New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder–Mead algorithm

Blondin, Maude-Josée; Sanchis, Javier; Sicard, Pierre; Herrero, J. M.

doi:10.1016/j.asoc.2017.10.007

Cited by 86 publications

(67 citation statements)

References 43 publications

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“…The example includes an AVR control system and a DC motor control system. These are verified by comparing the standard deviation calculation and the transient response analysis from the simulation with ACO‐NM (Blondina et al, ), GA, PSO, and NN.…”

Section: Example and Simulation Resultsmentioning

confidence: 72%

“…The global convergence is verified about the logical and processing of tuning that it can execute ill approach the optimal value, whereas the characteristic convergence focuses on the speed of tuning for achieving the objective function, called the fitness function. (3)The SLP algorithm is analysed by using the Markov chain modelling and judged by the convergence rule of random search algorithm. The superiority of characteristic convergence and performance is proved by comparing the standard deviation and transient response in each tuning with ant colony optimization with a new constrained Nelder–Mead algorithm (ACO‐NM; Blondina, Sicarda, Sanchis, & Herrero, ), GA, PSO, and NN in automatic voltage regulator (AVR) and direct current (DC) motor control system.…”

Section: Introductionmentioning

confidence: 99%

“…(3) The SLP algorithm is analysed by using the Markov chain modelling and judged by the convergence rule of random search algorithm. The superiority of characteristic convergence and performance is proved by comparing the standard deviation and transient response in each tuning with ant colony optimization with a new constrained Nelder-Mead algorithm (ACO-NM; Blondina, Sicarda, Sanchis, & Herrero, 2018), GA, PSO, and NN in automatic voltage regulator (AVR) and direct current (DC) motor control system. This paper is organized as follows.…”

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confidence: 99%

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A novel optimal PID controller autotuning design based on the SLP algorithm

Pongfai

Zhang

et al. 2019

Expert Systems

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A novel optimal proportional integral derivative (PID) autotuning controller design based on a new algorithm approach, the “swarm learning process” (SLP) algorithm, is proposed. It improves the convergence and performance of the autotuning PID parameter by applying the swarm and learning algorithm concepts. Its convergence is verified by two methods, global convergence and characteristic convergence. In the case of global convergence, the convergence rule of a random search algorithm is employed to judge, and Markov chain modelling is used to analyse. The superiority of the proposed method, in terms of characteristic convergence and performance, is verified through the simulation based on the automatic voltage regulator and direct current motor control system. Verification is performed by comparing the results of the proposed model with those of other algorithms, that is, the ant colony optimization with a new constrained Nelder–Mead algorithm, the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, and a neural network (NN). According to the global convergence analysis, the proposed method satisfies the convergence rule of the random search algorithm. With respect to the characteristic convergence and performance, the proposed method provides a better response than the GA, the PSO, and the NN for both control systems.

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Section: Example and Simulation Resultsmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A novel optimal PID controller autotuning design based on the SLP algorithm

Pongfai

Zhang

et al. 2019

Expert Systems

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“…Algorithm. Ant colony algorithm [13] is inspired by ants feeding behaviors; scientists found that ants have no vision, and they cannot grasp the relevant geographic information in the region, but the ant colony can find the optimal path from the nest to the food source. Study finds that ants release a kind of pheromones on the path of travel when feeding, through the pheromones, the ant colony transmit messages with each other to find the optimal path.…”

Section: The Basic Principle and Mathematical Model Of Ant Colonymentioning

confidence: 99%

Tensioning-Phase Box Girder Deformation Prediction Model Based on Ant Colony Algorithm and Residual Correction

Liu

Wang

2018

Mathematical Problems in Engineering

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The girder of prestressed concrete continuous box girder bridge strengthened by a stay cable system has a very complex deformation mechanism, and it is difficult to establish accurate numerical model for prediction. For these problems, a prediction method based on the combination of ant colony algorithm and residual combination correction model is proposed. In this method, the measuring points are considered as the cities in TSP ant colony algorithm, the information function model and heuristic function model are constructed, the pheromone update mechanism and ant search mechanism are established, and the deformation prediction of the main girder based on ant colony algorithm prediction model is achieved. On this basis, in order to fit the random change process of main girder deflection better, the periodic function generated by harmonic transform and sine function are introduced to modify the predicted results, which makes up for the low precision defect of single model. The results show that compared with finite element method, unequal interval gray model (1,1) (UIGM (1,1)), one-time residual correction UIGM (1,1), Markov chain residual correction UIGM (1,1), and ant colony algorithm model, this model can reflect the space-time effect and casual fluctuation feature of the development of girder deformation better and also has higher prediction accuracy and efficiency. The mean relative error of the predictive value is 3.39%, the posterior error ratio is 0.060, and the accuracy level reaches level 1. This model provides a new way for the girder deformation prediction of bridge strengthened by the stay cable system.

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“…At deterministic heuristics, the same initial population always generates the same final solution, while at stochastic heuristics different final solutions can be generated using the same initial population. The most popular stochastic algorithms used for PID controller design are evolutionary algorithms [34] such as: genetic algorithms [35][36][37], swarm techniques (ant colony [37][38][39], particle swarm [33,[40][41][42][43], bee colony [44,45]) etc. This paper deals with design of PID controller where optimal parameters * * * * of sensitivity to measurement noise.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization of PID Controller under Constraints on Performance and Robustness

Bošković

Rapaić²,

Jeličić³

2019

IJEEC

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This paper presents a design procedure of the PID controller where optimal parameters of controller * * * *p i d f (k ,k ,k ,T ) areobtained by solving the constrained optimization problem. The objective function is given in the form of the Integral of Absolute Error(IAE) under specifications to achieve predictable performance and robustness. The constraints within the optimization problem setupare desired maximum sensitivity, desired maximum complementary sensitivity and maximum sensitivity to measurement noise underhigh frequencies. The optimization problem is transformed to an unconstrained problem using penalty function approach. Solution tothe optimization problem is obtained using Particle Swarm algorithm (PSO) which leads to an efficient suppression of disturbance aswell as an adequate reference tracking performance of the closed-loop system with negligible overshoot. The suggested method isapplicable to the large class of stable, integral and unstable processes, processes with oscillatory dynamics with and without dead-time.Effectiveness of the proposed design procedure is verified via numerical simulations on test batch consisting of processes typicallyencountered in industry. Paper also provides two another solutions of the defined optimization problem using genetic algorithm (GA)and fminunc trust region based approach (TR). Performance of the PSO, GA, and TR based control system is compared with thoseusing recently proposed maximization of proportional gain denoted with max(kp) method. Although the present paper is focused to tunethe PID controller, the same procedure may be used to design PI controller, lead and lag compensators, high-order controllers as well asfractional-order controllers.

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New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder–Mead algorithm

Cited by 86 publications

References 43 publications

A novel optimal PID controller autotuning design based on the SLP algorithm

A novel optimal PID controller autotuning design based on the SLP algorithm

Tensioning-Phase Box Girder Deformation Prediction Model Based on Ant Colony Algorithm and Residual Correction

Particle Swarm Optimization of PID Controller under Constraints on Performance and Robustness

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