The fuzzy PID control optimized by genetic algorithm for trajectory tracking of robot arm

Zhao, Jie; Long, Han; Wang, Li; Yu, Zhou

doi:10.1109/wcica.2016.7578443

Cited by 23 publications

(14 citation statements)

References 10 publications

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“…This topic has attracted the attention of many researchers in the last years. For instance, Hernández-Alvarado, García-Valdovinos, Salgado-Jiménez, Gómez-Espinosa, and Fonseca-Navarro (2016) proposes an auto-tuned PID-like controller based on Neural Networks; Zhao, Han, Wang, and Yu (2016) presents a fuzzy PID controller optimized by genetic algorithms; Galicki (2016) discusses absolutely continuous Jacobian transpose robust controllers for finite-time trajectory tracking control in a task-space under dynamic uncertainties; Van Cuong and Nan (2016) proposes a neural-network controller for an -link robot manipulator with robust compensator to achieve a high-precision position tracking; Hsiao and Huang (2017) presents an iterative learning controller enhanced by a disturbance observer to robustly linearize the dynamics of robot manipulators. In general, these approaches are commonly based on computational demanding algorithms, also requiring advanced programming skills.…”

Section: Paper Contributionmentioning

confidence: 99%

Robot control parameters auto-tuning in trajectory tracking applications

Roveda

Forgione

Piga

2020

Control Engineering Practice

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Autonomy is increasingly demanded to industrial manipulators. Robots have to be capable to regulate their behavior to different operational conditions, adapting to the specific task to be executed without requiring high time/resource-consuming human intervention. Achieving an automated tuning of the control parameters of a manipulator is still a challenging task, which involves modeling/identification of the robot dynamics. This usually results in an onerous procedure, both in terms of experimental and data-processing time. This paper addresses the problem of automated tuning of the manipulator controller for trajectory tracking, optimizing control parameters based on the specific trajectory to be executed. A Bayesian optimization algorithm is proposed to tune both the low-level controller parameters (i.e., the equivalent link-masses of the feedback linearizator and the feedforward controller) and the high-level controller parameters (i.e., the joint PID gains). The algorithm adapts the control parameters through a data-driven procedure, optimizing a user-defined trajectory-tracking cost. Safety constraints ensuring, e.g., closed-loop stability and bounds on the maximum joint position error are also included. The performance of proposed approach is demonstrated on a torquecontrolled 7-degree-of-freedom FRANKA Emika robot manipulator. The 25 robot control parameters (i.e., 4 link-mass parameters and 21 PID gains) are tuned in less than 130 iterations, and comparable results with respect to the FRANKA Emika embedded position controller are achieved. In addition, the generalization capabilities of the proposed approach are shown exploiting the proper reference trajectory for the tuning of the control parameters.

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Section: Paper Contributionmentioning

confidence: 99%

Robot control parameters auto-tuning in trajectory tracking applications

Roveda

Forgione

Piga

2020

Control Engineering Practice

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“…For example, differential evolution and genetic algorithms have been used to perform an optimal design of a phase controller to track the trajectory of moving robots. [14][15][16] Some of these algorithma include the genetic algorithm (GA), 17 particle swarm optimization (PSO), 18 whale optimization algorithm (WOA), 19 gray wolf optimization (GWO), 20 etc. Based on 20 GWO technique illustrates its supremacy with an improved version of GWO technique named as IGWO.…”

Section: Introductionmentioning

confidence: 99%

Control of a DC motor using feedback linearization and gray wolf optimization algorithm

Vesović

Jovanović

Trišović

2022

Advances in Mechanical Engineering

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The aim of this study is to investigate nonlinear DC motor behavior and to control velocity as output variable. The linear model is designed, but as it is experimentally verified that it does not describe the system well enough it is replaced by the nonlinear one. System’s model has been obtained taking into account Coulomb and viscous friction in the firmly nonlinear environment. In the frame of the paper the dynamical analysis of the nonlinear feedback linearizing control is carried out. Furthermore, a metaheuristic optimization algorithm is set up for finding the coefficient of the proportional-integral feedback linearizing controller. For this purpose Gray wolf optimization technique is used. Moreover, after the introduction of the control law, analysis of the pole placement and stability of the system is establish. Optimized nonlinear control signal has been applied to the real object with simulated white noise and step signal as disturbances. Finally, for several desired output signals, responses with and without disruption are compared to illustrate the approach proposed in the paper. Experimental results obtained on the given system are provided and they verify nonlinear control robustness.

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“…The parameters of the FLC were optimized with particle swarm [15] and genetic [16] optimization algorithms for achieving a specific trajectory in a robot movement in terms of control precision and convergence speed. Optimal path search and control of mobile robot was also investigated using a hybridized sinecosine algorithm (SCA) and ant colony optimization technique [17].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy controller optimized by the African vultures algorithm for trajectory tracking of a two-link gripping mechanism

Jovanović¹,

Bugarić²,

Vesović³

et al. 2022

FME Transactions

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This paper presents the proportional-derivative fuzzy controller for trajectory tracking of the gripping mechanism with two degrees of freedom. Aiming to achieve movement of the gripping mechanism without sudden starting and stopping, a polynomial velocity profile is utilized. The African vultures optimization, as one of the latest metaheuristic algorithms, is used to obtain the optimal input/output scaling gains of the proposed fuzzy controller according to the selected fitness function. The results obtained by this algorithm are compared with the other three new and popular metaheuristic algorithms: the whale optimization, the ant lion optimization and the sine cosine algorithm. Moreover, a simulation study was done for the defined initial position and for the scenario where there is a certain deviation because the gripping mechanism is not at its original initial position. Finally, the robustness of the controller is tested for the case when the masses of the segments increase three times. The results revealed that the suggested controller was capable of dealing with nonlinearities of the gripping mechanism, initial position and parameter changes. The movement of the gripping mechanism is smooth and follows the defined trajectory.

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The fuzzy PID control optimized by genetic algorithm for trajectory tracking of robot arm

Cited by 23 publications

References 10 publications

Robot control parameters auto-tuning in trajectory tracking applications

Robot control parameters auto-tuning in trajectory tracking applications

Control of a DC motor using feedback linearization and gray wolf optimization algorithm

Fuzzy controller optimized by the African vultures algorithm for trajectory tracking of a two-link gripping mechanism

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