Effective Phase Tracking for Bioinspired Undulations of Robotic Fish Models: A Learning Control Approach

Hu, Tianjiang; Low, Kian Hsiang; Shen, Lincheng; Xu, Xiaojun

doi:10.1109/tmech.2012.2226049

Cited by 73 publications

(38 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

Theoretical Insights on Contraction-Type Iterative Learning Control for Biorobotic Systems with Preisach Hysteresis

Wang

Zhou

et al. 2016

International Journal of Advanced Robotic Systems

Self Cite

View full text Add to dashboard Cite

This article offers new insights on the learning control approach developed by [Hu et al. IEEE/ASME Trans. Mechatronics, 19(1): 191-200, 2014]. Theoretical insights are further proposed to unveil why the contraction-type iterative learning control (ILC) schemes are suitable and effective in compensating for hysteresis, widely existing in biorobotic locomotion. Under such circumstances, iteration-based second-order dynamics is adopted to describe the biorobotic systems acted upon by one unknown Preisach hysteresis term. The memory clearing operator is mathematically proven to enable feasibility of contractiontype ILC methods, regardless of whether the initial state is accurately set or not. The simulation examples confirm that the developed iteration-based controller combined with a preceded operator effectively reduce tracking errors caused by the hysteresis nonlinearity. Furthermore, the new insights on theoretical feasibility are definitively corroborated in accordance with the previously published experimental results.

show abstract

“…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%

Theoretical Insights on Contraction-Type Iterative Learning Control for Biorobotic Systems with Preisach Hysteresis

Wang

Zhou

et al. 2016

International Journal of Advanced Robotic Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

Design and Control of an Agile Robotic Fish With Integrative Biomimetic Mechanisms

Zhang

Qian

Pan

et al. 2016

IEEE/ASME Trans. Mechatron.

102

View full text Add to dashboard Cite

Flying insects and swimming fishes have high efficiency and high maneuverability in air and water, respectively. Their wings and fins have evolved for many ages to adapt to propelling in the complex environment. In the paper, an integrative biomimetic robotic fish is proposed and developed, which combines the advantages of insect wings and fish fins to achieve a high agility underwater. In the robotic fish, two caudal fins were equipped at the tail of the robotic fish in parallel as the main propulsion mechanism, the opposite flapping of the two caudal fins generates mutually opposing lateral forces during cruising, which leads to a stable and high-performance swimming. In addition, two pectoral fins that mimic the function of insect wings were equipped at two sides of the robotic fish, which enhances the robotic fish maneuverability in vertical plane. Moreover, a central pattern generator (CPG) model was designed to achieve the versatile maneuvering motions, motion switching, and autonomous swimming with an obstacle avoiding ability. The experiments have demonstrated that the robotic fish can swim more stably and efficiently with versatile maneuver motions by taking advantage of the integrative propulsion mechanism. The developed robotic fish have many potential applications for its agility, stable swimming, and low-cost structure.

show abstract

“…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%

On a Miniature Free-Swimming Robotic Fish with Multiple Sensors

Chen

et al. 2016

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Bio-inspired robotic fish hold strong promise for underwater missions. This paper deals with the design and control issues of a miniature free-swimming robotic fish with multiple sensors. Specifically, a synthesized mechanical design scheme mainly relying on a two-link serial mechanism and a pair of mechanical pectoral fins is first presented. Next, a bio-inspired Central Pattern Generator (CPG) based control method aided by feedback information from multiple sensors of various types to achieve three-dimensional swimming is proposed. Finally, experimental results on the 35-centimetre-long robotic fish verify the efficacy of the proposed mechatronic design and control methods. It is found that the CPG control combined with sensory information greatly contributes to swimming ability and intelligence of the robotic fish.

show abstract

Effective Phase Tracking for Bioinspired Undulations of Robotic Fish Models: A Learning Control Approach

Cited by 73 publications

References 43 publications

Theoretical Insights on Contraction-Type Iterative Learning Control for Biorobotic Systems with Preisach Hysteresis

Theoretical Insights on Contraction-Type Iterative Learning Control for Biorobotic Systems with Preisach Hysteresis

Design and Control of an Agile Robotic Fish With Integrative Biomimetic Mechanisms

On a Miniature Free-Swimming Robotic Fish with Multiple Sensors

Contact Info

Product

Resources

About