Robust task-space control of an autonomous underwater vehicle-manipulator system by PID-like fuzzy control scheme with disturbance estimator

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

doi:10.1016/j.oceaneng.2017.04.030

Cited by 56 publications

(23 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Based on (31)(32), it is known that the control performance largely depends on the value of the gain matrix, A. Its submatrices A ν , A m , H 2 and H c are defined as shown in (38)(39)(40)(41).…”

Section: B Fuzzy Algorithmmentioning

confidence: 99%

“…Moreover, the performance and stability of robust fuzzy controllers can be ensured and meanwhile the number of fuzzy rules can be reduced [39]. Londhe presented a robust nonlinear PID-like fuzzy control scheme [40] and a new robust single-input fuzzy logic control scheme [41] for a task-space trajectory tracking control of the UVMS. Although there have been some research attempts to investigate the coupling effects due to hydrodynamic effects in the case of external disturbances, e.g.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling and Fuzzy Decoupling Control of an Underwater Vehicle-Manipulator System

Han

Wei

et al. 2020

IEEE Access

View full text Add to dashboard Cite

This paper presents the detailed modeling and simulation of the dynamic coupling between an autonomous underwater vehicle (AUV) and a manipulator. The modeling processes are described with the incorporation of the most dominating hydrodynamic effects such as added mass, lift and drag forces. The hydrodynamic coefficients are derived using strip theory and are adjusted according to dynamical similarity. A fuzzy decoupling controller (FDC) is proposed for an autonomous underwater vehicle-manipulator system (UVMS) which consists of two subsystems, an underwater vehicle and a manipulator. The proposed controller uses a fuzzy algorithm (FA) to adaptively tune the gain matrix of the error function (EF). The EF is described by the integral sliding surface function. This technique allows the off-diagonal elements developed for decoupling the system to be incorporated in the gain matrix. Tracing the FA and EF back to the principle of feedback linearization, one further obtains evidence about the decoupling and stability of the system. Moreover, a desired trajectory with the consideration of the dynamic coupling of the AUV is designed to reduce the thruster forces and manipulator's torques. This technique provides high performance in terms of tracking error norms and expended energy norms. A major contribution of this study is that it adopts the off-diagonal elements to exploit the dynamic coupling between the degrees of freedom of the subsystem and the dynamic coupling between the two subsystems. Simulation results demonstrate the effectiveness and robustness of the proposed technique in the presence of parameter uncertainties and external disturbances. INDEX TERMS AUV, dynamic coupling, decoupling control, fuzzy algorithm, hydrodynamic effect, underwater manipulator.

show abstract

Section: B Fuzzy Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Fuzzy Decoupling Control of an Underwater Vehicle-Manipulator System

Han

Wei

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…This is the fundamental problem in FLC design [32][33]. To overcome the difficulties in optimal control tuning, a standard phase plane technique has been used for designing robust rule base for PI-like fuzzy type control [34][35].…”

Section: Fuzzy Logic Controllers (Flc)mentioning

confidence: 99%

Active and Reactive Power Control for DFIG Using PI, Fuzzy Logic and Self-Tuning PI Fuzzy Controllers

Tamaarat¹

2019

AMA_C

View full text Add to dashboard Cite

This paper presented a system consists of wind turbine operating under the wind speed change, doubly fed induction generator (DFIG) connected directly to the constant grid by the stator and fed by a power converter on the rotor side. Vector control is used to independently control the flow of active and reactive power between the stator of the (DFIG) and the grid, which in this paper is realized, with conventional PI, fuzzy logic and fuzzy self-tuning PI controllers. The proposed controllers generate the references rotor voltages required to guarantee active and reactive power reach their desired reference values. Modeling and simulation of system using MATLAB and their behavior has been examined with simulations. The dynamics of the system is analyzed under varying wind speed, this analysis is focused on the active and reactive power control. The results obtained from PI controller, fuzzy and Fuzzy self tuned PI controllers are compared to validate the advantages of the proposed intelligent controllers based on fuzzy systems.

show abstract

“…Recently, Khodayari et al [30] have proposed a self-adaptive fuzzy PID controller for the attitude control of an AUV based on its previously obtained dynamic model from mechanical principles. Also, fuzzy control formulations for underwater vehicle-manipulator system (UVMS) were formulated in Esfahani et al [31]. However, one disadvantage for using fuzzy control systems for AUVs is that subjective knowledge is required for the definition of the fuzzy rules and membership functions.…”

Section: Introductionmentioning

confidence: 99%

Adaptive low-level control of autonomous underwater vehicles using deep reinforcement learning

Carlucho

Paula

Wang

et al. 2018

Robotics and Autonomous Systems

134

View full text Add to dashboard Cite

show abstract

Robust task-space control of an autonomous underwater vehicle-manipulator system by PID-like fuzzy control scheme with disturbance estimator

Cited by 56 publications

References 44 publications

Modeling and Fuzzy Decoupling Control of an Underwater Vehicle-Manipulator System

Modeling and Fuzzy Decoupling Control of an Underwater Vehicle-Manipulator System

Active and Reactive Power Control for DFIG Using PI, Fuzzy Logic and Self-Tuning PI Fuzzy Controllers

Adaptive low-level control of autonomous underwater vehicles using deep reinforcement learning

Contact Info

Product

Resources

About