CPG-based control of a turtle-like underwater vehicle

Seo, Keehong; Chung, Soon‐Jo; Slotine, Jean‐Jacques E.

doi:10.1007/s10514-009-9169-0

Cited by 111 publications

(27 citation statements)

References 33 publications

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“…An attraction of the Hopf oscillator is its circular limit cycle with a radius of μ and a frequency of ω, along with the intrinsic synchronization property, making it an ideal building block to establish CPGs for robotic locomotion like swimming, flying, and walking [53], [54], [56], [58], [99].…”

Section: ) Matsuoka Nomentioning

confidence: 99%

A Survey on CPG-Inspired Control Models and System Implementation

Tan

Chen

et al. 2014

IEEE Trans. Neural Netw. Learning Syst.

226

103

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This paper surveys the developments of the last 20 years in the field of central pattern generator (CPG) inspired locomotion control, with particular emphasis on the fast emerging robotics-related applications. Functioning as a biological neural network, CPGs can be considered as a group of coupled neurons that generate rhythmic signals without sensory feedback; however, sensory feedback is needed to shape the CPG signals. The basic idea in engineering endeavors is to replicate this intrinsic, computationally efficient, distributed control mechanism for multiple articulated joints, or multi-DOF control cases. In terms of various abstraction levels, existing CPG control models and their extensions are reviewed with a focus on the relative advantages and disadvantages of the models, including ease of design and implementation. The main issues arising from design, optimization, and implementation of the CPG-based control as well as possible alternatives are further discussed, with an attempt to shed more light on locomotion control-oriented theories and applications. The design challenges and trends associated with the further advancement of this area are also summarized.

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Section: ) Matsuoka Nomentioning

confidence: 99%

A Survey on CPG-Inspired Control Models and System Implementation

Tan

Chen

et al. 2014

IEEE Trans. Neural Netw. Learning Syst.

226

103

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“…In [15], by using contraction theory, it has been proved that the oscillators in the model (5) synchronize globally and exponentially with strong enough couplings. As pointed in [9], the scalability of the GIM is indeed an important virtue for generation the similar locomotion behaviors that can be made identical with appropriate temporal scaling and spatial scaling.…”

Section: Fish-like Swimming Gaits Generationmentioning

confidence: 99%

“…In the first experiment described as Eq. (15), the output of the system V t is recorded when the input is the desired speed V r . Then, in the second experiment described as Eq.…”

Section: Feedback Control Design With Iftmentioning

confidence: 99%

A motion control approach for a robotic fish with iterative feedback tuning

Ren

2015

2015 IEEE International Conference on Industrial Technology (ICIT)

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This paper proposes a model-free motion control approach for a robotic fish. The fish-like swimming gaits first are generated by a general internal model (GIM)-based learning approach for the robot. Then, a feedforward controller and a proportional-integral-derivative (PID)-based feedback controller are scheduled to control the swimming gaits of the robot to achieve desired motion. To improve the performance of the feedback controller and avoid tedious manual tuning, a pure data-driven iterative feedback (IFT) method is adopted for tuning the parameters of the feedback controller. Finally, experiment results verify the effectiveness of the motion control approach.

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“…Such disturbance commonly exists in circumstances that a manipulator moves back and forth [3,4]. Normally, PID controllers can bring satisfying tracking performance when the friction is a constant or proportional to the movement speed [5][6][7]. Due to the un-modeled hydrodynamics and the uncertainties among parameters of mathematical model of fish robot, it is a challenge for the motorized robot system to track the periodical gait control signals in water environment.…”

Section: Introductionmentioning

confidence: 99%