Enhancing the Performance of the Particle Filtering Optimization Algorithm for the Tuning of PID Controllers

Plaza-Guingla, Douglas A.; Idrovo, Roger M.; Valencia, Angel J.; López, Carlos

doi:10.1145/3149827.3149850

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Also, there exist more than a thousand tuning rules to minimize tailored plant conditions and control performance criteria [15]. In the context of optimization, particle filtering improves the computation complexity [16], constrained optimization enables the inclusion of robustness contraints [17], and the ensembles of Kalman Filters use fewer particles while allowing a slight increase in computational time for complex transfer functions [18]. A comparison of tuning rules for fractional-order PID control is provided by [19].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

PID Tuning Using Differential Evolution With Success-Based Particle Adaptations

Parque,

Khalifa

2023

IEEE Access

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“…Since their introduction some decades ago, PID controllers have been effectively employed in fields controlled by industrial processes, including metallurgy, chemistry and energy (Ogata, 2009;Shamsuzzoha, 2018;Plaza-Glinga et al, 2017;Fan et al, 2019;Saad et al, 2012). Two of the main factors for the continual application of PID controllers are their easy of analysis and design, as well as being easily implemented.…”

Section: Introductionmentioning

confidence: 99%

Construction and operation of a modular didactic unit for Proportional-Integral-Derivative (PID) level control in tank

2023

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The construction and operation of a modular didactic unit for PID level control was carried out with the function of qualifying the student regarding the use of the instruments involved. The developed algorithm operated in Arduino together with a supplement, elaborating graphic visuals in real-time execution. It is expected that the use of this unit will enable the technical qualification, applied to problem resolution and implementation of solutions, according to teachings in class.

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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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Enhancing the Performance of the Particle Filtering Optimization Algorithm for the Tuning of PID Controllers

Cited by 4 publications

References 13 publications

PID Tuning Using Differential Evolution With Success-Based Particle Adaptations

PID Tuning Using Differential Evolution With Success-Based Particle Adaptations

Construction and operation of a modular didactic unit for Proportional-Integral-Derivative (PID) level control in tank

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

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