A New Multi-objective Artificial Bee Colony Algorithm for Optimal Adaptive Robust Controller Design

Mahmoodabadi, Mohammad Javad; Shahangian, Mohammad Mehdi

doi:10.1080/03772063.2019.1644211

Cited by 20 publications

(6 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Where: j≠i, ∈[-1,1],Xjd≠Xid and falls within [Ld,Ud]. When a new nectar source Vi is obtained (Vi=[Vi1,Vi2,.... ,Vid], the fitness function fi was adjusted (Mahmoodabadi and Shahangian, 2022).…”

Section: Neural Network Modelingmentioning

confidence: 99%

Research on residential building duration prediction model based on mean clustering and neural network

Ji,

Nan,

Wei

et al. 2023

Preprint

View full text Add to dashboard Cite

The duration target will directly affect the effectiveness of the implementation of the residential building project, so it is necessary to predict a reasonable residential building duration. In this work, genetic algorithm (GA) is used to optimize and improve the weights and thresholds of the BP neural network to form a GA-BP neural network model, and seven parameters, such as aboveground building structure, number of building floors, building area, decoration standards, foundation structure, number of underground building floors, and underground building area, are taken as the input parameters of the neural network model, and the residential building duration is taken as the output parameter. 111 sets of residential building duration data were collected and divided into 90 training sets and 21 test sets, and the model was validated and analyzed in comparison using root mean square error (RMSE), correlation coefficient (R) and average error rate, which were used to validate that the GA-BP neural network model helps in predicting the duration of residential buildings. In order to improve the prediction accuracy of GA-BP neural network model, this work uses artificial bee colony improved K-means clustering algorithm to classify 111 sets of experimental data and 33 sets of new data, according to the classification of the training and testing results show that the ABC-K-means-GA-B model all have a strong generalization ability and strong prediction accuracy, and at the same time, it proves that this paper's proposed ABC- K-means-GA-BP neural network helps to predict the construction period of residential buildings, which is of great practical significance for improving construction efficiency.

show abstract

Section: Neural Network Modelingmentioning

confidence: 99%

Research on residential building duration prediction model based on mean clustering and neural network

Ji,

Nan,

Wei

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…According to the last equation, the system trajectories will be pushed toward the sliding surface in finite time 36 . Since, the signum function causes chattering, we will replace it with the saturated vector function .…”

Section: Sliding Mode Controller With Pid Surfacementioning

confidence: 99%

Optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output

Azeez,

Abdelhaleem,

Elnaggar

et al. 2023

Sci Rep

View full text Add to dashboard Cite

The aim of this study is to enhance the performance of a nonlinear three-rigid-link maneuver (RLM) in terms of trajectory tracking, disturbance and noise cancellation, and adaptability to joint flexibility. To achieve this, an optimized sliding mode controller with a proportional integral derivative surface (SMC-PID) is employed for maneuver control. An improved artificial bee colony algorithm with multi-elite guidance (MGABC) is utilized to obtain optimal values for the sliding surface and switching mode gain and attain the best performance for the robot maneuver system. The selection of the MGABC algorithm is based on its efficient exploration and exploitation techniques. The performance of the optimized SMC-PID robotic system is compared against other optimization algorithms found in existing literature, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Ant Lion Optimizer (ALO), and Grey Wolf Optimizer (GWO). The implemented controller effectively reduces the tracking error to 0.00691 radians, eliminates chattering phenomena in the control effort, and demonstrates robustness against disturbances and noise. The controller ensures that the objective function (OBJF) is minimized, with 0.954% increase in OBJF under low disturbance and noise conditions and 14.55% under severe disturbance and noise conditions. Moreover, the optimized controller exhibits resilience to variations in payload mass analysis, with the percentage increase in OBJF values ranging from 5.726% under low uncertainty conditions to 18.887% under severe uncertainty conditions. Flexible-link maneuvers (FLM) offer advantages such as improved safety and increased operating speeds in real-world applications. In this study, we investigated the impact of joint flexibility on the performance of the FLM system. Our proposed controller demonstrated superior tracking performance, characterized by minimal vibrations in the movement of the end effector.

show abstract

“…The adaptive robust PID controller subjected to the sliding surface was originally introduced, and its stability was mathematically proved by Chang and Yan (2005). It was used to use the advantages of sliding mode control as a robust controller and PID for being straight forward, simple, and applicable; therefore, they were used in accordance to each other (Mahmoodabadi and Khoobroo Haghbayan, 2019; Nejadkourki and Mahmoodabadi, 2019; Mahmoodabadi and Shahangian, 2019; Mahmoodabadi et al, 2015). The original AR-PID was presented for chaotic systems and to avoid the chattering problem of the sliding mode control (Chang and Yan, 2005).…”

Section: Proposed Controllermentioning

confidence: 99%

Fuzzy adaptive robust proportional–integral–derivative control optimized by the multi-objective grasshopper optimization algorithm for a nonlinear quadrotor

Mahmoodabadi

Babak

2020

Journal of Vibration and Control

Self Cite

View full text Add to dashboard Cite

Proportional–integral–derivative is one of the most applicable control methods in industry. Although it is simple and effective in most cases, it does not provide robustness against disturbances and may not perform well in cases with uncertainties and nonlinearities. In this study, a fuzzy adaptive robust proportional–integral–derivative controller is used to control a nonlinear 4 degree-of-freedom quadrotor. An adaptation mechanism is submitted to the proportional–integral–derivative controller for updating the proportional, derivative, and integral gains of proportional–integral–derivative control. Furthermore, a sliding surface is generated and submitted to the adaptation mechanism for better regulation of proportional–integral–derivative gains. Afterward, a fuzzy engine is applied to regulate the sliding surface for better performance of the adaptive proportional–integral–derivative when there are disturbance and uncertainties. The multi-objective grasshopper optimization algorithm is implemented on the control system for the regulation of the control system parameters to minimize the error and control effort of the proposed hybrid control system. Finally, the obtained results are presented for a nonlinear 4 degree-of-freedom multi-purpose (for marine, ground, and aerial maneuvers) quadrotor system designed and built in Sirjan University of Technology, Sirjan, Iran, to assure the effectiveness of this technique.

show abstract

A New Multi-objective Artificial Bee Colony Algorithm for Optimal Adaptive Robust Controller Design

Cited by 20 publications

References 42 publications

Research on residential building duration prediction model based on mean clustering and neural network

Research on residential building duration prediction model based on mean clustering and neural network

Optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output

Fuzzy adaptive robust proportional–integral–derivative control optimized by the multi-objective grasshopper optimization algorithm for a nonlinear quadrotor

Contact Info

Product

Resources

About