Research on Autodisturbance-Rejection Control of Induction Motors Based on an Ant Colony Optimization Algorithm

Yin, Zhongdong; Du, Changqing; Sun, Xiaodong; Zhong, Yanru

doi:10.1109/tie.2017.2751008

Cited by 105 publications

(41 citation statements)

References 41 publications

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“…Han in 1998 [15,16]. The main idea of the ADRC theory is to treat the total disturbance as a new state variable and estimate it through an ESO [17].…”

Section: Related Workmentioning

confidence: 99%

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Maritime Buoyage Inspection System Based on an Unmanned Aerial Vehicle and Active Disturbance Rejection Control

Sheng

et al. 2021

IEEE Access

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The unmanned aerial vehicle (UAV) is inexpensive and offers a fast response speed and robust flexibility; thus, it is a promising tool in the maritime buoyage inspection scenario, which involves monitoring and accessing a lateral mark system far away from the coast. However, two main problems can occur during inspection. The first is extreme weather conditions, resulting in a deviation between the inspection route and the design route. The second is that the buoyage beacons are visited only once. Therefore, this article proposes a buoyage inspection system consisting of a single UAV and random coastal buoys. The UAV automatically takes off from the depot, performs a self-check on the buoy beacons, and then returns to the depot. A cascade active disturbance rejection controller (ADRC) is designed to adjust the real-time trajectory of the UAV system. A feasible trajectory planning method is also designed based on the continuous Hopfield neural network (CHNN) and genetic algorithm (GA) to minimize the inspection distance. Extensive simulations are conducted to demonstrate the effectiveness of the proposed method.

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“…Han in 1998 [15,16]. The main idea of the ADRC theory is to treat the total disturbance as a new state variable and estimate it through an ESO [17].…”

Section: Related Workmentioning

confidence: 99%

“…where ψ d (k) is the input, ψ 1 (k) and ψ 2 (k) are the discrete outputs, k is the sampling step, and h 0 and r 0 are filter coefficients. The fhan function is defined in (10) [15,16,35].…”

Section: A Tracking Differentiator (Td)mentioning

confidence: 99%

Maritime Buoyage Inspection System Based on an Unmanned Aerial Vehicle and Active Disturbance Rejection Control

Sheng

et al. 2021

IEEE Access

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“…In this algorithm, the search way is according to population, and it has very strong parallel computing capability. e ACO algorithm has several advantages, such as distributed computation, constructive greedy heuristics, and positive feedback [31]. e greatest advantage of this method is that it can tune a lot of parameters simultaneously and it can be used to solve optimization problems.…”

Section: Rbf Approximate Modelmentioning

confidence: 99%

An Effective Inverse Procedure for Identifying DEMParameters of Rock‐Like Materials

Chen

Qian

et al. 2019

Mathematical Problems in Engineering

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This study aims to identify discrete element model parameters of rock-like materials. An inverse procedure is developed to determine the discrete element model parameters from experimental measurements. This involves the solution of an inverse problem through minimizing the misfit function which describes the error between numerical computation and experiment by an optimization procedure. In this procedure, the discrete element method is adopted as the numerical calculation method of the forward problem. The orthogonal experimental design is used for parameter sensitivity analysis. Besides, the approximation model with radial basis function is adopted instead of the actual calculation model to reduce the time of forward calculation. The ant-colony optimization algorithm is employed as the inverse operator. Therefore, the parameters of the discrete element model are optimized by this procedure. The three-point bending experiment with discrete element simulation is provided to verify the validity and accuracy of the inversion results. The results indicate that it can rapidly obtain the available and reliable model parameters just through a few sets of experimental data. As a result, this inverse procedure can be applied more widely to parameter identification of the discrete element model for brittle materials.

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“…Since the parameters of ESO directly affect the observation of disturbances and uncertainties and then affect the control performance of ADRC, the optimization design of the parameters of ESO has been studied. An ant colony optimization (ACO) algorithm [30] is investigated to optimize the six parameters of ESO for an induction motor system by the self-learning ability of ACO. Thus, the robustness of the proposed optimization scheme is better than traditional ADRC when perturbation is produced.…”

Section: Introductionmentioning

confidence: 99%

“…The accurate tracking performance and robustness are obtained by using the proposed optimization algorithm. However, the optimizations of [30][31][32] are all offline optimization algorithms and the implements are more complex. When the system is suddenly influenced by the external environment, the control of offline parameter identification may not fully guarantee the system performance.…”

Section: Introductionmentioning

confidence: 99%

Speed Tracking and Synchronization of a Multimotor System Based on Fuzzy ADRC and Enhanced Adjacent Coupling Scheme

et al. 2018

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In this paper, a speed tracking and synchronization control approach is proposed for a multimotor system based on fuzzy active disturbance rejection control (FADRC) and enhanced adjacent coupling scheme. By employing fuzzy logic rules to adjust the coefficients of the extended state observer (ESO), FADRC is presented to guarantee the speed tracking performance and enhance the system robustness against external disturbance and parametric variations. Moreover, an enhanced adjacent coupling synchronization control strategy is proposed to simplify the structure of the speed synchronization controller through introducing coupling coefficients into the conventional adjacent coupling approach. Based on the proposed synchronization control scheme, an adaptive integral sliding mode control (AISMC) is investigated such that the chattering problem in conventional sliding mode control can be weakened by designing an adaptive estimation law of the control gain. Comparative simulations are carried out to prove the superiorities of the proposed method.

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Research on Autodisturbance-Rejection Control of Induction Motors Based on an Ant Colony Optimization Algorithm

Cited by 105 publications

References 41 publications

Maritime Buoyage Inspection System Based on an Unmanned Aerial Vehicle and Active Disturbance Rejection Control

Maritime Buoyage Inspection System Based on an Unmanned Aerial Vehicle and Active Disturbance Rejection Control

An Effective Inverse Procedure for Identifying DEMParameters of Rock‐Like Materials

Speed Tracking and Synchronization of a Multimotor System Based on Fuzzy ADRC and Enhanced Adjacent Coupling Scheme

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