Sliding mode path tracking control for fish-robot under ocean current perturbation

Suebsaiprom, Pichet; Lin, Chun-Liang

doi:10.1109/icca.2016.7505406

Cited by 6 publications

(19 citation statements)

References 13 publications

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“…For a fixed ∆t, expression (20) resembles 3, where the derivatives of the function f k are the observables Ψ(s k ).…”

Section: A Global Error Bounds Of Derivative-based Koopman Operatormentioning

confidence: 99%

“…When training a Koopman operator, pairs of measurements of the states s k and s k+1 are used to evaluate the basis functions at the corresponding time steps: Ψ(s k ) and Ψ(s k+1 ). Note that, in (20), all derivatives of f k are assumed to be different functions. The analytical expression 20is equivalent to a Taylor series expansion (17) across one time step ∆t for all the basis functions.…”

Section: A Global Error Bounds Of Derivative-based Koopman Operatormentioning

confidence: 99%

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Local Koopman Operators for Data-Driven Control of Robotic Systems

Mamakoukas¹,

Castaño²,

Tan³

et al. 2019

Robotics: Science and Systems XV

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This paper presents a methodology for linear embedding of nonlinear systems that bounds the model error in terms of the prediction horizon and the magnitude of the derivatives of the system states. Using higher-order derivatives of general nonlinear dynamics that need not be known, we construct a Koopman operator-based linear representation and utilize Taylor series accuracy to derive an error bound. The error formula is used to choose the order of derivatives in the basis functions and obtain a data-driven Koopman model using a closed-form expression that can be computed in real time. The Koopman representation of the nonlinear system is then used to synthesize LQR feedback. The efficacy of the embedding approach is demonstrated with simulation and experimental results on the control of a tail-actuated robotic fish. Experimental results show that the proposed data-driven control approach outperforms a tuned PID (Proportional Integral Derivative) controller and that updating the data-driven model online significantly improves performance in the presence of unmodeled fluid disturbance. This paper is complemented with a video: https://youtu.be/9 wx0tdDta0.

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“…For a fixed ∆t, expression (20) resembles 3, where the derivatives of the function f k are the observables Ψ(s k ).…”

Section: A Global Error Bounds Of Derivative-based Koopman Operatormentioning

confidence: 99%

Section: A Global Error Bounds Of Derivative-based Koopman Operatormentioning

confidence: 99%

Local Koopman Operators for Data-Driven Control of Robotic Systems

Mamakoukas¹,

Castaño²,

Tan³

et al. 2019

Robotics: Science and Systems XV

View full text Add to dashboard Cite

show abstract

“…Currently, model predictive control (MPC) is a widely used path tracking control method [7][8][9][10][11][12][13][14][15][16]. The biggest advantage of MPC over other control methods, such as pure pursuit control [17], feedforward-feedback control [18], and sliding mode control [19][20][21][22], is that it can take constraints of the system into account explicitly [23]. This advantage is very beneficial for solving the problem of path tracking under actuator saturation and other system constraints.…”

Section: Introductionmentioning

confidence: 99%

Path Tracking of Mining Vehicles Based on Nonlinear Model Predictive Control

et al. 2019

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Path tracking of mining vehicles plays a significant role in reducing the working time of operators in the underground environment. Because the existing path tracking control of mining vehicles, based on model predictive control, is not very effective when the longitudinal velocity of the vehicle is above 2 m/s, we have devised a new controller based on nonlinear model predictive control. Then, we compare this new controller with the existing model predictive controller. In the results of our simulation, the tracking accuracy of our controller at the longitudinal velocity of 4 m/s is close to that of the existing model predictive controller, at the longitudinal velocity of 2 m/s. When longitudinal velocity is 4 m/s, the existing model predictive controller cannot drive the mining vehicle to track the given path, but our nonlinear model predictive controller can, and the maximum displacement error and heading error are 0.1382 m and 0.0589 rad, respectively. According to these results, we believe that this nonlinear model predictive controller can be used to improve the performance of the path tracking of mining vehicles.

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“…There has been additional work done on model-based closedloop motion control to achieve maneuvering or trajectory tracking [35][36][37][38][39][40][41]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the authors in Ref. [41] designed a sliding mode controller for swimming, orientating, and waypoint tracking of robotic fish in three-dimensional motion. Despite aforementioned progress in the control of robotic fish, a unified, systematic control approach that incorporates performance objectives and accommodates input constraints for such robots has not been proposed.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control-Based Path-Following for Tail-Actuated Robotic Fish

Castaño

Tan

2019

Journal of Dynamic Systems, Measurement, and Control

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There has been an increasing interest in the use of autonomous underwater robots to monitor freshwater and marine environments. In particular, robots that propel and maneuver themselves like fish, often known as robotic fish, have emerged as mobile sensing platforms for aquatic environments. Highly nonlinear and often under-actuated dynamics of robotic fish present significant challenges in control of these robots. In this work, we propose a nonlinear model predictive control (NMPC) approach to path-following of a tail-actuated robotic fish that accommodates the nonlinear dynamics and actuation constraints while minimizing the control effort. Considering the cyclic nature of tail actuation, the control design is based on an averaged dynamic model, where the hydrodynamic force generated by tail beating is captured using Lighthill's large-amplitude elongated-body theory. A computationally efficient approach is developed to identify the model parameters based on the measured swimming and turning data for the robot. With the tail beat frequency fixed, the bias and amplitude of the tail oscillation are treated as physical variables to be manipulated, which are related to the control inputs via a nonlinear map. A control projection method is introduced to accommodate the sector-shaped constraints of the control inputs while minimizing the optimization complexity in solving the NMPC problem. Both simulation and experimental results support the efficacy of the proposed approach. In particular, the advantages of the control projection method are shown via comparison with alternative approaches.

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Sliding mode path tracking control for fish-robot under ocean current perturbation

Cited by 6 publications

References 13 publications

Local Koopman Operators for Data-Driven Control of Robotic Systems

Local Koopman Operators for Data-Driven Control of Robotic Systems

Path Tracking of Mining Vehicles Based on Nonlinear Model Predictive Control

Model Predictive Control-Based Path-Following for Tail-Actuated Robotic Fish

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