Motion Control of Robotic Fish Under Dynamic Environmental Conditions Using Adaptive Control Approach

Verma, Saurab; Shen, Dong; Xu, Jian‐Xin

doi:10.1109/joe.2017.2758158

Cited by 13 publications

(5 citation statements)

References 42 publications

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is the controller transfer function, and s is the complex frequency in the Laplace transform [ 38 ]. For practical use, the control loop [ 39 , 40 , 41 , 42 ] was designed to satisfy the following preferred conditions: (1) the gain margin should be higher than 10 dB to cover the model uncertainties [ 43 ]; and (2) the phase margin should be larger than 45° to cover time delays and model uncertainties [ 44 , 45 ].…”

Section: Simulation and Controller Design Resultsmentioning

confidence: 99%

Simulation and Controller Design for a Fish Robot with Control Fins

Gumpina,

Lee,

Kim

et al. 2024

Biomimetics

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In this paper, a nonlinear simulation block for a fish robot was designed using MATLAB Simulink. The simulation block incorporated added masses, hydrodynamic damping forces, restoring forces, and forces and moments due to dorsal fins, pectoral fins, and caudal fins into six-degree-of-freedom equations of motion. To obtain a linearized model, we used three different nominal surge velocities (i.e., 0.2 m/s, 0.4 m/s, and 0.6 m/s). After obtaining output responses by applying pseudo-random binary signal inputs to a nonlinear model, an identification tool was used to obtain approximated linear models between inputs and outputs. Utilizing the obtained linearized models, two-degree-of-freedom proportional, integral, and derivative controllers were designed, and their characteristics were analyzed. For the 0.4 m/s nominal surge velocity models, the gain margins and phase margins of the surge, pitch, and yaw controllers were infinity and 69 degrees, 26.3 dB and 85 degrees, and infinity and 69 degrees, respectively. The bandwidths of surge, pitch, and yaw control loops were determined to be 2.3 rad/s, 0.17 rad/s, and 2.0 rad/s, respectively. Similar characteristics were observed when controllers designed for linear models were applied to the nonlinear model. When step inputs were applied to the nonlinear model, the maximum overshoot and steady-state errors were very small. It was also found that the nonlinear plant with three different nominal surge velocities could be controlled by a single controller designed for a linear model with a nominal surge velocity of 0.4 m/s. Therefore, controllers designed using linear approximation models are expected to work well with an actual nonlinear model.

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“…

Section: Simulation and Controller Design Resultsmentioning

confidence: 99%

Simulation and Controller Design for a Fish Robot with Control Fins

Gumpina,

Lee,

Kim

et al. 2024

Biomimetics

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“…In the field of bionic underwater robots, proportional–integral–derivative (PID), sliding mode control (SMC), fuzzy control, etc., are widely used in the motion control task [ 13 , 75 , 76 ]. Adaptive control and autodisturbance rejection control are also widely studied for the strong disturbance problem in the underwater environment [ 77 , 78 , 79 ]. Traditional control methods are influenced by the structure and parameters of the controller, resulting in mixed control effects.…”

Section: Task Spaces Of Bionic Underwater Robotsmentioning

confidence: 99%

A Survey on Reinforcement Learning Methods in Bionic Underwater Robots

Feng

Wang

et al. 2023

Biomimetics

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Bionic robots possess inherent advantages for underwater operations, and research on motion control and intelligent decision making has expanded their application scope. In recent years, the application of reinforcement learning algorithms in the field of bionic underwater robots has gained considerable attention, and continues to grow. In this paper, we present a comprehensive survey of the accomplishments of reinforcement learning algorithms in the field of bionic underwater robots. Firstly, we classify existing reinforcement learning methods and introduce control tasks and decision making tasks based on the composition of bionic underwater robots. We further discuss the advantages and challenges of reinforcement learning for bionic robots in underwater environments. Secondly, we review the establishment of existing reinforcement learning algorithms for bionic underwater robots from different task perspectives. Thirdly, we explore the existing training and deployment solutions of reinforcement learning algorithms for bionic underwater robots, focusing on the challenges posed by complex underwater environments and underactuated bionic robots. Finally, the limitations and future development directions of reinforcement learning in the field of bionic underwater robots are discussed. This survey provides a foundation for exploring reinforcement learning control and decision making methods for bionic underwater robots, and provides insights for future research.

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“…Deng et al designed a simple 3D-printed robot fish that can detect obstacles and move away adaptively using neuro-fuzzy control [18] . Verma et al used an adaptive control approach for robot fish closed-loop orientation control [19] . However, these robot fish just avoided a simple static obstacle with a stiff flapping.…”

Section: Introductionmentioning

confidence: 99%

Bioinspired Closed-loop CPG-based Control of a Robot Fish for Obstacle Avoidance and Direction Tracking

et al. 2021

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This paper presents a study on bioinspired closed-loop Central Pattern Generator (CPG) based control of a robot fish for obstacle avoidance and direction tracking. The biomimetic robot fish is made of a rigid head with a pair of pectoral fins, a wire-driven active body covered with soft skin, and a compliant tail. The CPG model consists of four input parameters: the flapping amplitude, the flapping angular velocity, the flapping offset, and the time ratio between the beat phase and the restore phase in flapping. The robot fish is equipped with three infrared sensors mounted on the left, front and right of the robot fish, as well as an inertial measurement unit, from which the surrounding obstacles and moving direction can be sensed. Based on these sensor signals, the closed-loop CPG-based control can drive the robot fish to avoid obstacles and to track designated directions. Four sets of experiments are presented, including avoiding a static obstacle, avoiding a moving obstacle, tracking a designated direction and tracking a designated direction with an obstacle in the path. The experiment results indicated that the presented control strategy worked well and the robot fish can accomplish the obstacle avoidance and direction tracking effectively.

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Motion Control of Robotic Fish Under Dynamic Environmental Conditions Using Adaptive Control Approach

Cited by 13 publications

References 42 publications

Simulation and Controller Design for a Fish Robot with Control Fins

Simulation and Controller Design for a Fish Robot with Control Fins

A Survey on Reinforcement Learning Methods in Bionic Underwater Robots

Bioinspired Closed-loop CPG-based Control of a Robot Fish for Obstacle Avoidance and Direction Tracking

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