Differential Evolution Algorithms Comparison Used to Tune a Visual Control Law

Sánchez-Sánchez, Pablo; Reyes, Jose Guillermo Cebada; Ruiz-Garcia, A.; Montiel-Martínez, Aideé; Reyes-Cortés, Fernando

doi:10.1109/access.2022.3168965

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…It has commonly been used to optimize engineering problems, such as signal processing [32], satellite image enhancement [33], and numerical optimization [34]. In the robotic area, DE algorithms and their variants have been widely used in motion planning [35], path trajectory [36], visual control [37], and so on. A novel DE algorithm was proposed by Ren et al [38] to plan a minimum-acceleration trajectory for a humanoid robot.…”

Section: Advantagesmentioning

confidence: 99%

Inverse Kinematics of Robot Manipulator Based on BODE-CS Algorithm

Luo

Qiao

2023

Machines

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Differential evolution is a popular algorithm for solving global optimization problems. When tested, it has reportedly outperformed both robotic problems and benchmarks. However, it may have issues with local optima or premature convergence. In this paper, we present a novel BODE-CS (Bidirectional Opposite Differential Evolution–Cuckoo Search) algorithm to solve the inverse kinematics problem of a six-DOF EOD (Explosive Ordnance Disposal) robot manipulator. The hybrid algorithm was based on the differential evolution algorithm and Cuckoo Search algorithm. To avoid any local optimum and accelerate the convergence of the swarm, various strategies were introduced. Firstly, a forward-kinematics model was established, and the objective function was formulated according to the structural characteristics of the robot manipulator. Secondly, a Halton sequence and an opposite search strategy were used to initialize the individuals in the swarm. Thirdly, the optimization algorithms applied to the swarm were dynamically allocated to the Differential Evolution algorithm or the Cuckoo algorithm. Fourthly, a composite differential algorithm, which consisted of a dynamically opposite differential strategy, a bidirectional search strategy, and two other typically used differential strategies were introduced to maintain the diversity of the swarm. Finally, two adaptive parameters were introduced to optimize the amplification factor F and cross-over probability Cr. To verify the performance of the BODE-CS algorithm, two different tasks were tested. The experimental results of the simulation showed that the BODE-CS algorithm had high accuracy and a fast convergence rate, which met the requirements of an inverse solution for the manipulator.

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Section: Advantagesmentioning

confidence: 99%

Inverse Kinematics of Robot Manipulator Based on BODE-CS Algorithm

Luo

Qiao

2023

Machines

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“…For instance, [90] decoupled tackled the multivariable control in a context of distillation column model in which the scaling factor of a DE algorithm was normalized by Zaslavsky [97] mapping function. [49] showed that DE algorithms attained the lowest positional error for PIDbased visual control laws in robot manipulator. [87] used dynamic constraints in DE within the context of control of a parallel robot, [37] used the strategy-adaptation DE (SADE) for temperature control of a parabolic trough, [45] used DE with Fuzzy Logic for PID tuning of a semi-active suspension system (quarter car), [35] used the many-objective DE for level temperature control of a rector model, [81] proposed the distance-based diversity enhancing mutation in DE for PID-based trajectory tracking of a four-bar linkage mechanism, [80] used the multi-objective differential evolution with spherical pruning for PID-based gasifier control (pressure, temperature, bed mass and calorific value).…”

Section: Related Workmentioning

confidence: 99%

PID Tuning Using Differential Evolution With Success-Based Particle Adaptations

Parque,

Khalifa

2023

IEEE Access

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Proportional-Integral-Derivative (PID) is a simple and intuitive feedback-based control mechanism being useful to track set points and to reject disturbances. A key question in gradient-free optimization is to ascertain whether the class of optimization algorithms based on the difference of vectors generalize reasonably well to tackle a large class of PID control problems. For generalization and practical purposes, it would be desirable to render algorithms being able to tune PID controllers over a diverse and large set of control problems/tasks with minimal human intervention (self-adaptation features), and under tight computational budgets. In this paper, aiming to fill the above-mentioned gap, we propose and investigate the effectiveness of a new class of algorithm based on the difference of vectors and self-adaptation mechanisms for PID tuning. As such, we introduce a new class of Differential Evolution with successbased Particle Adaptations (DEPA), which unifies the principles of difference of vectors, particle schemes and trial/parameter adaptation through archive (memory) mechanisms. Our computational simulations using a large/relevant set of 25 control problem instances (tracking of linear, nonlinear, continuous, and discontinuous trajectories in motor position control, motor velocity control, magnetic levitation, inverted pendulum, crane stabilization), and the comparisons with a large set of closely related optimization algorithms, and their extended adaptive variants (23 optimization algorithms in total) has shown the outperforming benefits of the proposed approach in convergence performance under tight function evaluation budgets (1000 function evaluations). Also, the experiments on a real-world inverted pendulum device show the potential for transferability of the learned gains to unseen situations during training. Furthermore, we evaluated the algorithmic extension and the generalization towards diverse fitness landscapes in the CEC 2017 benchmark suite, showing the attractive/outperforming performance overall problem instances. In particular, the proposed framework performs better in 358 control instances compared to other algorithms for PID control tasks, and the algorithmic extension for general optimization landscapes performs better than the related algorithms in 182, 215 and 235 problem instances of CEC 2017 benchmark suite for 10, 30 and 50 dimensions, respectively. Our obtained results have the potential to further advance towards developing efficient and self-adaptive optimization algorithms based on the difference of vectors, which may find use in a wider set of optimization and control problems.

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Differential Evolution Algorithms Comparison Used to Tune a Visual Control Law

Cited by 2 publications

References 27 publications

Inverse Kinematics of Robot Manipulator Based on BODE-CS Algorithm

Inverse Kinematics of Robot Manipulator Based on BODE-CS Algorithm

PID Tuning Using Differential Evolution With Success-Based Particle Adaptations

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