Nonlinear Reduced-Order Observer-Based Predictive Control for Diving of an Autonomous Underwater Vehicle

Yao, Xuliang; Yang, Guangyi; Yu, Pei

doi:10.1155/2017/4394571

Cited by 8 publications

(4 citation statements)

References 36 publications

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“…Herein, N p denotes the prediction horizon, while N c signifies the control horizon. In (14), the explicit forms of parameters and k C are detailed as follows:…”

Section: Design Of Kinematic Controllers For Tiltrotor Uav Based On M...mentioning

confidence: 99%

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Tracking Control Based on Model Predictive and Adaptive Neural Network Sliding Mode of Tiltrotor UAV

Ouyang,

Xu,

2024

FCIS

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As the low-altitude economy rapidly expands, the demand for UAVs is increasingly growing, and their operational scenarios are becoming more complex, with higher requirements for endurance and short-distance take-off and landing performance. Tiltrotor UAVs, characterized by vertical take-off and landing and long endurance, have attracted widespread attention for their potential applications. However, the dynamics and flight paths of tiltrotor UAVs are highly nonlinear, and traditional linear flight controllers cannot fully utilize the real-time performance capabilities of tiltrotor UAVs. Under the conditions of model uncertainty and input saturation in tiltrotor UAVs, traditional LOS+PID control strategies exhibit characteristics of insufficient responsiveness and excessive overshoot. To improve the performance of tiltrotor UAVs in completing path tracking tasks, we have developed a new control strategy. By establishing an error model for three-dimensional space path tracking, we propose a cascaded control strategy of motion controllers and dynamic controllers. The motion controller is designed based on model predictive control, generating a series of speed-limited signals. Then, in the dynamic controller part, an adaptive radial basis function neural network is used to estimate the model uncertainty caused by aerodynamic parameters to enhance its robustness. Finally, the proposed algorithm is compared with the LOS guidance method and PID controller through simulation experiments. The comparison results show that the proposed algorithm can improve the path tracking effect, increase the response speed, and reduce the overshoot.

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“…Herein, N p denotes the prediction horizon, while N c signifies the control horizon. In (14), the explicit forms of parameters and k C are detailed as follows:…”

Section: Design Of Kinematic Controllers For Tiltrotor Uav Based On M...mentioning

confidence: 99%

“…Taking into account the constraints on control deflection and rate, reference [14] introduced a Model Predictive Control (MPC) algorithm to address the issues of depth tracking and attitude control for tiltrotor unmanned aerial vehicles. However, both algorithms are based on nominal models.…”

Section: Introductionmentioning

confidence: 99%

Tracking Control Based on Model Predictive and Adaptive Neural Network Sliding Mode of Tiltrotor UAV

Ouyang,

Xu,

2024

FCIS

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“…AUVs, on the other hand, must rely entirely on their autonomously operated on-board control systems in which the depth controller is implemented. As the majority of the motion of the AUV is performed in the direction of the longitudinal axis of the hull, the obvious method for depth control is to adjust the angles of attack of the vehicle's control surfaces (elevators) while the vehicle is moving at a certain speed by means of its main thrusters (Yao et al, 2017). Using additional vertical thrusters for the depth control of AUVs is less common but also possible (Melo & Matos, 2015).…”

Section: Introductionmentioning

confidence: 99%

Depth control for biomimetic and hybrid unmanned underwater vehicles

Morawski

Malec

2021

Tech.Trans.

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Unmanned underwater vehicles which use biomimetic mechanisms are becoming increasingly useful in the realisation of tasks requiring silent and efficient propulsion. Complex fish kinematics are simplified to some extent and implemented in such vehicles. One of the essential fish behaviours is their ability to adjust their buoyancy using a swim bladder. This paper covers the issues concerning the implementation of artificial swim bladders as well as depth regulators in two underwater vehicles: biomimetic and hybrid. The control of vehicle depth through buoyancy change was examined in the computer simulation and in the experiment. Two types of artificial swim bladder were tested – a rigid cylinder with a piston and an elastic container with a water pump.

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“…In [32], the constraint of the input was considered, and the LOS guidance law was optimized by using MPC algorithm to improve the waypoint tracking quality of an underactuated ship. In [33], the velocity and amplitude constraints of the rudder were considered, and the AUV's attitude and depth control were optimized by using MPC algorithm. The limitation of the aforementioned papers is that the controllers are based on a nominal model.…”

Section: Introductionmentioning

confidence: 99%

Path Following Based on Waypoints and Real-Time Obstacle Avoidance Control of an Autonomous Underwater Vehicle

Yao

Wang

et al. 2020

Sensors

Self Cite

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This paper studies three-dimensional (3D) straight line path following and obstacle avoidance control for an underactuated autonomous underwater vehicle (AUV) without lateral and vertical driving forces. Firstly, the expected angular velocities are designed by using two different methods in the kinematic controller. The first one is a traditional method based on Line-of-sight (LOS) guidance law, and the second one is an improved method based on model predictive control (MPC). At the same time, a penalty item is designed by using the obstacle information detected by onboard sensors, which can realize the real-time obstacle avoidance of the unknown obstacle. Then, in order to overcome the uncertainty of the dynamics model and the saturation of actual control input, the dynamic controller is designed by using sliding mode control (SMC) technology. Finally, in the simulation experiment, the performance of the improved control method is verified by comparison with two traditional control methods based on LOS guidance law. Since the constraint of an AUV’s angular velocities are considered in MPC, simulation results show that the improved control method uses MPC, and SMC not only improves the tracking quality of the AUV when switching paths near the waypoints and realizes real-time obstacle avoidance but also effectively reduces the mean square error (MSE) and saturation rate of the rudder angle. Therefore, this control method is more conducive to the system stability and saves energy.

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Nonlinear Reduced-Order Observer-Based Predictive Control for Diving of an Autonomous Underwater Vehicle

Cited by 8 publications

References 36 publications

Tracking Control Based on Model Predictive and Adaptive Neural Network Sliding Mode of Tiltrotor UAV

Tracking Control Based on Model Predictive and Adaptive Neural Network Sliding Mode of Tiltrotor UAV

Depth control for biomimetic and hybrid unmanned underwater vehicles

Path Following Based on Waypoints and Real-Time Obstacle Avoidance Control of an Autonomous Underwater Vehicle

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