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Optimal Recursive Terminal Sliding-Mode Control Using Super-Twisting algorithm for Improving High Efficiency and Reliability of Pump Systems

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IEEE CONTROL SYSTEMS LETTERS 2023年 7卷 3385-3390页

作者： Nassiri, Samir Labbadi, Moussa Chatri, Chakib Cherkaoui, Mohamed Mohammed V Univ Rabat Mohammadia Sch Engineers Engn Smart & Sustainable Syst Res Ctr Rabat 10090 Morocco Aix Marseille Univ CNRS UMR 7020 LIS F-13000 Marseille France

In this letter, we propose an optimal recursive terminal sliding-mode control (ORTSMC) combined with super-twisting algorithm (STA) for a pump system under uncertainties. The main objective of the approach developed is to ensure rapid convergence of the pumping system with minimal power losses. To calculate the optimal input parameters of the pump system, a quantum particle swarm optimization algorithm (QPSO) is used. Next, we introduce a non-linear sliding variable into the cost function of the linear quadratic regulator (LQR). This proposal, along with the ORTSM manifold, aims to achieve fast convergence, dynamic stability, and minimize energy consumption. Additionally, the STA is employed to enhance performance during the reaching phase and reduce the chattering problem. The stability of the closed-loop control system is guaranteed using the Lyapunov theory. Finally, we conduct a comparative simulation analysis with two existing control schemes to demonstrate the superiority and effectiveness of our proposed control strategy.

关键词： Convergence Sliding mode control Robustness Mathematical models Uncertainty Steady-state Pumps quantum particle swarm optimization algorithm linear quadratic regulator

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Multi-objective QPSO for short-term load forecast based on diagonal recursive neural network

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2021年第5期21卷 1113-1124页

作者： Zhang, Lan Xu, Lei Xian Aeronaut Polytech Inst Xian Shaanxi Peoples R China

The short-term load forecast is an important part of power system operation, which is usually a nonlinear problem. The processing of load forecast data and the selection of forecasting methods are particularly important. In order to get accurate and effective prediction for power system load, this article proposes a hybrid multi-objective quantum particle swarm optimization (QPSO) algorithm for short-term load forecast of power system based on diagonal recursive neural network. Firstly, a multi-objective mathematical model for short-term load forecast is proposed. Secondly, the discrete particle swarm optimization (PSO) algorithm is used to select the characteristics of load data and screen out the appropriate data. Finally, the hybrid multi-objective QPSO algorithm is used to train diagonal recursive neural network. The experimental results show that the hybrid multi-objective QPSO for short-term load forecast based on diagonal recursive neural network is effective.

关键词： Multi-objective optimization problem diagonal recursive neural network quantum particle swarm optimization algorithm load forecast

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A quantum particle Swam optimization algorithm with Available Transfer Capability

A Quantum Particle Swam Optimization Algorithm with Availabl...

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第十九届分布式计算及其应用国际学术研讨会

作者： Qu Liping Li Dongheng Meng Yan XUE Hai-bo Engineering Training Center of Beihua University Beihua University State Grid Jilin Electric Power Co. Ltd.Shulan power supply company Department of Electrical Engineering Jilin Institute of industrial technology Peoples Government of Jilin City

quantum particle swarm algorithm integrated the quantum behavior with particle swarm optimization algorithm,is used to settle the majorization question of calculating available transmission *** by using the software of Matlab to IEEE-30 bus system as an example of the simulation,after comparing the simulation results with the traditional particle swarm optimization algorithm results,we dissected the optimization performance and convergence speed of the above two algorithms,and verify the effectiveness of quantum particle swarm algorithm to settle the majorization question of the available transmission capability.

关键词： available transmission capacity particle swarm optimization algorithm quantum particle swarm optimization algorithm

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Simulation Research of the QoS Multicast Routing in WSN Based on QPSO algorithm

Simulation Research of the QoS Multicast Routing in WSN Base...

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3rd International Symposium on Intelligent Information Technology Application

作者： Huang Yourui Tian Yiming Tang Chaoli Anhui Univ Sci & Technol Sch Elect & Informat Engn Huainan 232001 Anhui Peoples R China

ISBN: (纸本)9780769538594

The energy constraint bottleneck in the wireless sensor networks (WSN) exist objectively. To reduce energy consumption and ensure the Quality of Service (QoS) in data transmission, a method of solving the QoS multicast routing in WSN by quantum particle swarm optimization algorithm (QPSO). The algorithm is based on PSO, quantum-bit, principle of superposition and quantum gates. By the premise of avoiding local convergence and reducing the energy consumption, it can solving the QoS multicast routing rapidly, which help to prolong the network lifetime efficiently. The simulation results indicate that its feasibility and effectiveness.

关键词： quantum particle swarm optimization algorithm QoS multicast routing energy wireless sensor networks

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Automatic Guided Vehicle Global Path Planning Considering Multi-objective optimization and Speed Control

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SENSORS AND MATERIALS 2021年第6期33卷 1999-2011页

作者： Song, Jing Guangzhou Maritime Univ Coll Port & Shipping Management Guangzhou 510725 Peoples R China

Over the past few decades, as the main tool of intelligent material transportation, automatic guided vehicles (AGVs) have been widely used in modern production systems, logistics, transportation, industry, and commerce to further improve productivity, reduce labor costs, raise energy efficiency, and enhance safety. Path planning is a key issue in the field of AGVs to ensure that they do not collide with obstacles during movement and reach the destination as fast as possible to complete the assigned task. We propose two different and crucial operating environments in this paper. More specifically, in a static environment, a multi-objective mathematical model is established with the shortest path and the maximum smoothness, and the improved Levy random quantum particle swarm optimization (LRQPSO) algorithm is used to solve the proposed model and screen the AGV's driving path. In a dynamic environment, an inductive steering algorithm (ISA) that considers the movement of obstacles is proposed to make the AGV avoid obstacles rationally. By combining the steering characteristics of the two environments, AGV speed control rules are set and applied to the steering process in complex environments to ensure that the AGV can travel more smoothly and quickly. Simulation results show that the proposed method can ensure the obstacle avoidance and flexible steering of an AGV, and improve the driving speed and work efficiency in the two environments. In addition, compared with the conventional algorithm, the smoothness, operation speed, and work efficiency of the AGV are significantly increased using the improved LRQPSO algorithm and ISA.

