Disturbance estimator based non-singular fast fuzzy terminal sliding mode control of an autonomous underwater vehicle

Patre, B. M.; Londhe, P. S.; Waghmare, L. M.; Mohan, Santhakumar

doi:10.1016/j.oceaneng.2018.03.082

Cited by 73 publications

(35 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nonetheless, the main disadvantage of the aforementioned control schemes is the inherent control input chattering that is energy intensive and may result in high frequency dynamics, which is undesirable for underwater applications. Finally, adaptive neural network [11], [14], learning [15], [16] and fuzzy control [17], [18] schemes that deal with model uncertainties by exploiting the universal approximation capabilities of neural network and fuzzy system structures, but unfortunately, yield inevitably reduced levels of robustness against modeling imperfections [19].…”

Section: Introductionmentioning

confidence: 99%

Robust Trajectory Tracking Control for Underactuated Autonomous Underwater Vehicles in Uncertain Environments

Heshmati-alamdari

Νίκου

Dimarogonas

2021

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

This paper addresses the tracking control problem of 3D trajectories for underactuated underwater robotic vehicles operating in a constrained workspace including obstacles. More specifically, a robust Nonlinear Model Predictive Control (NMPC) scheme is presented for the case of underactuated Autonomous Underwater Vehicles (AUVs) (i.e., unicycle-like vehicles actuated only in surge, heave and yaw). The purpose of the controller is to steer the unicycle-like AUV to a desired trajectory with guaranteed input and state constraints (e.g., obstacles, predefined vehicle velocity bounds, thruster saturations) inside a partially known and dynamic environment where the knowledge of the operating workspace is constantly updated via the vehicle's on-board sensors. In particular, considering the sensing range of the vehicle, obstacle avoidance with any of the detected obstacles is guaranteed, by on-line generation of a collision-free trajectory tracking path, despite the model dynamic uncertainties and the presence of external disturbances representing ocean currents and waves. Finally, realistic simulation studies verify the performance and efficiency of the proposed framework.

show abstract

Section: Introductionmentioning

confidence: 99%

Robust Trajectory Tracking Control for Underactuated Autonomous Underwater Vehicles in Uncertain Environments

Heshmati-alamdari

Νίκου

Dimarogonas

2021

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…The chattering problem in tracking control is eliminated, and the steady-state error in tracking control is reduced. In literature [38], a nonsingular fast fuzzy terminal sliding mode controller (NFFTSMC) with interference estimator is designed to realize the convergence of finite time error and robust control.…”

Section: Introductionmentioning

confidence: 99%

Manipulator Trajectory Tracking of Fuzzy Control Based on Spatial Extended State Observer

et al. 2020

View full text Add to dashboard Cite

This paper mainly studies the trajectory tracking control problem of the manipulator of TianGong-2 when capturing floating objects in the experimental cabin. Microgravity environment will lead to the existing control system of space robot can not produce good control effect. Different gravity environments change the design requirements of space robots, and the ground test cannot meet these space requirements. In this paper, a fuzzy control strategy based on a spatial extended state observer(FC-SESO) is proposed to solve these inconsistent problems. First, a spatial extended state observer is designed to estimate the influence of disturbance on the system. In order to estimate the disturbance quickly and accurately, a disturbance model including gust is established. Second, in order to ensure the rapid convergence of the desired trajectory, the fuzzy control law is derived. Based on Lyapunov stability theory, the stability of the control system based on the spatial extended state observer is proven. Finally, the proposed system is simulated and captured on the Tiangong-2 manipulator platform to verify the effectiveness of the control algorithm. INDEX TERMS Fuzzy control, microgravity, spatial extended state observer, trajectory tracking control.

show abstract

“…Zhang et al proved that interval type-2 fuzzy logic controllers can improve and optimize tracking accuracy and eliminate stickslip [19], and an augmented fuzzy observer was proposed to attenuate the negative impact from the unknown output delays, which likely degrade the performance stability of the control systems [18]. Patre et al adopted a fuzzy logic control (FLC) tool to generate the control signal in order to reduce chattering in control inputs, which commonly occur in conventional terminal sliding mode controller (TSMC), and an estimated uncertainty term to compensate for the unmodeled dynamics, external disturbances, and time-varying parameters [20]. For the photovoltaic (PV) system application, a novel beta-parameter three-input one-output fuzzy-logic-based maximum-power point-tracking (MPPT) algorithm was presented and the advantages of the proposed algorithm are verified [21].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control

Yin

et al. 2019

Electronics

View full text Add to dashboard Cite

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.

show abstract

Disturbance estimator based non-singular fast fuzzy terminal sliding mode control of an autonomous underwater vehicle

Cited by 73 publications

References 58 publications

Robust Trajectory Tracking Control for Underactuated Autonomous Underwater Vehicles in Uncertain Environments

Robust Trajectory Tracking Control for Underactuated Autonomous Underwater Vehicles in Uncertain Environments

Manipulator Trajectory Tracking of Fuzzy Control Based on Spatial Extended State Observer

Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control

Contact Info

Product

Resources

About