An evolutionary performance-enhanced PSO approach by using a BP neural-learning-based PID controller

Lu, Yongzhong; Levy, David; Zhang, Jingyu; Li, Dahai

doi:10.1080/09540091.2011.573070

Cited by 13 publications

(4 citation statements)

References 15 publications

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“…It is usually used for the optimization of continuous nonlinear systems. Since PSO uses a simple swarm emulating mechanism to guide the particles to search for globally optimal solutions and performs easily, it has succeed in solving many real-world optimization problems [ 27 – 42 ].…”

Section: Analysis and Methodsmentioning

confidence: 99%

Parameter Identification of Robot Manipulators: A Heuristic Particle Swarm Search Approach

Yan

Levy

2015

PLoS ONE

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Parameter identification of robot manipulators is an indispensable pivotal process of achieving accurate dynamic robot models. Since these kinetic models are highly nonlinear, it is not easy to tackle the matter of identifying their parameters. To solve the difficulty effectively, we herewith present an intelligent approach, namely, a heuristic particle swarm optimization (PSO) algorithm, which we call the elitist learning strategy (ELS) and proportional integral derivative (PID) controller hybridized PSO approach (ELPIDSO). A specified PID controller is designed to improve particles’ local and global positions information together with ELS. Parameter identification of robot manipulators is conducted for performance evaluation of our proposed approach. Experimental results clearly indicate the following findings: Compared with standard PSO (SPSO) algorithm, ELPIDSO has improved a lot. It not only enhances the diversity of the swarm, but also features better search effectiveness and efficiency in solving practical optimization problems. Accordingly, ELPIDSO is superior to least squares (LS) method, genetic algorithm (GA), and SPSO algorithm in estimating the parameters of the kinetic models of robot manipulators.

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Section: Analysis and Methodsmentioning

confidence: 99%

Parameter Identification of Robot Manipulators: A Heuristic Particle Swarm Search Approach

Yan

Levy

2015

PLoS ONE

Self Cite

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“…The structure of the three-layer BP neural network (Lu et al, 2011) designed in this paper is 3-4-2, as shown in Figure 2.…”

Section: Feedback Rate Coefficient Adjustmentmentioning

confidence: 99%

“…Compared with the BP-PID control method proposed by Lu et al (2011), the results are shown in Figure 3 to 5 and Table 2. Figure 4 shows system control parameter varying curves of BP-PID and BP-ADRC, respectively.…”

Section: Simulation and Analysismentioning

confidence: 99%

Adaptive control of missile attitude based on BP–ADRC

Qiao

Meng

Ke³

et al. 2020

AEAT

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Purpose To improve the robustness of missile control system and reduce the error, a missile attitude adaptive control method based on active disturbance rejection control technology (ADRC) and BP neural network is innovatively proposed. Design/methodology/approach ADRC improves the performance of the missile control system by estimating and eliminating the total disturbance of the system. BP neural network adjusts the parameters of ADRC controller according to the state of the system to realize adaptive control. Based on the control system and missile dynamics model, the convergence analysis of the extended state observer and the stability analysis of the closed-loop system after embedding BP neural network are given. Findings The simulation results show that the adaptive control method can adjust the coefficient of error feedback rate according to the system input, output and error change rate, which accelerates the response speed of missile attitude angle and reduces the attitude angle error. Practical implications BP–ADRC further improves the robustness and environmental adaptability of the missile control system. The BP–ADRC control method proposed in this paper is proved feasible. Originality/value Different from the traditional ADRC, the BP–ADRC feedback signal proposed in this paper uses the output signal and its rate of the closed-loop system instead of the system state quantity estimated by extended state observer (ESO). This innovative method combined with BP neural network can make the system output meet the requirements when ESO has errors in the estimation of missile dynamics model.

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“…Traditionally, after writing the codes, the programmer has to generate the test cases for program and then to execute all test cases. During execution of all test cases, some test cases fail and the developer has to manually search for the bug by applying different debugging techniques (Desikan and Ramesh, 2006;Lu et al, 2011). Manual debugging becomes very tedious task even for a moderate size program (Liu et al, 2006;Gu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A dynamic slicing-based approach for effective SBFL technique

Ghosh

Singh

2021

IJCSE

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Fault finding is an activity to locate the fault or bug present in a software. It is a very time taking job and needs much more effort if done manually. Hence, automated fault localisation is always in high demand which reduces the human effort and also makes the task more accurate. Among different existing debugging techniques, spectrum-based debugging is the most efficient for automated fault localisation. Dynamic program slicing is another technique which can reduce the debugging time by reducing the unaffected source codes depending on slicing criteria. In this paper, we present a spectrum-based fault localisation technique by using dynamic slicing. Context-sensitive slicing is used to diminish fault localisation time and makes the process more effective. SBFL metrics are used in the sliced program to find the suspiciousness score of individual program statements. The efficiency of the proposed approach is evaluated on three open-source programs. From results, we notice that due to dynamic slicing the technique takes less time to find the suspiciousness score of individual statements in the sliced program compared to original program. We have also observed that the programmer needs to inspect less source code to detect the buggy statement. The result says that the proposed approach outperforms the pure spectrum-based fault localisation techniques.

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An evolutionary performance-enhanced PSO approach by using a BP neural-learning-based PID controller

Cited by 13 publications

References 15 publications

Parameter Identification of Robot Manipulators: A Heuristic Particle Swarm Search Approach

Parameter Identification of Robot Manipulators: A Heuristic Particle Swarm Search Approach

Adaptive control of missile attitude based on BP–ADRC

A dynamic slicing-based approach for effective SBFL technique

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