Adaptive backstepping sliding-mode controller with bound estimation for underwater robotics vehicles

Patompak, Pakpoom; Nilkhamhang, Itthisek

doi:10.1109/ecticon.2012.6254254

Cited by 11 publications

(4 citation statements)

References 7 publications

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“…[7] and [8], an adaptive mechanism is developed to estimate the uncertain bound parameters and the estimates are then used as controller parameters to guarantee that effects of the system uncertainties can be eliminated and asymptotic error convergence can be obtained. Patompark et al 9 proposed an adaptive backstepping SMC with bound estimation for underwater robotics vehicles. Since the neural network has the capability to approximate any nonlinear function over the compact input space, it can be utilized for interpolation and functional modeling.…”

Section: Introductionmentioning

confidence: 99%

Robust neural network control of MEMS gyroscope using adaptive sliding mode compensator

Fei

Yang

2014

Robotica

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SUMMARYA new robust neural sliding mode (RNSM) tracking control scheme using radial basis function (RBF) neural network (NN) is presented for MEMS z-axis gyroscope to achieve robustness and asymptotic tracking error convergence. An adaptive RBF NN controller is developed to approximate and compensate the large uncertain system dynamics, and a robust compensator is designed to eliminate the impact of NN modeling error and external disturbances for guaranteeing the asymptotic stability property. Moreover, another RBF NN is employed to learn the upper bound of NN modeling error and external disturbances, so the prior knowledge of the upper bound of system uncertainties is not required. All the adaptive laws in the RNSM control system are derived in the same Lyapunov framework, which can guarantee the stability of the closed loop system. Comparative numerical simulations for an MEMS gyroscope are investigated to verify the effectiveness of the proposed RNSM tracking control scheme.

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Section: Introductionmentioning

confidence: 99%

Robust neural network control of MEMS gyroscope using adaptive sliding mode compensator

Fei

Yang

2014

Robotica

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“…For example, in [7] and [8] an adaptive mechanism is developed to estimate the uncertain bound parameters and the estimates are then used as controller parameters to guarantee that effects of the system uncertainties can be eliminated and asymptotic error convergence can be obtained. Patompark et al [9] proposed an adaptive backstepping SMC with bound estimation for underwater robotics vehicles. Since neural network has the capability to approximate any nonlinear function over the compact input space, it can be utilized for interpolation and functional modeling.…”

Section: Introductionmentioning

confidence: 99%

Robust RBF neural network control with adaptive sliding mode compensator for MEMS gyroscope

Fei

Yang

2013

2013 IEEE/ACIS 12th International Conference on Computer and Information Science (ICIS)

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A new robust neural sliding mode(RNSM) tracking control scheme using radial basis function(RBF) neural network (NN) is presented for MEMS (MicroElectroMechanical systems) Z-axis gyroscope to achieve robustness and asymptotic tracking error convergence. An adaptive RBF NN controller is developed to approximate and compensate the large uncertain system dynamics, and a robust compensator is designed to eliminate the impact of NN modeling error and external disturbances. Moreover, another RBF NN is employed to learn the upper bound of NN modeling error and external disturbances, so the prior knowledge of the upper bound of system uncertainties is not required. All the adaptive laws in the RNSM control system are derived in the same Lyapunov framework, which can guarantee the stability of the closed loop system. Numerical simulations for a MEMS gyroscope are investigated to verify the effectiveness of the proposed RNSM tracking control scheme.

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“…Before underwater manipulator begins to grasp the operation target autonomously, ROV is remotely operated to swim to the target position and then kept positioning [10]- [11] by its computer controller. After that, the controller estimates and predicts the position and orientation of the target by vision sensors in real time.…”

Section: Dynamic Joint Trajectory Generationmentioning

confidence: 99%

Dynamic trajectory planning of underwater hydraulic manipulator in joint space

Zhou

Wang

et al. 2014

2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014)

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Abstract-7-function underwater hydraulic manipulator can be mounted on underwater vehicles such as ROVs to achieve underwater operation. When the underwater vehicle is hovering in the sea, the target is in dynamic motion state compared to the vehicle position, which makes difficult for operators to accomplish dynamic operation task. Based on the underwater hydraulic manipulator structure characteristics, joint angle, joint angular velocity and total flow constraints are analyzed for manipulator trajectory generation. Cubic B-spline is applied to generate joint trajectories. To generate dynamic target trajectories, a method that can modify control points by calculating adjacent control points' intervals to fit the dynamic target is proposed. It can easily achieve synchronization and does not need to determine the total execution time before operation. Simulation experiments are performed to verify the efficacy of the proposed algorithm.

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Adaptive backstepping sliding-mode controller with bound estimation for underwater robotics vehicles

Cited by 11 publications

References 7 publications

Robust neural network control of MEMS gyroscope using adaptive sliding mode compensator

Robust neural network control of MEMS gyroscope using adaptive sliding mode compensator

Robust RBF neural network control with adaptive sliding mode compensator for MEMS gyroscope

Dynamic trajectory planning of underwater hydraulic manipulator in joint space

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