A PID-Type Fuzzy Logic Controller-Based Approach for Motion Control Applications

García-Martínez, José R.; Cruz-Miguel, Edson E.; Carrillo-Serrano, Roberto V.; Mendoza-Mondragón, F.; Toledano‐Ayala, Manuel; Rodríguez‐Reséndiz, Juvenal

doi:10.3390/s20185323

Cited by 39 publications

(24 citation statements)

References 42 publications

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“…Then, a fuzzy PID controller is adopted in the control loop. The controller is customized for the thrust system, which requires appropriate modification of the fuzzy PID controller [26][27][28]. Many scholars have studied fuzzy control in various aspects and algorithms [29,30].…”

Section: Design Of Fuzzy Pid Controllermentioning

confidence: 99%

Performance Analysis of Electro-Hydraulic Thrust System of TBM Based on Fuzzy PID Controller

Gong

Chen

et al. 2022

Energies

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The tunnel boring machine (TBM) is widely used in tunnel construction projects. The thrust system plays a crucial role to drive the machine ahead and support gripper shoes stably while tunneling. More and more attention has been paid to the pressure and velocity regulation efficiency as the TBM advances in complex rock conditions to ensure the stabilization of the tunneling process. A thrust hydraulic control system, assembled with a proportional pressure reducing valve, is established with system operating parameters. The mathematical model of the thrust electro-hydraulic system is revealed. To improve the control characteristics of the thrust system, a self-tuning fuzzy PID controller is introduced in the pressure and velocity regulation procedures. After that, tests on a Φ2.5 m scaled TBM test rig are carried out. The test results show that the thrust system adopting the fuzzy PID controller results in less oscillation and a smoother regulation process. It takes less time to reach the target goal of pressure regulation with less vibration during the pressure regenerating periods, and both systems of conventional PID controller and fuzzy PID controller are qualified in velocity regulation movements. The proposed control methods show better benefits in reduction of vibrations and shorter time of regulation to stable conditions, which extends the machine’s life and affects the acceleration of the tunneling process.

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Section: Design Of Fuzzy Pid Controllermentioning

confidence: 99%

Performance Analysis of Electro-Hydraulic Thrust System of TBM Based on Fuzzy PID Controller

Gong

Chen

et al. 2022

Energies

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“…However, the KNN drawback is that at higher database size the classification process becomes slower due to more iterations involved. Alternative fuzzy logic that is not investigated here could be based on the field programmable gate array (FPGA) or its Proportional-Integral-Derivative (PID)-type version [39]. The ANN algorithm works differently by simulating a network of artificial neurons that requires a training procedure [37] that is time consuming but at the end of the training procedure the algorithm works without training data making the classification very fast.…”

Section: Database Analysis By Discrimination Algorithmsmentioning

confidence: 99%

GeSi Nanocrystals Photo-Sensors for Optical Detection of Slippery Road Conditions Combining Two Classification Algorithms

Palade

Stavarache

Stoïca

et al. 2020

Sensors

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One of the key elements in assessing traffic safety on the roads is the detection of asphalt conditions. In this paper, we propose an optical sensor based on GeSi nanocrystals embedded in SiO2 matrix that discriminates between different slippery road conditions (wet and icy asphalt and asphalt covered with dirty ice) in respect to dry asphalt. The sensor is fabricated by magnetron sputtering deposition followed by rapid thermal annealing. The photodetector has spectral sensitivity in the 360–1350 nm range and the signal-noise ratio is 102–103. The working principle of sensor setup for detection of road conditions is based on the photoresponse (photocurrent) of the sensor under illumination with the light reflected from the asphalt having different reflection coefficients for dry, wet, icy and dirty ice coatings. For this, the asphalt is illuminated sequentially with 980 and 1064 nm laser diodes. A database of these photocurrents is obtained for the different road conditions. We show that the use of both k-nearest neighbor and artificial neural networks classification algorithms enables a more accurate recognition of the class corresponding to a specific road state than in the case of using only one algorithm. This is achieved by comparing the new output sensor data with previously classified data for each algorithm and then by performing an intersection of the algorithms’ results.

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“…To address the uncertain problem, different kinds of control approach have been developed, such as the PID control method [ 10 ], sliding mode control [ 11 , 12 ], and adaptive control [ 13 ]. In addition, many researchers have integrated these control strategies to further improve the control performance.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners

Liang

Xiao

et al. 2022

Micromachines

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The motion control of high-precision electromechanitcal systems, such as micropositioners, is challenging in terms of the inherent high nonlinearity, the sensitivity to external interference, and the complexity of accurate identification of the model parameters. To cope with these problems, this work investigates a disturbance observer-based deep reinforcement learning control strategy to realize high robustness and precise tracking performance. Reinforcement learning has shown great potential as optimal control scheme, however, its application in micropositioning systems is still rare. Therefore, embedded with the integral differential compensator (ID), deep deterministic policy gradient (DDPG) is utilized in this work with the ability to not only decrease the state error but also improve the transient response speed. In addition, an adaptive sliding mode disturbance observer (ASMDO) is proposed to further eliminate the collective effect caused by the lumped disturbances. The micropositioner controlled by the proposed algorithm can track the target path precisely with less than 1 μm error in simulations and actual experiments, which shows the sterling performance and the accuracy improvement of the controller.

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A PID-Type Fuzzy Logic Controller-Based Approach for Motion Control Applications

Cited by 39 publications

References 42 publications

Performance Analysis of Electro-Hydraulic Thrust System of TBM Based on Fuzzy PID Controller

Performance Analysis of Electro-Hydraulic Thrust System of TBM Based on Fuzzy PID Controller

GeSi Nanocrystals Photo-Sensors for Optical Detection of Slippery Road Conditions Combining Two Classification Algorithms

Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners

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