Differential Evolution for the Control Gain's Optimal Tuning of a Four-bar Mechanism

Calva-Yáñez, María Barbara; Niño-Suárez, Paola Andrea; Villarreal-Cervantes, Miguel Gabriel; Sepulveda-Cervantes, Gabriel; Portilla-Flores, Edgar Alfredo

doi:10.17562/pb-47-8

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…The velocities of each body are presented in equations (33) to (46), and were obtained from equations (25) to (32):…”

Section: Coupled Inverse Dynamics Of MMmentioning

confidence: 99%

“…• Add the integral action to the PD+ controller, to increase the robustness of the controller and to eliminate the singularities generated from an approximate modeling [21], [25]. The following section presents the optimization problem for the tuning of the PD+ controller, and a description of the implemented metaheuristic.…”

Section: Pd+ Controllermentioning

confidence: 99%

“…A specific case is the design and tuning of controllers, where the main objective is to find the global optimum (that is, the best possible solution) for a given controller. In [25], the parameters of a Proportional-Integral-Derivative (PID) control for the path tracking problem of a four-bar mechanism are calculated. A mono-objective dynamic optimization problem is proposed considering the spring and damping forces, to minimize the trajectory tracking error.…”

Section: Introductionmentioning

confidence: 99%

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Tuning a PD+ Controller by Means of Dynamic Optimization in a Mobile Manipulator With Coupled Dynamics

et al. 2019

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This article presents the development of a dynamic optimization method for controller tuning. This is proposed because most traditional tuning methods for complex coupled dynamic models are based on experience, and thus lacking accuracy. The case study is a Mobile Manipulator that consists of an anthropomorphic manipulator and a differential mobile platform. The system model has a trajectory generator that includes the coupled kinematic model, the Jacobian model, the coupled dynamic model, and a Proportional-Derivative plus controller. The tuning of the model is obtained by solving an optimization problem, using the Differential Evolution algorithm. This optimization approach allows to minimize simultaneously the energy consumption and the error on the trajectory tracking by the end effector. A novel strategy is applied to formulate the objective function, including constant weights for balancing the minimization effect. The objective is to avoid an energy consumption equal to zero that represents an error condition of no motion. The results of the case study and its statistical analysis are presented. The best result was modeled in Solidworks R and simulated in Matlab R. This model was exported to Simscape Multibody TM of Matlab, and its simulation produced satisfactory results, suggesting that the proposed optimization method can be a useful tool to solve real engineering problems.

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“…The velocities of each body are presented in equations (33) to (46), and were obtained from equations (25) to (32):…”

Section: Coupled Inverse Dynamics Of MMmentioning

confidence: 99%

Section: Pd+ Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tuning a PD+ Controller by Means of Dynamic Optimization in a Mobile Manipulator With Coupled Dynamics

et al. 2019

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Optimal design of a mechanism for children foot guiding

Suárez

Calderon-Romero

Calva-Yáñez

et al. 2017

VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, October 26th -28th, 2016

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Abstract-In this paper the result of a binational research, Mexico-Colombia, developed for the last four years is presented. This project aims to develop a robotic system to gait training for children 2 to 12 years with cerebral palsy. The concept design system and the methodology used during the development of the design process is presented. And finally, the optimal design of the ankle-foot stage is showed. The design of this stage was resolved as a dynamic optimization problem using a differential evolution algorithm with a constraint handling mechanism.

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Bacterial foraging optimization algorithm with mutation to solve constrained problems

et al. 2019

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A simple version of a Swarm Intelligence algorithm called bacterial foraging optimization algorithm with mutation and dynamic stepsize (BFOAM-DS) is proposed. The bacterial foraging algorithm has the ability to explore and exploit the search space through its chemotactic operator. However, premature convergence is a disadvantage. This proposal uses a mutation operator in a swim, similar to evolutionary algorithms, combined with a dynamic stepsize operator to improve its performance and allows a better balance between the exploration and exploitation of the search space. BFOAM-DS was tested in three well-known engineering design optimization problems. Results were analyzed with basic statistics and common measures for nature-inspired constrained optimization problems to evaluate the behavior of the swim with a mutation operator and the dynamic stepsize operator. Results were compared against a previous version of the proposed algorithm to conclude that BFOAM-DS is competitive and better than a previous version of the algorithm.

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Differential Evolution for the Control Gain's Optimal Tuning of a Four-bar Mechanism

Cited by 3 publications

References 16 publications

Tuning a PD+ Controller by Means of Dynamic Optimization in a Mobile Manipulator With Coupled Dynamics

Tuning a PD+ Controller by Means of Dynamic Optimization in a Mobile Manipulator With Coupled Dynamics

Optimal design of a mechanism for children foot guiding

Bacterial foraging optimization algorithm with mutation to solve constrained problems

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