Particle Swarm Optimized Deep Convolutional Neural Sugeno-Takagi Fuzzy PID Controller in Permanent Magnet Synchronous Motor

Raj, F. Vijay Amirtha; Kannan, V. Kamatchi

doi:10.1007/s40815-021-01126-6

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…Raj and Kannan proposed the insertion point at the 4/3 approximation of the distance matrix to study the reoptimization problem of the metric minimum path. In dealing with the reoptimiza-tion problem of the metric maximum path, they proposed the optimization algorithm of the insertion point at the 4/5 approximation of the distance matrix to optimize the path planning problem [8]. Ramaraju et al proposed a hybrid particle swarm optimization algorithm to embed the championship selection method in evolutionary computing into PSO [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Improved Particle Swarm Optimization Algorithm in Logistics Energy-Saving Picking Information Network

2022

Wireless Communications and Mobile Computing

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In order to solve the logistics optimization problem, an application method of the improved particle swarm optimization algorithm in logistics energy-saving pickup information network is proposed. Firstly, a mathematical model of logistics cycle picking information scheduling optimization is established, logistics and picking paths are encoded as particles, and the optimal logistics cycle picking optimization scheme is found through the cooperation between particles. Secondly, the deficiencies of the particle swarm optimization algorithm are improved accordingly. In order to test the performance of the IPSO algorithm in solving the logistics circulation picking problem, in the simulation environment of P42 core, 2.6 GHz CPU, 4 GB memory, and Windows XP, the simulation experiment was carried out using VC++6.0 programming operating system. The particle number of the IPSO algorithm is 20, ω max = 5 , ω max = 1 . The experimental results show that the improved particle swarm optimization algorithm can effectively bypass the premature convergence of the traditional particle swarm optimization algorithm and ensure that the optimal solution is searched in the global scope, and the optimal probabilistic solution is obtained, which is better than other scheduling algorithms, with more obvious advantages.

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Section: Literature Reviewmentioning

confidence: 99%

Application of Improved Particle Swarm Optimization Algorithm in Logistics Energy-Saving Picking Information Network

2022

Wireless Communications and Mobile Computing

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“…According to [ 12 ], the following PID controller was chosen with

based on Remark 10 and trial and error, the design parameters are chosen to guarantee the tracking error small enough:

.…”

Section: Simulation and Comparison Resultsmentioning

confidence: 99%

“…Despite dynamic surface control methods [ 7 , 8 , 9 ], that can reduce computing efforts, various nonlinear factors such as time delays, external disturbances, and physical constraints are ubiquitous in real industrial scenarios [ 10 , 11 ], which may diminish the controlling precision of the PMSM systems. Researchers have proposed proportional integral derivative (PID) control [ 12 ], neural network (NN) [ 5 ], time delay control [ 13 , 14 ], disturbance observer (DO) [ 15 , 16 ], and constraint control [ 17 , 18 ] methods for different nonlinearities to reach satisfying control results. Hence, the key point is how to design an effective controller to address the various nonlinear uncertainties such as unknown functions, mismatched disturbance, state constraints, and time delays.…”

Section: Introductionmentioning

confidence: 99%

Neural Adaptive Funnel Dynamic Surface Control with Disturbance-Observer for the PMSM with Time Delays

Zhang

et al. 2022

Entropy

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This paper suggests an adaptive funnel dynamic surface control method with a disturbance observer for the permanent magnet synchronous motor with time delays. An improved prescribed performance function is integrated with a modified funnel variable at the beginning of the controller design to coordinate the permanent magnet synchronous motor with the output constrained into an unconstrained one, which has a faster convergence rate than ordinary barrier Lyapunov functions. Then, the specific controller is devised by the dynamic surface control technique with first-order filters to the unconstrained system. Therein, a disturbance-observer and the radial basis function neural networks are introduced to estimate unmatched disturbances and multiple unknown nonlinearities, respectively. Several Lyapunov-Krasovskii functionals are constructed to make up for time delays, enhancing control performance. The first-order filters are implemented to overcome the “complexity explosion” caused by general backstepping methods. Additionally, the boundedness and binding ranges of all the signals are ensured through the detailed stability analysis. Ultimately, simulation results and comparison experiments confirm the superiority of the controller designed in this paper.

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“…The average pooling layer is to extract the average value from the filter, and The max pooling layer is to extract the max value from the filter. The existing studies indicate that the max pooling layer is preferable in model training ( Raj & Kannan, 2022 ).…”

Section: Theoretical Methodsmentioning

confidence: 99%

An enhanced CNN-LSTM remaining useful life prediction model for aircraft engine with attention mechanism

Wang

2022

PeerJ Computer Science

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Remaining useful life (RUL) prediction is one of the key technologies of aircraft prognosis and health management (PHM) which could provide better maintenance decisions. In order to improve the accuracy of aircraft engine RUL prediction under real flight conditions and better meet the needs of PHM system, we put forward an improved CNN-LSTM model based on the convolutional block attention module (CBAM). First, the features of aircraft engine operation data are extracted by multi-layer CNN network, and then the attention mechanism is processed by CBAM in channel and spatial dimensions to find key variables related to RUL. Finally, the hidden relationship between features and service time is learned by LSTM and the predicted RUL is output. Experiments were conducted using C-MPASS dataset. Experimental results indicate that our prediction model has feasibility. Compared with other state-of-the-art methods, the RMSE of our method decreased by 17.4%, and the score of the prediction model was improved by 25.9%.

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Particle Swarm Optimized Deep Convolutional Neural Sugeno-Takagi Fuzzy PID Controller in Permanent Magnet Synchronous Motor

Cited by 13 publications

References 15 publications

Application of Improved Particle Swarm Optimization Algorithm in Logistics Energy-Saving Picking Information Network

Application of Improved Particle Swarm Optimization Algorithm in Logistics Energy-Saving Picking Information Network

Neural Adaptive Funnel Dynamic Surface Control with Disturbance-Observer for the PMSM with Time Delays

An enhanced CNN-LSTM remaining useful life prediction model for aircraft engine with attention mechanism

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