introductionmentioning

confidence: 99%

“…In summary, on the one hand, fuzzy approaches [7] and intelligent algorithms such as PSO and AG (see [5], [6]), which are used to tune the PID control, can be useful to obtain good performance from the controller. On the other hand, the disturbance estimation made by ADRC can provide robustness to the PID, as seen in the experiments shown in references [1], [11] , [12]. However, based on the experimental results of these works, the robustness improvement of this methodology towards parameter uncertainties or external disturbances is not clear.…”

Section: Introductionmentioning

confidence: 99%

Saturation based nonlinear PID control for underwater vehicles: Design, stability analysis and experiments

et al. 2019

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During sea missions, underwater vehicles are often exposed to changes in the parameters of their control systems and subject to external disturbances due to the influences of ocean currents. These issues make the design of a robust controller quite a challenging task. This paper focuses on the design of a nonlinear PID controller, based on a set of saturation functions for trajectory tracking on an underwater vehicle. The main feature of the proposed control law is that it preserves the advantages of robust control and remains easy to fine-tune in real applications. Using the Lyapunov concept, we prove the asymptotic stability of the closed-loop tracking system. The effectiveness and robustness of our proposed controller for trajectory tracking in depth and yaw dynamics is demonstrated through real-time experiments.

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“…Liu et al investigated the pitch axis control of satellite camera based on a novel active disturbance rejection controller [4]. Shen et al tried to solve the control problem of Autonomous Underwater Vehicle (AUV) by designing an active disturbance rejection controller for diving [5].…”

Section: Introductionmentioning

confidence: 99%

Study on ADRC Parameter Optimization Using CPSO for Clamping Force Control System

Zhang

Armaou

et al. 2018

Mathematical Problems in Engineering

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Clamping force control system is essential for clamping tasks that require high precision. In this paper, Active Disturbance Rejection Controller (ADRC) is applied for clamping force control system, aiming to achieve higher control precision. Furthermore, the CPSO-ADRC system is proposed and implemented by optimizing the critical parameters of ordinary ADRC using chaos particle swarm optimization (CPSO) algorithm. To verify the effectiveness of CPSO-ADRC, Particle Swarm Optimization- (PSO-) ADRC is introduced as a comparison. The simulation results show that the CPSO-ADRC can effectively improve the control quality with faster dynamic response and better command tracking performance compared to ordinary ADRC and PSO-ADRC.

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Diving control of Autonomous Underwater Vehicle based on improved active disturbance rejection control approach

Cited by 58 publications

References 34 publications

A data‐driven approximate solution to the model‐free HJB equation

A data‐driven approximate solution to the model‐free HJB equation

Saturation based nonlinear PID control for underwater vehicles: Design, stability analysis and experiments

Study on ADRC Parameter Optimization Using CPSO for Clamping Force Control System

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