Optimize motion energy of AUV based on LQR control strategy

Shi-kai, Wang; Huang, Jin; Meng, Lexuan; Guicang, Li

doi:10.1109/chicc.2016.7554068

Cited by 7 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2021, Jia et al [6] retrained the controller by using an ANN, which improved the accuracy of the output and solved the problem of yaw deviation during berthing. Wang et al [7] designed the Linear Quadratic Regulator (LQR) controller combined with a GA, which reduced the additional resistance of the system caused by pitch and heave motion.…”

Section: Introductionmentioning

confidence: 99%

Ship Autonomous Berthing Simulation Based on Covariance Matrix Adaptation Evolution Strategy

Chen

Yin

Shuzi

2023

JMSE

View full text Add to dashboard Cite

Existing research on auto-berthing of ships has mainly focused on the design and implementation of controllers for automatic berthing. For the real automatic docking processes, not only do external environmental perturbations need to be taken into account but also motion paths, docking strategies and ship mechanical constraints, which are important influential factors to measure autonomous docking methods. Through a literature review of ship path planning and motion control for automatic berthing, it is found that many studies ignore the interference of the actual navigational environment, especially for ships sailing at slow speed when berthing, or do not consider the physical constraints of the steering gear and the main engine. In this paper, we propose a hybrid approach for autonomous berthing control systems based on a Linear Quadratic Regulator (LQR) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which systematically addresses the problems involved in the berthing process, such as path planning, optimal control, adaptive berthing strategies, dynamic environmental perturbations and physically enforced structural constraints. The berthing control system based on the LQR and modified LQR-CMA-ES have been validated by simulation work. The simulation results show that the proposed method is able to achieve the automatic docking of the ship well and the system is robust and well adapted to environmental disturbances at slow speed when docking.

show abstract

Section: Introductionmentioning

confidence: 99%

Ship Autonomous Berthing Simulation Based on Covariance Matrix Adaptation Evolution Strategy

Chen

Yin

Shuzi

2023

JMSE

View full text Add to dashboard Cite

show abstract

“…Nowadays, many control methods have achieved high tracking accuracy, such as sliding mode control [11,12], neural network control [13,14], and fuzzy control [15,16]. In addition, some published articles, such as [17,18,19,20], pay attention to the energy consumption optimization of AUVs by improving the control method. In [17,18], an energy suboptimizer block based on the Euler–Lagrange equation is introduced into the cost function to address the issue of energy optimization.…”

Section: Introductionmentioning

confidence: 99%

“…The square tracking error term is used to penalize deviations of the actual trajectory from the desired trajectory, and the input and input changes terms are used to minimize the thruster use to optimize energy consumption. An improved linear quadratic regulator (LQR) is developed in [20], which chooses a cost function composed of a square tracking error and a square input. Energy consumption is reduced by optimizing the weight matrices with the genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Energy Consumption of Depth Tracking Control Based on Model Predictive Control for Autonomous Underwater Vehicles

Yao

Yang

Zhang

et al. 2019

Sensors

View full text Add to dashboard Cite

For long-term missions in complex seas, the onboard energy resources of autonomous underwater vehicles (AUVs) are limited. Thus, energy consumption reduction is an important aspect of the study of AUVs. This paper addresses energy consumption reduction using model predictive control (MPC) based on the state space model of AUVs for trajectory tracking control. Unlike the previous approaches, which use a cost function that consists of quadratic deviations of the predicted controlled output from the reference trajectory and quadratic input changes, a term of quadratic energy (i.e., quadratic input) is introduced into the cost function in this paper. Then, the MPC control law with the new cost function is constructed, and an analysis on the effect of the quadratic energy term on the stability is given. Finally, simulation results for depth tracking control are given to demonstrate the feasibility and effectiveness of the improved MPC on energy consumption optimization for AUVs.

show abstract

“…The working mechanism of the AUV is similar to the DSPB, so related researches of the AUV can be referenced in this paper. The energy consumption optimization of AUVs are mainly from optimizing parameters such as dynamic resistance model, gliding pitch angle and speed, the force applied by thrusters and their opening times [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Energy Consumption Analysis and Optimization of the Deep-Sea Self-Sustaining Profile Buoy

Liu

Yang

et al. 2019

Energies

View full text Add to dashboard Cite

In order to reduce the energy consumption of deep-sea self-sustaining profile buoy (DSPB) and extend its running time, a stage quantitative oil draining control mode has been proposed in this paper. System parameters have been investigated including oil discharge resolution (ODR), judgment threshold of the floating speed and frequency of oil draining on the energy consumption of the system. The single-objective optimization model with the total energy consumption of DSPB’s ascent stage as the objective function has been established by combining the DSPB’s floating kinematic model. At the same time, as the static working current of the DSPB can be further optimized, a multi-objective energy consumption optimization model with the floating time and the energy consumption of the oil pump motor as objective functions has been established. The non-dominated sorted genetic algorithm-II (NSGA-II) has been employed to optimized the energy consumption model in the ascent stage of the DSPB. The results showed that the NSGA-II method has a good performance in the energy consumption optimization of the DSPB, and can reduce the dynamic energy consumption in the floating process by 28.9% within 2 h considering the increase in static energy consumption.

show abstract

Optimize motion energy of AUV based on LQR control strategy

Cited by 7 publications

References 7 publications

Ship Autonomous Berthing Simulation Based on Covariance Matrix Adaptation Evolution Strategy

Ship Autonomous Berthing Simulation Based on Covariance Matrix Adaptation Evolution Strategy

Optimization of the Energy Consumption of Depth Tracking Control Based on Model Predictive Control for Autonomous Underwater Vehicles

Energy Consumption Analysis and Optimization of the Deep-Sea Self-Sustaining Profile Buoy

Contact Info

Product

Resources

About