Design of robust control based on linear matrix inequality and a novel hybrid PSO search technique for autonomous underwater vehicle

Bejarbaneh, Elham Yazdani; Masoumnezhad, Mojtaba; Armaghani, Danial Jahed; Pham, Binh Thai

doi:10.1016/j.apor.2020.102231

Cited by 17 publications

(3 citation statements)

References 25 publications

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“…PSO can be combined with other algorithms to improve its performance. Bejarbaneh et al [13] combined PSO with the sine and cosine algorithm (SCA) and levy flight (LF), and found that the new algorithm could find global optimal solutions of different reference functions faster and improve the robustness of the system. Ji et al [14] found a novel algorithm named SAPSO by combining PSO with simulated annealing.…”

Section: Introductionmentioning

confidence: 99%

Seabed Terrain-Aided Navigation Algorithm Based on Combining Artificial Bee Colony and Particle Swarm Optimization

et al. 2023

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Position errors of inertial navigation systems (INS) increase over time after long-term voyages of the autonomous underwater vehicle. Terrain-aided navigation (TAN) can effectively reduce the accumulated error of the INS. However, traditional TAN algorithms require a long positioning time and need better positioning accuracy, and nonmatching and mismatching are prone to occur, especially when the initial position error is large. To solve this problem, a new algorithm combining the artificial bee colony (ABC) and particle swarm optimization (PSO) was proposed according to the principle of terrain matching, to improve the matching effect. Considering that PSO easily falls into a local optimum, the acceleration factor and inertia weight of PSO were improved. The improved PSO was called WAPSO. ABC was introduced based on WAPSO and could help WAPSO escape local optimum. The final algorithm was termed ABC search-based WAPSO (F-WAPSO). During the continuous iteration of particles, F-WAPSO seeks the optimal position for the particles. Simulation tests show that F-WAPSO can effectively improve the matching accuracy. When the initial position error is 1000 m, the matching error can be reduced to 93.5 m, with a matching time of only 13.7 s.

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Section: Introductionmentioning

confidence: 99%

Seabed Terrain-Aided Navigation Algorithm Based on Combining Artificial Bee Colony and Particle Swarm Optimization

et al. 2023

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“…But the performance PSO tuning can be further improved by increasing the number of particles and iterations. To solve the premature convergence and getting stuck in the local minima of the PSO approach, a novel hybrid PSO algorithm, combining Sine Cosine Algorithm (SCA) and Levy Flight (LF) distribution, was used to optimize the gains of the PID depth controller [ 12 ]. The obtained simulation results demonstrate that the proposed PSOSCALF-tuned PID has higher accuracy and a faster convergence rate as compared to the PSO-based PID.…”

Section: Introductionmentioning

confidence: 99%

“…In practical application, too many parameters, unknown tuning range, and direction make it difficult to set parameters and limit the usage of ITCS. However, the tuning methodology of the aforementioned depth controllers highly depends on the operation iteration ability of the system, resulting in long implementation time [ 11 , 12 , 13 ]. In order to solve the problem of repeated adjustments in the parameter setting process of the ITCS depth controller and the possibility of non-optimal parameters, the new tuning technique is employed for the adjustment of key control parameters of the ITCS strategy for DSIB depth-control system.…”

Section: Introductionmentioning

confidence: 99%

Depth-Keeping Control for a Deep-Sea Self-Holding Intelligent Buoy System Based on Inversion Time Constraint Stability Strategy Optimization

Wang

Qiu

et al. 2022

Sensors

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Based on the nonlinear disturbance observer (NDO), the inversion time-constraint stability strategy (ITCS) is designed to make the deep-sea self-holding intelligent buoy (DSIB) system hovered at an appointed depth within a specified time limit. However, it is very challenging to determine the optimal parameters of an ITCS depth controller. Firstly, a genetic algorithm based on quantum theory (QGA) is proposed to obtain the optimal parameter combination by using the individual expression form of quantum bit and the adjustment strategy of quantum rotary gate. To improve the speed and accuracy of global search in the QGA optimization process, taking the number of odd and even evolutions as the best combination point of the genetic and chaos particle swarm algorithm (GACPSO), an ITCS depth controller based on GACPSO strategy is proposed. Besides, the simulations and hardware-in-the-loop system experiments are conducted to examine the effectiveness and feasibility of the proposed QGA–ITCS and GACPSO–ITCS depth controller. The results show that the proposed GACPSO–ITCS depth controller provides higher stability with smaller steady-state error and less settling time in the depth-control process. The research of the proposed method can provide a stable operation condition for the marine sensors carried by the DSIB.

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A hybrid GEP and WOA approach to estimate the optimal penetration rate of TBM in granitic rock mass

Bejarbaneh

Asteris

et al. 2021

Soft Comput

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Design of robust control based on linear matrix inequality and a novel hybrid PSO search technique for autonomous underwater vehicle

Cited by 17 publications

References 25 publications

Seabed Terrain-Aided Navigation Algorithm Based on Combining Artificial Bee Colony and Particle Swarm Optimization

Seabed Terrain-Aided Navigation Algorithm Based on Combining Artificial Bee Colony and Particle Swarm Optimization

Depth-Keeping Control for a Deep-Sea Self-Holding Intelligent Buoy System Based on Inversion Time Constraint Stability Strategy Optimization

A hybrid GEP and WOA approach to estimate the optimal penetration rate of TBM in granitic rock mass

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