A new adaptive sliding mode controller based on the RBF neural network for an electro-hydraulic servo system

ISA Transactions, Год журнала: 2022, Номер 129, С. 472 - 484

Опубликована: Янв. 10, 2022

Язык: Английский

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A review for control theory and condition monitoring on construction robots

Journal of Field Robotics, Год журнала: 2023, Номер 40(4), С. 934 - 954

Опубликована: Янв. 20, 2023

Abstract The application of robotic technologies in building construction leads to great convenience and productivity improvement, robots (CRs) bring enormous opportunities for the way we conduct design construction. To get a better understanding trends track CRs on‐site conditions, this paper conducts systematic review control models status monitoring CRs, which are two key aspects that determine accuracy efficiency. Control flexibility primary needs applied different scenes, so methods based on driving vitally important. Status contains knowledge fault detection, intelligence maintenance, fault‐tolerant control, multiple objectives need be met optimized whole drive chain. Moreover, state‐of‐the‐art is comprehensively summarized, new insights also provided carry promising researches.

Язык: Английский

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Optimal Nonlinear PID TSK3DCMAC Controller Based on Balancing Composite Motion Optimization for Ballbot with External Forces

ISA Transactions, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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Particle Swarm Optimization (PSO) Tuning of PID Control on DC Motor

International Journal of Robotics and Control Systems, Год журнала: 2022, Номер 2(2), С. 435 - 447

Опубликована: Июль 6, 2022

The use of DC motors is now common because its advantages and has become an important necessity in helping human activities. Generally, motor control designed with PID control. main problem that often discussed parameter tuning, namely determining the value Kp, Ki, Kd parameters order to obtain optimal system performance. In this study, one method for tuning on a will be used, Particle Swarm Optimization (PSO) method. Parameter optimization using PSO stable results compared other methods. controller MATLAB Simulink obtained where Kp = 8.9099, K 2.1469, 0.31952 rise time 0.0740, settling 0.1361 overshoot 0. Then hardware testing by entering Arduino IDE software produce speed response 1.4551, Ki= 1.3079, 0.80271 4.3296, 7.3333 1.

Язык: Английский

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Design and Application of PLC-based Speed Control for DC Motor Using PID with Identification System and MATLAB Tuner

International Journal of Robotics and Control Systems, Год журнала: 2023, Номер 3(2), С. 233 - 244

Опубликована: Апрель 7, 2023

Industries use numerous drives and actuators, including DC motors. Due to the wide-ranged adjustable speed, motor is widely used in many industries. However, prone external disturbance parameter changes, causing its speed be unstable. Thus, a requires an appropriate controller design obtain fast stable with small steady-state error. In this study, was designed based on PID control method, gains tuned by trial-and-error MATLAB Tuner identification system. The proposed implemented using PLC OMRON CP1E NA20DRA hardware implementation. Each tuning method repeated five times so that system performances could compared improved. Based implementation results, trial-error gave acceptable results but had errors. On other hand, of provided responses no error still oscillations high overshoot during transition. Therefore, acquired from must finely get better responses.

Язык: Английский

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A hybrid particle swarm optimization algorithm for solving engineering problem

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Апрель 10, 2024

Abstract To overcome the disadvantages of premature convergence and easy trapping into local optimum solutions, this paper proposes an improved particle swarm optimization algorithm (named NDWPSO algorithm) based on multiple hybrid strategies. Firstly, elite opposition-based learning method is utilized to initialize position matrix. Secondly, dynamic inertial weight parameters are given improve global search speed in early iterative phase. Thirdly, a new optimal jump-out strategy proposed "premature" problem. Finally, applies spiral shrinkage from whale (WOA) Differential Evolution (DE) mutation later iteration accelerate speed. The further compared with other 8 well-known nature-inspired algorithms (3 PSO variants 5 intelligent algorithms) 23 benchmark test functions three practical engineering problems. Simulation results prove that obtains better for all 49 sets data than 3 variants. Compared algorithms, 69.2%, 84.6%, 84.6% best function ( $${f}_{1}-{f}_{13}$$ f 1 - 13 ) kinds dimensional spaces (Dim = 30,50,100) 80% solutions 10 fixed-multimodal functions. Also, design obtained by classical

Язык: Английский

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PID Control Optimization Based on MDE-RBF Hybrid Algorithm and Its Application in Controlling Vibrations in Avionics

Journal of Vibration Engineering & Technologies, Год журнала: 2025, Номер 13(1)

Опубликована: Янв. 1, 2025

Язык: Английский

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Prediction and analysis of key parameters of head deformation of hot-rolled plates based on artificial neural networks

Journal of Manufacturing Processes, Год журнала: 2022, Номер 77, С. 282 - 300

Опубликована: Март 25, 2022

Язык: Английский

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PID Controller for PMSM Speed Control Based on Improved Quantum Genetic Algorithm Optimization

IEEE Access, Год журнала: 2023, Номер 11, С. 61091 - 61102

Опубликована: Янв. 1, 2023

When traditional proportional integral and differential controllers are applied to speed control in permanent magnet synchronous motors(PMSM), their coefficients basically determined based on experience, which inevitably leads unsatisfactory results when using this parameter the stability of motors. Therefore, paper proposes an improved quantum genetic algorithm states as basic unit. Utilizing properties for global optimization optimize control, improving rotation angle state particles through idea velocity changes particle swarm optimization(PSO), introducing adaptive weight changes, Hadamard gates replace mutation strategies, incorporating disaster mechanisms. In addition, uses four test functions find minimum value, thereby verifying that our has better performance iteration compared other algorithms, providing initial basis next step application PID optimization. Prove method can solve problem algorithms falling into local optima due improper selection, crossover, methods, cannot effectively motor speed. Finally, Matlab2018a simulation compare with show values achieve oscillation, overshoot, faster target

Язык: Английский

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Research and Design of Hybrid Optimized Backpropagation (BP) Neural Network PID Algorithm for Integrated Water and Fertilizer Precision Fertilization Control System for Field Crops

Agronomy, Год журнала: 2023, Номер 13(5), С. 1423 - 1423

Опубликована: Май 21, 2023

China’s field crops such as cotton, wheat, and tomato have been produced on a large scale, but their cultivation process still adopts more traditional manual fertilization methods, which makes the use of chemical fertilizers in China high causes waste fertilizer resources ecological environmental damage. To address above problems, hybrid optimization genetic algorithms particle swarm (GA–PSO) is used to optimize initial weights backpropagation (BP) neural network, optimization-based BP network PID controller designed realize accurate control flow integrated water precision system for crops. At same time, STM32 microcontroller-based application was developed performance verified experimentally. The results show that has an average maximum overshoot 5.1% adjustment time 68.99 s, better than based (BP–PID) controllers; among them, algorithm by algorithm(GA–PSO–BP–PID) best-integrated when rate 0.6m3/h.

Язык: Английский

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