Decoupled PD set-point controller for underwater vehicles

Herman, Przemysław

doi:10.1016/j.oceaneng.2009.02.003

Cited by 74 publications

(31 citation statements)

References 9 publications

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“…The simulations were done for 6 DOF model of underwater vehicle which parameters can be found in [58].…”

Section: Underwater Vehicle Modelmentioning

confidence: 99%

“…8 ) we see that all angular velocity errors are reduced to zero after about 5 s. We can note that the dynamical coupling causes that at the start all variables are actuated (in spite of that only is r tracked). The velocity tracking without overshoot can be explained by the strong mechanical couplings and great mass of the vehicle (m = 250 kg [58], whereas m = 18.375 kg [62] for the airship). Observing the control signals time history related to linear velocity variables given in Fig.…”

Section: Underwater Vehicle Modelmentioning

confidence: 99%

“…Taking the above into account and inserting (3) into (1), and next pre-multiplying both sides by ϒ T (as in [58]) we can write: by [22]:…”

Section: Introductionmentioning

confidence: 99%

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Non-adaptive velocity tracking controller for a class of vehicles

Herman

Adamski

2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract.A non-adaptive controller for a class of vehicles is proposed in this paper. The velocity tracking controller is expressed in terms of the transformed equations of motion in which the obtained inertia matrix is diagonal. The control algorithm takes into account the dynamics of the system, which is included into the velocity gain matrix, and it can be applied for fully actuated vehicles. The considered class of systems includes underwater vehicles, fully actuated hovercrafts, and indoor airship moving with low velocity (below 3 m/s) and under assumption that the external disturbances are weak. The stability of the system under the designed controller is demonstrated by means of a Lyapunov-based argument. Some advantages arising from the use of the controller as well as the robustness to parameters uncertainty are also considered. The performance of the proposed controller is validated via simulation on a 6 DOF robotic indoor airship as well as for underwater vehicle model.

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“…The simulations were done for 6 DOF model of underwater vehicle which parameters can be found in [58].…”

Section: Underwater Vehicle Modelmentioning

confidence: 99%

Section: Underwater Vehicle Modelmentioning

confidence: 99%

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Non-adaptive velocity tracking controller for a class of vehicles

Herman

Adamski

2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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“…In the existing literatures, several different studies have been done in order to design autopilots for controlling the AUV's such as PD/PID controllers are designed in [16,22,23,31,33,48,50,61,79] as model based controllers most dynamically used in dynamic positioning and motion control. The adaptive control law is developed with estimation of uncertain parameters associated with the hydrodynamic damping co-efficients, which is used to generate appropriate control for the AUV mentioned in [2,4,11,53,[76][77][78].…”

Section: Introductionmentioning

confidence: 99%

Diving Autopilot Design for Underwater Vehicles Using an Adaptive Neuro-Fuzzy Sliding Mode Controller

Lakhekar

Waghmare

Vaidyanathan

2016

Advances and Applications in Nonlinear Control Systems

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In general, the diving dynamics of an autonomous underwater vehicle (AUV) has been derived under various assumptions on the motion of the vehicle in vertical plane. Usually, pitch angle of AUV is assumed to be small in maneuvering, so that the nonlinear dynamics in the depth motion of the vehicle could be linearized. However, a small-pitch-angle is a somewhat strong restricting condition and may cause serious modeling inaccuracies of AUV's dynamics. For this reason, many researchers concentrated their interests on the applications of adaptive control methodology to the motion control of underwater vehicle. In this chapter, we directly resolve the nonlinear equation of the AUV's diving motion without any restricting assumption on the pitch angle in diving model. The proposed adaptive neuro-fuzzy sliding mode controller (ANFSMC) with a proportional + integral + derivative (PID) sliding surface is derived so that the actual depth position tracks the desired trajectory despite uncertainty, nonlinear dynamics and external disturbances. In the proposed control structure, the corrective term is approximated by a continuous fuzzy logic control and the equivalent control is determined by a feedforward neural network. The weights of the neural network are updated such that the corrective control term of the ANFSMC goes to zero. The adaptive laws are employed to adjust the output scaling factor and to compute PID sliding surface coefficients. Finally, the lyapunov theory is employed to prove the stability of the ANFSMC for trajectory tracking of diving behaviors. Simulation results show that this control strategy can attain excellent control performance.

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“…A set-point controller for autonomous underwater vehicles was proposed by Herman [4]. The controller was expressed in transformed equations of motion with a diagonal inertia matrix.…”

mentioning

confidence: 99%

Design of Neural Network Control System for Controlling Trajectory of Autonomous Underwater Vehicles

Eski

Yildirim

2014

International Journal of Advanced Robotic Systems

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A neural network based robust control system design for the trajectory of Autonomous Underwater Vehicles (AUVs) is presented in this paper. Two types of control structure were used to control prescribed trajectories of an AUV. The vehicle was tested with random disturbances while taxiing under water. The results of the simulation showed that the proposed neural network based robust control system has superior performance in adapting to large random disturbances such as underwater flow. It is proved that this kind of neural predictor could be used in real-time AUV applications.

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Decoupled PD set-point controller for underwater vehicles

Cited by 74 publications

References 9 publications

Non-adaptive velocity tracking controller for a class of vehicles

Non-adaptive velocity tracking controller for a class of vehicles

Diving Autopilot Design for Underwater Vehicles Using an Adaptive Neuro-Fuzzy Sliding Mode Controller

Design of Neural Network Control System for Controlling Trajectory of Autonomous Underwater Vehicles

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