Adaptive fuzzy output feedback stabilization control for the underactuated surface vessel

Lin, Xin; Nie, Jun; Jiao, Yuzhao; Liang, Kun; Li, Heng

doi:10.1016/j.apor.2018.01.015

Cited by 44 publications

(23 citation statements)

References 17 publications

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“…Consider an under-actuated dynamical system of the form (3). If control law adopted is as given in (22) with the sliding surfaces as designed in Eqs. (5), (9), and (13), then the first-level sliding surfaces are asymptotically stable.…”

Section: Theorem 52 (Asymptotic Stability Of the First-level Surfaces)mentioning

confidence: 99%

See 1 more Smart Citation

Robust hierarchical sliding mode control with state-dependent switching gain for stabilization of rotary inverted pendulum

Idrees¹,

Muhammad²,

Ullah³

2019

Kybernetika

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Section: Theorem 52 (Asymptotic Stability Of the First-level Surfaces)mentioning

confidence: 99%

“…Based on SMC, different new techniques are designed to address nonlinear control problems [36,41,48,53]. It is widely used to design controllers for robotics [33,37,54], under-actuated cranes [21,27,42,43], quadrotors for unmanned aerial vehicles (UAV) [1], and under-actuated vessels [22].…”

Section: Introductionmentioning

confidence: 99%

Robust hierarchical sliding mode control with state-dependent switching gain for stabilization of rotary inverted pendulum

Idrees¹,

Muhammad²,

Ullah³

2019

Kybernetika

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show abstract

“…Joo [7] considered a controller composed of a linear-quadratic regulator (LQR) controller to maintain zigzag vertical movement for gliding and towing (proportional plus derivative) PD controllers to control elevator/rudder angles. Lin et al [8] designed an adaptive fuzzy stabilization controller for an underactuated surface vessel in the presence of unknown time-varying environmental disturbances and input saturation. Makavita et al [9] reported the results of an experimental study conducted to compare the performances of different adaptive control methods for the depth control of a UUV, which represents a significant challenge compared with heading control due to increased noise, time delay, and thrust requirements.…”

Section: Introductionmentioning

confidence: 99%

“…Next, we used the approach in an experiment with multiple obstacles in a real environment. (8) and (9), the cost function values of the destination and obstacle were negative and positive, respectively. Therefore, the sum of the cost function values from Equation (7) was negative, which meant that no obstacles were present inside the range of the scanning sonar, and the ROV traveled toward the destination.…”

mentioning

confidence: 98%

Application of Fuzzy Theory and Optimum Computing to the Obstacle Avoidance Control of Unmanned Underwater Vehicles

et al. 2020

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Autonomous underwater vehicles and remotely operated vehicles (ROVs) are unmanned underwater vehicles widely used in marine environments. Establishing an efficient obstacle avoidance approach in underwater environments remains a challenge for these vehicles. Most studies have relied on simulated results; few have been conducted with vehicles in a real environment. This study used an ROV equipped with a scanning sonar as an experimental platform and applied fuzzy logic control to solve nonlinear and uncertain problems, which are difficult to address using conventional control theory. Using data from the depth and inertial sensors, fuzzy logic control can output defuzzification command values that are passed through a fuzzy inference engine to control ROV motion. Fuzzy logic control was used to evaluate depth and heading degrees in navigation experiments. In heading navigation, scanning sonar was used to detect obstacles in the scanning range. An optimum navigation strategy was also developed to calculate appropriate headings to safely and stably navigate during a mission to attain a predetermined destination. The results indicated that the ROV with fuzzy logic control had superior control stability and obstacle avoidance in an underwater environment.

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“…In recent years, many path following control methods have been well verified in theory, such as fuzzy control [6,7], backstepping control [8,9], sliding mode control [10,11], etc., [12,13]. Specifically, an adaptive fuzzy control method is presented to stabilize the underactuated surface vessel (USV) to the desired position [14]. However, the fuzzy method can not adapt to the change of a controlled object and has poor robustness.…”

Section: Introductionmentioning

confidence: 99%

3DOF Adaptive Line-Of-Sight Based Proportional Guidance Law for Path Following of AUV in the Presence of Ocean Currents

et al. 2019

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In order to achieve high-precision path following of autonomous underwater vehicle (AUV) in the horizontal plane, a three degrees-of-freedom adaptive line-of-sight based proportional (3DOFAPLOS) guidance law is proposed. Firstly, the path point coordinate system is introduced, which is suitable for the conversion of an arbitrary path. Then, the appropriate look-ahead distance is obtained by an improved adaptive line-of-sight (ALOS) according to three degrees-of-freedom (3DOF), including the cross-track error, the curvature of reference path, and the forward speed. Moreover, combining three degrees-of-freedom ALOS (3DOFALOS) with proportional guidance law, the desired heading is calculated considering the drift angle. 3DOFAPLOS has two functions: in the convergence stage, 3DOFALOS plays a leading role, making AUV converge to the path more quickly and smoothly. In the guidance stage, proportional guidance law plays a major role in effectively resisting the influence of drift angle and making AUV sail along the reference path. If the path is curved, 3DOFALOS makes contributions in both stages, adjusting look-ahead distance in real time with respect to curvature. The stability of the designed closed system is proved by Lyapunov theory. Both simulation and experiment results have verified that 3DOFAPLOS has a satisfactory result, which improves tracking performance more than 50% compared with the traditional line-of-sight (LOS). Specifically, the mean average error (MAE) of path following under 3DOFAPLOS can be reduced by about 60%, and the root mean square error (RMSE) can be reduced by about 50% compared with LOS.

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Adaptive fuzzy output feedback stabilization control for the underactuated surface vessel

Cited by 44 publications

References 17 publications

Robust hierarchical sliding mode control with state-dependent switching gain for stabilization of rotary inverted pendulum

Robust hierarchical sliding mode control with state-dependent switching gain for stabilization of rotary inverted pendulum

Application of Fuzzy Theory and Optimum Computing to the Obstacle Avoidance Control of Unmanned Underwater Vehicles

3DOF Adaptive Line-Of-Sight Based Proportional Guidance Law for Path Following of AUV in the Presence of Ocean Currents

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