A robust non‐linear MPC framework for control of underwater vehicle manipulator systems under high‐level tasks

Νίκου, Αλέξανδρος; Verginis, Christos K.; Heshmati-alamdari, Shahab; Dimarogonas, Dimos V.

doi:10.1049/cth2.12045

Cited by 18 publications

(10 citation statements)

References 24 publications

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“…The practice of applying the obtained optimal damping coefficient to the anti-yaw damper of a railway vehicle through Model Predictive Control (MPC) has not been proposed yet. Based on the latest model predictive control (MPC) method [4][5][6], the active control of the anti-yaw damper is studied in this paper. The state quantity information of the vehicle is obtained in real time, and the traversing speed and the hunting speed of the frame are targeted.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control of active yaw damper for high-speed trains

Chen

Wang

et al. 2023

Sixth International Conference on Traffic Engineering and Transportation System (ICTETS 2022)

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To improve the line operation adaptability of high-speed trains, a Model Predictive Control (MPC) method based on anti-yaw damper is proposed to realize the stability control of vehicle hunting motion based on the real-time adjustment of the damping value of the shock absorbers. A simplified dynamic model with seven degrees of freedom in the lateral direction of the whole vehicle is established, including the traverse, yaw and roll degrees of freedom of the vehicle body, the traverse and yaw degrees of freedom of the bogie, and the linear wheel-rail contact relationship is considered. Based on Model Predictive Control (MPC) theory, the active yaw damper is designed, the objective function and constraint conditions are established, and the optimal damping coefficient is solved. The effect of the stability control of vehicle hunting motion is analyzed. The results show that compared with the passive suspension, the MPC active yaw damper can effectively suppress the primary hunting motion of the vehicle, but the restraint effect on the second hunting motion of the vehicle is not obvious.

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Section: Introductionmentioning

confidence: 99%

Model predictive control of active yaw damper for high-speed trains

Chen

Wang

et al. 2023

Sixth International Conference on Traffic Engineering and Transportation System (ICTETS 2022)

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“…MPC also has a strong ability to process problems with constraints. 24,25 Zhang and Liu et al successfully applied standard MPC control to AUV trajectory tracking control, but the stability of the controller was not verified by rationality. 26 Hu and Zhu et al designed a trajectory re-planning controller based on model predictive control (MPC) and used a quadratic programming (QP) optimization algorithm to calculate the objective function and obtain the optimal tracking control law.…”

Section: Introductionmentioning

confidence: 99%

Improved SSA‐RBF neural network‐based dynamic 3‐D trajectory tracking model predictive control of autonomous underwater vehicles with external disturbances

Bao,

Zhu,

Liu

2023

Optim Control Appl Methods

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This paper studies the three‐dimensional (3‐D) dynamic trajectory tracking control of an autonomous underwater vehicle (AUV). As AUV is a typical nonlinear system, each degree of freedom is strongly coupled, so the traditional control method based on the nominal model of AUV cannot guarantee the accuracy of the control system. To solve this problem, we first propose a prediction model based on a radial basis function neural network (RBF‐NN). The nonlinearity of AUV is learned and modeled offline by RBF‐NN based on previous data. This model can reflect the time sequence state and control variables of AUV. Secondly, to avoid the overfitting problem in network training based on the traditional gradient descent method, a new adaptive chaotic sparrow search algorithm (ACSSA) is proposed to optimize the network parameters, to improve the full approximation ability of RBF‐NN to nonlinear systems. To eliminate the steady‐state error caused by external interference during AUV trajectory tracking, a nonlinear optimizer is designed by updating the deviation of the NN model output layer. In each sampling period, the predictive control law is calculated online according to the deviation between the predicted value and the actual value. In addition, the stability analysis based on the Lyapunov method proves the asymptotic stability of the controller. Finally, the 3‐D dynamic trajectory tracking the performance of AUV under different external disturbances is verified by MATLAB/Simulink, and the results show that the proposed controller is more efficient and robust than the standard model predictive controller (MPC) controller and the standard NN model predictive controller (NNPC).

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“…Such uncertainty can be addressed with good state estimation [Steenson et al, 2014], with additional safety and robustness constraints [Nikou et al, 2021], or by extending the MPC to hybrid automata (including state machines or behavior trees) [Gomes and Pereira, 2019]. To the best of our knowledge, there have been no real-time MPC implementations for agile underactuated AUVs.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Model Predictive Control for Hydrobatics: Experiments with an Underactuated AUV

Bhat

Panteli

Stenius

et al. 2022

Preprint

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Hydrobatic Autonomous Underwater Vehicles (AUVs) can be efficient in range and speed, as well as agile in maneuvering. They can be beneficial in scenarios such as obstacle-avoidance, inspections, docking, and under-ice operations. However, such AUVs are underactuated systems - this means exploiting the system dynamics is key to achieving elegant hydrobatic maneuvers with minimum controls. This paper explores the use of Model Predictive Control (MPC) techniques to control underactuated AUVs in hydrobatic maneuvers and presents new simulation and experimental results with the small and hydrobatic SAM AUV. Simulations are performed using nonlinear MPC (NMPC) on the full AUV system to provide optimal control policies for several hydrobatic maneuvers in Matlab/Simulink. For implementation on AUV hardware in ROS, a linear time varying MPC (LTV-MPC) is derived from the nonlinear model to enable real-time control. In simulations, NMPC and LTV-MPC shows promising results to offer much more efficient control strategies than what can be obtained with PID and LQR based controllers in terms of rise-time, overshoot, steady-state error and robustness. The LTV-MPC shows satisfactory real-time performance in experimental validation. The paper further also demonstrates experimentally that LTV-MPC can be run real-time on the AUV in performing hydrobatic maneouvers.

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A robust non‐linear MPC framework for control of underwater vehicle manipulator systems under high‐level tasks

Cited by 18 publications

References 24 publications

Model predictive control of active yaw damper for high-speed trains

Model predictive control of active yaw damper for high-speed trains

Improved SSA‐RBF neural network‐based dynamic 3‐D trajectory tracking model predictive control of autonomous underwater vehicles with external disturbances

Nonlinear Model Predictive Control for Hydrobatics: Experiments with an Underactuated AUV

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