Autonomous Underwater Vehicle Motion Planning via Sampling Based Model Predictive Control

2014 ◽  
Vol 670-671 ◽  
pp. 1370-1377 ◽  
Author(s):  
Lin Lin Wang ◽  
Hong Jian Wang ◽  
Li Xin Pan
Keyword(s):  
Model Predictive Control ◽  
Motion Planning ◽  
Predictive Control ◽  
Autonomous Underwater Vehicle ◽  
Angular Rate ◽  
Control Variable ◽  
Underwater Vehicle ◽  
Planning Optimization ◽  
Sampling Points ◽  
Rolling Optimization

In order to improve the ability of independent planning for AUV (Autonomous Underwater Vehicle), a new method of motion planning based on SBMPC (Sampling Based Model Predictive Control) is proposed, which is combined with model predictive control theory. Input sampling is directly made in control variable space, and sampling data is substituted into the predictive model of AUV motion. Then surge velocity and yaw angular rate in next sampling time are obtained through calculations. If predictive states are evaluated according to the performance index previously defined, optimal prediction of AUV states in next sampling can be used to realize motion planning optimization. Effects of three sampling methods (viz. uniform sampling, Halton sampling and CVT sampling) on motion planning performance are also compared in simulations. Statistical analysis demonstrates that CVT sampling points has the most uniform coverage in two-dimensional plane when amount of sampling points is the same for three methods. Simulation results show that it is effective and feasible to plan a route for AUV by using CVT sampling and rolling optimization of MPC (Model Predictive Control).

scholarly journals Robust Model Predictive Control for Autonomous Underwater Vehicle – Manipulator System with Fuzzy Compensator

2019 ◽  
Vol 26 (2) ◽  
pp. 104-114 ◽  
Author(s):  
Hossein Nejatbakhsh Esfahani
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Control Approach ◽  
Robust Model Predictive Control ◽  
Robust Model ◽  
Uncertainty Estimator ◽  
Fuzzy Compensator ◽  
Improved Model

Abstract This paper proposes an improved Model Predictive Control (MPC) approach including a fuzzy compensator in order to track desired trajectories of autonomous Underwater Vehicle Manipulator Systems (UVMS). The tracking performance can be affected by robot dynamical model uncertainties and applied external disturbances. Nevertheless, the MPC as a known proficient nonlinear control approach should be improved by the uncertainty estimator and disturbance compensator particularly in high nonlinear circumstances such as underwater environment in which operation of the UVMS is extremely impressed by added nonlinear terms to its model. In this research, a new methodology is proposed to promote robustness virtue of MPC that is done by designing a fuzzy compensator based on the uncertainty and disturbance estimation in order to reduce or even omit undesired effects of these perturbations. The proposed control design is compared with conventional MPC control approach to confirm the superiority of the proposed approach in terms of robustness against uncertainties, guaranteed stability and precision.

scholarly journals Aplicación de Muestreo basado en Modelos de Control Predictivo a un Vehículo Autónomo Subacuático

2010 ◽  
Vol 4 (7) ◽  
pp. 55
Author(s):  
Charmane V. Caldwell ◽  
Damion D. Dunlap ◽  
Emmanuel G. Collins
Keyword(s):  
Model Predictive Control ◽  
Motion Planning ◽  
Predictive Control ◽  
Planning Process ◽  
Autonomous Underwater Vehicle ◽  
Kinematic Model ◽  
Local Minima ◽  
Cluttered Environments ◽  
Unmanned Underwater Vehicles ◽  
Planning And Control

Unmanned Underwater Vehicles (UUVs) can be utilized to perform difficult tasks in cluttered environments such as harbor and port protection. However, since UUVs have nonlinear and highly coupled dynamics, motion planning and control can be difficult when completing complex tasks. Introducing models into the motion planning process can produce paths the vehicle can feasibly traverse. As a result, Sampling-Based Model Predictive Control (SBMPC) is proposed to simultaneously generate control inputs and system trajectories for an autonomous underwater vehicle (AUV). The algorithm combines the benefits of sampling-based motion planning with model predictive control (MPC) while avoiding some of the major pitfalls facing both traditional sampling-based planning algorithms and traditional MPC. The method is based on sampling (i.e., discretizing) the input space at each sample period and implementing a goal-directed optimization (e.g., A*) in place of standard numerical optimization. This formulation of MPC readily applies to nonlinear systems and avoids the local minima which can cause a vehicle to become immobilized behind obstacles. The SBMPC algorithm is applied to an AUV in a 2D cluttered environment and an AUV in a common local minima problem. The algorithm is then used on a full kinematic model to demonstrate the benefits.

scholarly journals Experimental Evaluation on Depth Control Using Improved Model Predictive Control for Autonomous Underwater Vehicle (AUVs)

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2321 ◽  
Author(s):  
Feng Yao ◽  
Chao Yang ◽  
Xing Liu ◽  
Mingjun Zhang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Autonomous Underwater Vehicle ◽  
Absolute Error ◽  
Underwater Vehicle ◽  
Settling Time ◽  
Experimental Results ◽  
Current Time ◽  
Weighting Matrix ◽  
Depth Control

Due to the growing interest using model predictive control (MPC), there are more and more researches about the applications of MPC on autonomous underwater vehicle (AUV), and these researches are mainly focused on simulation and simple application of MPC on AUV. This paper focuses on the improvement of MPC based on the state space model of an AUV. Unlike the previous approaches using a fixed weighting matrix, in this paper, a coefficient, varied with the error, is introduced to adjust the control increment vector weighting matrix to reduce the settling time. Then, an analysis on the effect of the adjustment to the stability is given. In addition, there is always a lag between the AUV real trajectory and the desired trajectory when the AUV tracks a continuous trajectory. To solve this problem, a simple re-planning of the desired trajectory is developed. Specifically, the point certain steps ahead from current time on the desired trajectory is treated as the current desired point and input to the controller. Finally, experimental results for depth control are given to demonstrate the feasibility and effectiveness of the improved MPC. Experimental results show that the method of real-time adjusting control increment weighting matrix can reduce settling time by about 2 s when tracking step trajectory of 1 m, and the simple re-planning of the desired trajectory method can reduce the average of absolute error by about 15% and standard deviation of error by about 17%.

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