Motion Control Of Robotic Fish Research Articles

CFD based parameter tuning for motion control of robotic fish

After millions of years of evolution, fishes have been endowed with agile swimming ability to accomplish various behaviourally relevant tasks. In comparison, robotic fish are still quite poor swimmers. One of the unique challenges facing robotic fish is the difficulty in tuning the motion control parameters on the robot directly. This is mainly due to the complex fluid environment robotic fish need to contend with and endurance limitations (i.e. battery capacity limitations). To overcome these limitations, we propose a computational fluid dynamics (CFD) simulation platform to first tune the motion control parameters for the computational robotic fish and then refine the parameters by experiments on robotic fish. Within the simulation platform, the body morphology and gait control of the computational robotic fish are designed according to a robotic fish. The gait control is implemented by a central pattern generator (CPG); The CFD model is solved by using a hydrodynamic-kinematics strong-coupling method. We tested our simulation platform with three basic tasks under active disturbance rejection control (ADRC) and try-and-error-based parameter tuning. Trajectory comparisons between the computational robotic fish and robotic fish verify the effectiveness of our simulation platform. Moreover, power costs and swimming efficiency under the motion control are also analyzed based on the outputs from the simulation platform. Our results indicate that the CFD based simulation platform is powerful and robust, and shed new light on the efficient design and parameter optimization of the motion control of robotic fish.

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

In this paper, we propose a novel robust adaptive control technique to steer the direction of attack of the robotic fish swimming under influence from varying environmental conditions. Due to complex nature of robot motion hydrodynamics, it is difficult to predict the true dynamics of the system with good accuracy. Hence, a discrete-time adaptive control technique is proposed, which can effectively track a reference even if the robot system's model parameters might vary over time due to physical variations in the system. Rigorous theoretical convergence analysis on the closed-loop system confirms that the reference tracking error will asymptotically be bounded within a prescribed limit. Furthermore, the adaptive control approach is experimentally verified to produce desirable performance under significant variations in payload and drag force on the robotic fish. The latest results, thus, signify that the proposed control algorithm can efficiently control the robotic fish motion in complex underwater environments.

A Lecture on Current Limiter the Motion Control of Robotic Fish Based on BP Neural Network

Robotic fish is a multivariate and nonlinear controlled object, its work environment is complicated, these lead the motion controlling of robot fish be very difficult. This paper provides a method of robotic fish controlling based on BP neural network, and on URWPGSim2D simulation platform. Using the relative position of the fish robot, the ball and the target point in current cycle, this method can compute the two controlling value of robotic fish, which are the velocity and angular velocity, in the next cycle. The experiments show that this method can efficiently reduce the error that comes from the platform randomness, and robotic fish can run smoothly according to the predefined moving route.