Optimization of a Proportional-Summation-Difference Controller for a Line-Tracing Robot Using Bacterial Memetic Algorithm

Zahn, Brandon; Ucherdzhiev, Ivan; Szeles, Julia; Botzheim, János; Kubota, Naoyuki

doi:10.1007/978-3-319-43506-0_32

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Furthermore, natural processes in nature were used as referents to devise new heuristics, and used in PID tuning. For instance, [88] mimicked the behaviour of microbial evolution for PID tuning in line-tracing robot context, [66] used a theory of the evolution of the universe to render the Bing Bang-Big Crunch (BBBC) algorithm for spatial inverted pendulum, [84] used the model of social hierarchy and hunting dynamics of grey wolves to render the Grey Wolf Optimization (GWO) for tuning a Takagi-Sugeno-Kang PI-Fuzzy control, [55] used fractional order fish migration for PID parameter optimization of transfer functions, [56] proposed a modified monkey-multiagent deep reinforcement learning for PID tuning in quadrotor position control, [68] used the model of force interactions in atoms through the Lennard-Jones potential to render the atom search optimization (ASO) for fractional-order PID tuning in motor velocity control.…”

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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Section: Related Workmentioning

confidence: 99%

PID Tuning Using Differential Evolution With Success-Based Particle Adaptations

Parque,

Khalifa

2023

IEEE Access

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“…The robot is attractive in its simplicity of design and control. It has been used previously in studying route-following by klinokinesis, inspired by the navigation skills of desert ants [24], randomised algorithm mimicking biased lone exploration in roaches [21], and the self optimisation procedure on a line-tracing application by using a evolutionary computing algorithm [45].…”

Section: Introductionmentioning

confidence: 99%

Belousov-Zhabotinsky liquid marbles in robot control

Tsompanas

Fullarton

Adamatzky

2019

Sensors and Actuators B: Chemical

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We show how to control the movement of a wheeled robot using on-board liquid marbles made of Belousov-Zhabotinsky solution droplets coated with polyethylene powder. Two stainless steel, iridium coated electrodes were inserted in a marble and the electrical potential recorded was used to control the robot's motor. We stimulated the marble with a laser beam. It responded to the stimulation by pronounced changes in the electrical potential output. The electrical output was detected by robot. The robot was changing its trajectory in response to the stimulation. The results open new horizons for applications for oscillatory chemical reactions in robotics.

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A Differential Particle Scheme with Successful Parent Selection and its Application to PID Control Tuning

Parque

2021

2021 IEEE Congress on Evolutionary Computation (CEC)

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Optimization of a Proportional-Summation-Difference Controller for a Line-Tracing Robot Using Bacterial Memetic Algorithm

Cited by 3 publications

References 5 publications

PID Tuning Using Differential Evolution With Success-Based Particle Adaptations

PID Tuning Using Differential Evolution With Success-Based Particle Adaptations

Belousov-Zhabotinsky liquid marbles in robot control

A Differential Particle Scheme with Successful Parent Selection and its Application to PID Control Tuning

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