Neuro-fuzzy self-tuning of PID control for semiglobal exponential tracking of robot arms

Armendariz, J.; Parra-Vega, Vicente; García-Rodríguez, R.; Rosales, S.

doi:10.1016/j.asoc.2014.08.037

Cited by 27 publications

(8 citation statements)

References 28 publications

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“…The literature shows that PID controllers have been improved using several methods. In [8], a hybrid PD-PID controller has been developed for two-link flexible manipulator systems. The authors [9] have presented a new modified PID hybrid fuzzy controller for robot manipulators that provides high performance nonlinear methodology.…”

Section: Introductionmentioning

confidence: 99%

Optimal Improved Pid Controller With Goa Algorithm for Single Link Human Leg Robot

Adnan

Karam

2022

jeasd

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A Human Leg Robot (HLR) is a type of manipulator robot that has non-linear, quality, time-varying behaviors and uncertainty in parameters. Therefore, it must be controlled. The purpose of this work is to design an optimal Improved Proportional-Integral-Derivative (IPID) controller for this robot to improve its performance in tracking the desired path. Two schemes of IPID controllers are suggested to enhance the performance of the traditional PID in controlling the human leg robot and following the desired path. The Grasshopper Optimization Algorithm (GOA) is considered to optimize the IPID parameters. The performance of the suggested IPID structures has been compared to traditional PID methods. The simulation results show that the efficiency of these schemes was enough to handle the tracking problems of the HLR.

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Section: Introductionmentioning

confidence: 99%

Optimal Improved Pid Controller With Goa Algorithm for Single Link Human Leg Robot

Adnan

Karam

2022

jeasd

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“…Common tasks performed by robot manipulators include trajectory tracking, reaching positions, and picking and dropping objects. These tasks need the robot controllers to maintain satisfactory dynamic behavior in spite of possible external perturbations, unknown dynamic parameters, and sensor information loss (Armendariz et al, 2014 ). Several controllers that are often implemented to manage these features are also mentioned (Luo and Kuo, 2016 ; Makarov et al, 2016 ; Nicolis et al, 2016 ; Pan et al, 2018 ; Hwang and Yu, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

PD Control Compensation Based on a Cascade Neural Network Applied to a Robot Manipulator

et al. 2020

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A Proportional Integral Derivative (PID) controller is commonly used to carry out tasks like position tracking in the industrial robot manipulator controller; however, over time, the PID integral gain generates degradation within the controller, which then produces reduced stability and bandwidth. A proportional derivative (PD) controller has been proposed to deal with the increase in integral gain but is limited if gravity is not compensated for. In practice, the dynamic system non-linearities frequently are unknown or hard to obtain. Adaptive controllers are online schemes that are used to deal with systems that present non-linear and uncertainties dynamics. Adaptive controller use measured data of system trajectory in order to learn and compensate the uncertainties and external disturbances. However, these techniques can adopt more efficient learning methods in order to improve their performance. In this work, a nominal control law is used to achieve a sub-optimal performance, and a scheme based on a cascade neural network is implemented to act as a non-linear compensation whose task is to improve upon the performance of the nominal controller. The main contributions of this work are neural compensation based on a cascade neural networks and the function to update the weights of neural network used. The algorithm is implemented using radial basis function neural networks and a recompense function that leads longer traces for an identification problem. A two-degree-of-freedom robot manipulator is proposed to validate the proposed scheme and compare it with conventional PD control compensation.

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“…There are also examples of SVMs implemented to FPGA-based systems, like speech recognition system [ 24 ] or melanoma detection [ 25 ]. Regarding ANFIS, there are many examples of real-time applications of these algorithms in automatic control area, like of hydraulic systems [ 26 ], active suspension system for passenger comfort improvement [ 27 ], robot arms [ 28 ], or mobile robots navigation for target seeking and obstacle avoidance [ 29 ]. Karakuzu et al [ 30 ] and Maldonado et al [ 31 ] deployed ANFIS algorithms to FPGAs.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Algorithms for Structure State Prediction in Operational Load Monitoring

Mucha

2020

Sensors

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The aim of operational load monitoring is to make predictions about the remaining usability time of structures, which is extremely useful in aerospace industry where in-service life of aircraft structural components can be maximized, taking into account safety. In order to make such predictions, strain sensors are mounted to the structure, from which data are acquired during operational time. This allows to determine how many load cycles has the structure withstood so far. Continuous monitoring of the strain distribution of the whole structure can be complicated due to vicissitude nature of the loads. Sensors should be mounted in places where stress and strain accumulations occur, and due to experiencing variable loads, the number of required sensors may be high. In this work, different machine learning and artificial intelligence algorithms are implemented to predict the current safety factor of the structure in its most stressed point, based on relatively low number of strain measurements. Adaptive neuro-fuzzy inference systems (ANFIS), support-vector machines (SVM) and Gaussian processes for machine learning (GPML) are trained with simulation data, and their effectiveness is measured using data obtained from experiments. The proposed methods are compared to the earlier work where artificial neural networks (ANN) were proven to be efficiently used for reduction of the number of sensors in operational load monitoring processes. A numerical comparison of accuracy and computational time (taking into account possible real-time applications) between all considered methods is provided.

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Neuro-fuzzy self-tuning of PID control for semiglobal exponential tracking of robot arms

Cited by 27 publications

References 28 publications

Optimal Improved Pid Controller With Goa Algorithm for Single Link Human Leg Robot

Optimal Improved Pid Controller With Goa Algorithm for Single Link Human Leg Robot

PD Control Compensation Based on a Cascade Neural Network Applied to a Robot Manipulator

Comparison of Machine Learning Algorithms for Structure State Prediction in Operational Load Monitoring

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