Black-box Optimization of PID Controllers for Aircraft Maneuvering Control

Kim, Dohyung; Oh, Hyun-Shik

doi:10.1007/s12555-020-0915-6

Int. J. Control Autom. Syst.

2022

DOI: 10.1007/s12555-020-0915-6

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Black-box Optimization of PID Controllers for Aircraft Maneuvering Control

Dohyung Kim

Hyun-Shik Oh

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Cited by 3 publications

(1 citation statement)

References 22 publications

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“…Data-driven methods are used to make decisions based on a large amount of data, which can effectively prevent the effects of personal bias. In recent years, data-driven methods have been widely used for many industrial applications, such as grinding, construction [11], the newsvendor problem [19], iron removal by goethite [8], and controllers for aircrafts [18,13]. Dai et al [9] proposed a data-driven model for particle size prediction based on a random configuration network and robust technology.…”

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confidence: 99%

Research on particle size and consumption collaborative optimization problems of grinding process based on an enhanced Harris hawks optimization algorithm

Liu,

Dai,

Chen

et al. 2024

JIMO

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The grinding process is an essential part of iron and steel smelting. Due to the changeable ore source and the complex dynamic characteristics of the process, the grinding particle size often cannot meet the requirements, resulting in an increase in process energy and material consumption, as well as reworking. In response to the above problems, the following goal was achieved through this study: a collaborative optimization problem for minimization of the grinding particle size error, process water, amountand electricity consumption has been established, which yields the grinding particle size in real time through the use of a neural network. In order to improve the optimization performance, an enhanced Harris hawks optimization (EHHO) algorithm has been developed. EHHO strengthens the team cooperation between individuals in the algorithm initialization phase and location update phase, as well as introduces an attenuation factor in the search phase to improve the stability and accuracy of the algorithm. The effectiveness of the proposed method is demonstrated through the use of some benchmark functions and an industrial grinding process. The results show that the enhanced Harris hawks optimization algorithm has better optimization accuracy and generalizability; furthermore, it can be effectively applied to the collaborative optimization of grinding process.

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mentioning

confidence: 99%

Research on particle size and consumption collaborative optimization problems of grinding process based on an enhanced Harris hawks optimization algorithm

Liu,

Dai,

Chen

et al. 2024

JIMO

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Performance-Driven Safe Bayesian Optimization for Intelligent Tuning of High-Order Cascade Systems

Jiang

Huang

et al. 2024

IEEE Trans. Artif. Intell.

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Self-Balancing and Position Control of a Balancer System Using a Pattern-Based Intelligent Optimization Method

Chaudhary,

Bhushan

2023

Int. J. Patt. Recogn. Artif. Intell.

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This paper presents a new Data-Driven Teaching Learning-Based Optimization (DDTLBO) method used to improve the self-balancing and position control of a two-degree of freedom ball and balancer (2DOFBB) system. DDTLBO is a modified version of teaching learning-based optimization (TLBO) method, based on the natural pattern followed by “learners” in an academic class to improve their knowledge. In this, an intelligent algorithm is used on MATLAB platform to handle the nonlinearities and un-stability of the ball and balancer system. A ball-balancer is a highly nonlinear, electromechanical, multivariable and underactuated system. A sophisticated automated intelligent control approach is used to handle these complications. In the proposed approach, the pattern followed by inclination angle of balancer plate is traced after observing the control dynamics of 2DOFBB system. Modeling of ball balancer system is accomplished with the help of proportional integral and derivative (PID) controller. Further, an intelligent data-driven computational approach is implemented, which improves the execution of TLBO optimization method. This improved optimization is used to tune the performance of proportional integral derivative controller employed on the ball balancer system. This approach addresses the major challenge of unknown disturbances on the closed loop system of the considered problem. The dominance of proposed intelligent approach is verified over other optimization techniques using benchmark CEC functions. The optimized parameters of PID controllers hence obtained are implemented on 2DOFBB system. Results obtained are validated using real-time response analysis on the MATLAB/Simulation platform, which demonstrates the dominance of DDTLBO-PID controller over traditional controllers.

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Black-box Optimization of PID Controllers for Aircraft Maneuvering Control

Cited by 3 publications

References 22 publications

Research on particle size and consumption collaborative optimization problems of grinding process based on an enhanced Harris hawks optimization algorithm

Research on particle size and consumption collaborative optimization problems of grinding process based on an enhanced Harris hawks optimization algorithm

Performance-Driven Safe Bayesian Optimization for Intelligent Tuning of High-Order Cascade Systems

Self-Balancing and Position Control of a Balancer System Using a Pattern-Based Intelligent Optimization Method

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