Optimization Strategy of Bio-Inspired Metaheuristic Algorithms Tuned PID Controller for PMBDC Actuated Robotic Manipulator

Valluru, Sudarshan K.; Singh, Madhusudan

doi:10.1016/j.procs.2020.04.219

Cited by 18 publications

(7 citation statements)

References 3 publications

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“…Some authors focus on improving the response of the PID controllers using metaheuristics, especially in problems with a highly nonlinear behaviour, because, for them, the traditional methods of adjusting the gains are inefficient, obtaining results comparable or superior to conventional techniques [53]. Valluru and Singh [54] show that the efficiency of tuning nonlinear drivers using particle swarm optimization and other bioinspired techniques is superior to the results with traditional tuning techniques. e experimental results show that the overshoot and settling time of a nonlinear PID controller can be improved while maintaining a satisfactory system response.…”

Section: Proportional Integrative Derivative Controllermentioning

confidence: 99%

Speed Proportional Integrative Derivative Controller: Optimization Functions in Metaheuristic Algorithms

López

Serradilla

Hernández

et al. 2021

Journal of Advanced Transportation

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Recent advancements in computer science include some optimization models that have been developed and used in real applications. Some metaheuristic search/optimization algorithms have been tested to obtain optimal solutions to speed controller applications in self-driving cars. Some metaheuristic algorithms are based on social behaviour, resulting in several search models, functions, and parameters, and thus algorithm-specific strengths and weaknesses. The present paper proposes a fitness function on the basis of the mathematical description of proportional integrative derivate controllers showing that mean square error is not always the best measure when looking for a solution to the problem. The fitness developed in this paper contains features and equations from the mathematical background of proportional integrative derivative controllers to calculate the best performance of the system. Such results are applied to quantitatively evaluate the performance of twenty-one optimization algorithms. Furthermore, improved versions of the fitness function are considered, in order to investigate which aspects are enhanced by applying the optimization algorithms. Results show that the right fitness function is a key point to get a good performance, regardless of the chosen algorithm. The aim of this paper is to present a novel objective function to carry out optimizations of the gains of a PID controller, using several computational intelligence techniques to perform the optimizations. The result of these optimizations will demonstrate the improved efficiency of the selected control schema.

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Section: Proportional Integrative Derivative Controllermentioning

confidence: 99%

Speed Proportional Integrative Derivative Controller: Optimization Functions in Metaheuristic Algorithms

López

Serradilla

Hernández

et al. 2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…Over the past two decades, multi-objective optimization (MO) has aroused increasing research interest due to its widespread real-world applications, such as energy dispatch [1], [2], job assembly [3] and controller optimization [4]. However, the conflicts among different objectives lead the issue of simultaneously gaining global optimum to each objective to be challenging or even impossible [5].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Optimization Approach Based on an Enhanced Particle Swarm Optimization Algorithm With Evolutionary Game Theory

Yin

Tang

et al. 2023

IEEE Access

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Due to conflicts among objectives of multi-objective optimization (MO) problems, it remains challenging to gain high-quality Pareto fronts for different MO issues. Attempt to handle this challenge and obtain high-performance Pareto fronts, this paper proposes a novel MO optimizer via leveraging particle swarm optimization (PSO) with evolutionary game theory (EGT). Firstly, a modified self-adaptive PSO (MSAPSO) adopting a novel self-adaptive parameter adaption rule determined by the evolutionary strategy of EGT to tune the three key parameters of each particle is proposed in order to well balance the exploration and exploitation abilities of MSAPSO. Then, a parameter selection principle is provided to sufficiently guarantee convergence of MSAPSO followed after the analytical convergence investigation of this optimizer so as to assure convergence of the searched Pareto front toward the true Pareto front as far as possible. Subsequently, a MSAPSO-based MO optimizer is developed, in which an external archive is applied to preserve the searched non-dominated solutions and a circular sorting method is amalgamated with the elitistsaving method to update the external archive. Lastly, the performance of the proposed method is examined by 16 benchmark test functions against 4 well-known MOO methods. The simulation results reveal that the proposed method dominates its peers regarding the quality of the Pareto fronts for most of the studied benchmarks. Furthermore, the results of the non-parametric analysis confirm that the proposed method significantly outperforms its contenders at the confidential level of 95% over the 16 benchmarks.

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“…They require not only good real-time performance but also the capacity to effectively perform multiple tasks and good adaptability to the complex control environments [4] , [5] , [6] . Traditional control algorithms, such as PID and synovial control, perform well in ideal environments [7] , [8] , [9] , [10] . For example, in [7] , a fractional non-singular terminal sliding mode controller without manipulator dynamic information was presented.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in [7] , a fractional non-singular terminal sliding mode controller without manipulator dynamic information was presented. In [8] , two algorithms were used to tune PID controllers to implement the control of a motor-actuated manipulator. In [9] , an adaptively prescribed controller was developed for uncertain manipulators, which reduces the update frequency and avoids uninterrupted detection.…”

Section: Introductionmentioning

confidence: 99%

Multilayered hybrid time-varying problem solving based on integrated-enhanced zeroing neural network for robust manipulator control

Zhao,

Xiong

2023

Heliyon

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Optimization Strategy of Bio-Inspired Metaheuristic Algorithms Tuned PID Controller for PMBDC Actuated Robotic Manipulator

Cited by 18 publications

References 3 publications

Speed Proportional Integrative Derivative Controller: Optimization Functions in Metaheuristic Algorithms

Speed Proportional Integrative Derivative Controller: Optimization Functions in Metaheuristic Algorithms

A Multi-Objective Optimization Approach Based on an Enhanced Particle Swarm Optimization Algorithm With Evolutionary Game Theory

Multilayered hybrid time-varying problem solving based on integrated-enhanced zeroing neural network for robust manipulator control

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