关键词： multi-objective optimization global path planning inductive steering algorithm quantum particle swarm optimization algorithm automatic guided vehicle

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Multimedia Teaching Quality Evaluation System in Colleges Based on Genetic algorithm and Social Computing Approach

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IEEE ACCESS 2019年 7卷 183790-183799页

作者： Jian, Qiang Xuchang Univ Sch Marxism Studies Cent Plains Rural Dev Res Ctr Xuchang 461000 Peoples R China

To improve the quality of classroom teaching and understand the effectiveness of teaching in a timely manner, the multimedia teaching quality evaluation system of colleges and universities was established. Combined with genetic algorithm, through the investigation, the evaluation process of multimedia teaching in colleges and the status quo of multimedia teaching were analyzed. Many teachers were interviewed. A set of evaluation indicators suitable for college multimedia teaching quality was discussed, and the indicators were visually displayed with data. From the aspects of multimedia course ware, multimedia classroom teaching process and multimedia teaching effect, teachers' basic information, students' basic information and evaluation relationship management are analyzed. The teaching quality evaluation system of college multimedia was designed and the results were tested. The research results showed that the evaluation of teaching quality was realized through the teaching evaluation system. Therefore, the system is of great significance to improve the effectiveness of classroom teaching.

关键词： Teaching quality evaluation index evaluation system genetic algorithm multimedia social computing quantum particle swarm optimization algorithm

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Hybrid QPSO-NNIA2 algorithm for Multi-Objective optimization Problem

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2019年第8期33卷

作者： Zhang, Lan Northwestern Polytech Univ Dept Appl Math Xian Shaanxi Peoples R China Xian Aeronaut Polytech Inst Basic Dept Xian Shaanxi Peoples R China

To improve the convergence and distribution of a multi-objective optimization algorithm, a hybrid multi-objective optimization algorithm, based on the quantum particle swarm optimization (QPSO) algorithm and adaptive ranks clone and neighbor list-based immune algorithm (NNIA2), is proposed. The contribution of this work is threefold. First, the vicinity distance was used instead of the crowding distance to update the archived optimal solutions in the QPSO algorithm. The archived optimal solutions are updated and maintained by using the dynamic vicinity distance based m-nearest neighbor list in the QPSO algorithm. Secondly, an adaptive dynamic threshold of unfitness function for constraint handling is introduced in the process. It is related to the evolution algebra and the feasible solution. Thirdly, a new metric called the distribution metric is proposed to depict the diversity and distribution of the Pareto optimal. In order to verify the validity and feasibility of the QPSO-NNIA2 algorithm, we compare it with the QPSO, NNIA2, NSGA-II, MOEA/D, and SPEA2 algorithms in solving unconstrained and constrained multi-objective problems. The simulation results show that the QPSO-NNIA2 algorithm achieves superior convergence and superior performance by three metrics compared to other algorithms.

关键词： Multi-objective optimization problem Pareto optimal solution vicinity distance quantum particle swarm optimization algorithm particle swarm optimization algorithm

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Interference Suppression Control Method for Aircraft Electromechanical Speed Control System

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JORDAN JOURNAL OF MECHANICAL AND INDUSTRIAL ENGINEERING 2022年第1期16卷 19-29页

作者： Zhang, Qian Guangdong Univ Educ Dept Comp Sci Guangzhou 510303 Peoples R China

In order to improve the stability and dynamic performance of the aircraft electromechanical speed control system, the interference suppression control method is proposed. Firstly, the interference factors of the speed regulation system are analyzed in detail, and then the chaotic mutation attractor is introduced into the quantum particle swarm optimization algorithm by using intelligent optimization method. The nonlinear model of the aircraft electromechanical control system is established to realize the tracking and identification of the parameters of the aircraft electromechanical speed regulation system. Accordingly, the PID control method is improved, and the interference suppression of aircraft electromechanical speed regulation system is realized. The simulation results show that the control method has high control accuracy, good dynamic and static characteristics, and also has good performance in resisting disturbance. The purpose of improving the transient stability of power system is realized. (C) 2022 Jordan Journal of Mechanical and Industrial Engineering. All rights reserved

关键词： Aircraft electromechanical Speed control system quantum particle swarm optimization algorithm Interference suppression

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Study on optimization of QPSO algorithm in Beamforming

Study on Optimization of QPSO Algorithm in Beamforming

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Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Ye Tian Jiaqi Wang Longxiang Guo Jingwei Yin College of Underwater Acoustic Engineering Harbin Engineering University Harbin China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Adaptive beamforming is a typical optimization problem, which can be solved by artificial intelligence algorithm. This paper designed a beamforming algorithm based on quantum particle swarm optimization algorithm. According to the characteristics of the beamforming, this paper optimizes the fitness function simply, and beamforms with it. Then, the convergence speed and convergence precision of the algorithm of beamforming is increased. And the practicability of beamforming algorithm based on swarm intelligence algorithm with the fitness function of this paper is improved.

关键词： adaptive beamforming quantum particle swarm optimization algorithm Fitness function Adaptive Beam forming Beamforming algorithms particle swarm optimization health

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