2014
DOI: 10.1109/tmech.2012.2226049
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Effective Phase Tracking for Bioinspired Undulations of Robotic Fish Models: A Learning Control Approach

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Cited by 71 publications
(37 citation statements)
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“…Using the anticipant scheme [12,[21][22][23] as a representative of contraction-type ILC methods, the iterative learning control law is stated in the form ( ) …”
Section: Ilc Scheme With a Memory-clearing Operatormentioning
confidence: 99%
“…Using the anticipant scheme [12,[21][22][23] as a representative of contraction-type ILC methods, the iterative learning control law is stated in the form ( ) …”
Section: Ilc Scheme With a Memory-clearing Operatormentioning
confidence: 99%
“…Since the first robotic fish, RoboTuna, was developed in 1995 [1], there have been many robotic fishes that mimic various kinds of fishes in the past years. According to the location of propulsion mechanisms, the propulsion methods of robotic fishes can be classified into two categories [2]: (1) body and/or caudal fins propulsion (BCF) [3][4][5], and (2) middle and/or paired fins propulsion (MPF) [6][7][8][9][10][11]. However, the current most robotic fishes are far from duplicating the locomotion characteristics of real fishes, which also sets up an obstacle for their applications in engineering.…”
Section: Introductionmentioning
confidence: 99%
“…Fortunately, real fish offer design and control paradigms or solutions. To date, fish-inspired swimming robots (hereafter termed robotic fish) have shown superior performance in efficient propulsion and high manoeuvrability compared with conventional underwater vehicles propelled by rotary propellers [4][5][6][7][8][9][10].…”
Section: Introductionmentioning
confidence: 99%