Neuro-fuzzy control of underwater vehicle-manipulator systems

Xu, Bin; Pandian, S.R.; Sakagami, Norimitsu; Petry, Frederick E.

doi:10.1016/j.jfranklin.2012.01.003

Cited by 76 publications

(27 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…and (ii) it consists of Fig. 1 Transfer function model of two-area interconnected thermal power system www.ietdl.org simple line segments which reduces the computational burden [24][25][26]. Membership functions for error, error derivative and FLC output is shown in Fig.…”

Section: Controller Structurementioning

confidence: 99%

Hybrid differential evolution particle swarm optimisation optimised fuzzy proportional–integral derivative controller for automatic generation control of interconnected power system

Sahu

Pati

Panda

2014

IET Generation, Transmission & Distribution

177

View full text Add to dashboard Cite

A novel fuzzy proportional-integral derivative (PID) controller is proposed in this study for automatic generation control (AGC) of interconnected power systems. The optimum gains of the proposed fuzzy PID controller are optimised employing a hybrid differential evolution particle swarm optimisation (DEPSO) technique using an integral of time multiplied by absolute value of error criterion. The superiority of hybrid DEPSO algorithm over differential evolution and particle swarm optimisation (PSO) algorithm has also been demonstrated. The results are also compared with some recently published approaches such as artificial bee colony and PSO based proportional-integral/PID controllers for the same interconnected power systems. Furthermore, performance of the proposed system is analysed by varying the different parameters such as loading condition, system parameters and objective functions. It is observed that the optimum gains of the proposed fuzzy PID controller need not be reset even if the system is subjected to variation in loading condition and system parameters. Finally, the study is extended to a three area system considering generation rate constraint to demonstrate the ability of the proposed approach to cope with multiple interconnected systems. Comparison with previous AGC methods reported in the literature validates the significance of the proposed approach.

show abstract

Section: Controller Structurementioning

confidence: 99%

Hybrid differential evolution particle swarm optimisation optimised fuzzy proportional–integral derivative controller for automatic generation control of interconnected power system

Sahu

Pati

Panda

2014

IET Generation, Transmission & Distribution

177

View full text Add to dashboard Cite

show abstract

“…In the antecedent parts, the input space is divided into a set of fuzzy regions, and in the consequent parts the system behavior in those regions is described. Recently a number of different approaches have been used for designing fuzzy IF-THEN rules based on clustering [35][36][37][38][39][40], the table look-up scheme [41], the least-squares method (LSM) [1,16], gradient algorithms [2,3,7,18,19,29], and genetic algorithms [5,29,31]. In this paper, the fuzzy clustering is applied to design the antecedent (premise) parts, and the gradient algorithm is applied to design the consequent parts of the fuzzy rules.…”

Section: Parameter Update Rulesmentioning

confidence: 99%

“…In such situations, the use of fuzzy approaches may alleviate the difficulties. The integration of fuzzy systems with neural networks has recently become a popular approach for modeling and control of such uncertain systems [1][2][3][4][5][6]. The structures of these systems are generally based on type-1 fuzzy systems, that is the membership functions used in the antecedent and/or the consequent parts of the fuzzy rules are generally of type-1.…”

Section: Introductionmentioning

confidence: 99%

A type-2 fuzzy wavelet neural network for system identification and control

Abiyev

Kaynak

Kayacan

2013

Journal of the Franklin Institute

View full text Add to dashboard Cite

“…Many interacting factors which are involved in underwater vehicle dynamics can cause oscillatory or unstable operations (Perstro, 1994). Several control solutions for position or velocity control of underwater vehicles have been proposed in the literature (Akçakaya & Gören Sümer, 2014;Goheen & Jeffery, 1990;Guo, Chiu, & Huang, 2003;Healey & Lienard, 1993;Kumar, Kumar, Sen, & Dasgupta, 2009;Pisano & Usai, 2004;Subudhi, Mukherjee, & Ghosh, 2013;Sun & Cheah, 2009;Xu, Pandian, Sakagami, & Petry, 2012); however, all of them have concentrated on autonomous underwater vehicles (AUVs). It is worth noting that, unlike the variable mass underwater vehicles (VMUVs), the masses of AUVs are fixed.…”

Section: Introductionmentioning

confidence: 99%

Movement control of a variable mass underwater vehicle based on multiple-modeling approach

Shafiei

Binazadeh

2014

Systems Science & Control Engineering

View full text Add to dashboard Cite

This paper is one of the few studies dealing with modeling and control of a variable mass underwater vehicle with six degrees of freedom. Since, the mass of fuel changes during the operation, both mass and the center of mass of the vehicle change with time. Therefore, dynamic equations are written around the center of buoyancy and these equations are more complicated than the equations of the fixed mass underwater vehicles. Also, fin angles which are means of path-following (control inputs) do not directly appear in the dynamical equations which cause more complexity in the controller design. In this paper, the multiple-modeling approach is used in the controller design and the control signals are achieved by the weighed combination of multiple controllers. Simulation results reveal the effectiveness of the multiple controllers in set point tracking.

show abstract

Neuro-fuzzy control of underwater vehicle-manipulator systems

Cited by 76 publications

References 12 publications

Hybrid differential evolution particle swarm optimisation optimised fuzzy proportional–integral derivative controller for automatic generation control of interconnected power system

Hybrid differential evolution particle swarm optimisation optimised fuzzy proportional–integral derivative controller for automatic generation control of interconnected power system

A type-2 fuzzy wavelet neural network for system identification and control

Movement control of a variable mass underwater vehicle based on multiple-modeling approach

Contact Info

Product

Resources

About