Temperature Control Design with Differential Evolution Based Improved Adaptive-Fuzzy-PID Techniques

Kaliappan, Prabhu Shankar; Illango, Aravindguru; Muthusamy, Sivachitra; Sembanan, Banumathi

doi:10.32604/iasc.2023.030047

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…A number of studies by various researchers are available in the literature to handle the local optima issue, balance between exploration and exploitation, improve the convergence speed and improve the solution quality of the DE algorithm. A few of the variants introduced by researchers include tournament selection-based DE [10], rank-based DE [11], fuzzy-based DE [12], self-adaptive DE [13], adaptive DE [14], and Pool-based DE [15] to maintain the balance between exploration and exploitation as well as to improve the convergence performance of the DE algorithm in their research work.…”

Section: Major Contributions Of Studymentioning

confidence: 99%

Differential Evolution Using Enhanced Mutation Strategy Based on Random Neighbor Selection

Baig,

Abbas,

Ahmad

et al. 2023

Symmetry

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Symmetry in a differential evolution (DE) transforms a solution without impacting the family of solutions. For symmetrical problems in differential equations, DE is a strong evolutionary algorithm that provides a powerful solution to resolve global optimization problems. DE/best/1 and DE/rand/1 are the two most commonly used mutation strategies in DE. The former provides better exploitation while the latter ensures better exploration. DE/Neighbor/1 is an improved form of DE/rand/1 to maintain a balance between exploration and exploitation which was used with a random neighbor-based differential evolution (RNDE) algorithm. However, this mutation strategy slows down convergence. It should achieve a global minimum by using 1000 × D, where D is the dimension, but due to exploration and exploitation balancing trade-offs, it can not achieve a global minimum within the range of 1000 × D in some of the objective functions. To overcome this issue, a new and enhanced mutation strategy and algorithm have been introduced in this paper, called DE/Neighbor/2, as well as an improved random neighbor-based differential evolution algorithm. The new DE/Neighbor/2 mutation strategy also uses neighbor information such as DE/Neighbor/1; however, in addition, we add weighted differences after various tests. The DE/Neighbor/2 and IRNDE algorithm has also been tested on the same 27 commonly used benchmark functions on which the DE/neighbor/1 mutation strategy and RNDE were tested. Experimental results demonstrate that the DE/Neighbor/2 mutation strategy and IRNDE algorithm show overall better and faster convergence than the DE/Neighbor/1 mutation strategy and RNDE algorithm. The parametric significance test shows that there is a significance difference in the performance of RNDE and IRNDE algorithms at the 0.05 level of significance.

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Section: Major Contributions Of Studymentioning

confidence: 99%

Differential Evolution Using Enhanced Mutation Strategy Based on Random Neighbor Selection

Baig,

Abbas,

Ahmad

et al. 2023

Symmetry

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show abstract

“…It is easy to realize and performs well in finding the best solutions to complex optimization problems [ 28 , 29 ]. There are three main steps of the differential evolution algorithm, including the mutation process, the crossover process, and the selection process [ 30 ]. Recently, by using the differential evolution algorithm and designing a specific mutation strategy, Stanovov et al.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Moisture Ratio Model of Cantaloupe Slices by Maximum Likelihood Principle-Based Algorithms

Zhu

Raghavan

2023

Plants

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As an agricultural plant, the cantaloupe contains rich nutrition and high moisture content. In this paper, the estimation problem of the moisture ratio model during a cantaloupe microwave drying process was considered. First of all, an image processing-based cantaloupe drying system was designed and the expression of the moisture ratio with regard to the shrinkage was built. Secondly, a maximum likelihood principle-based iterative evolution (MLP-IE) algorithm was put forward to estimate the moisture ratio model. After that, aiming at enhancing the model fitting ability of the MLP-IE algorithm, a maximum likelihood principle-based improved iterative evolution (MLP-I-IE) algorithm was proposed by designing the improved mutation strategy, the improved scaling factor, and the improved crossover rate. Finally, the MLP-IE algorithm and MLP-I-IE algorithm were applied for estimating the moisture ratio model of cantaloupe slices. The results showed that both the MLP-IE algorithm and MLP-I-IE algorithm were effective and that the MLP-I-IE algorithm performed better than the MLP-IE algorithm in model estimation and validation.

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“…Furthermore, a fuzzy logic inference mechanism removes the chattering issue from conventional sliding mode control by employing a hitting control law. Currently, thanks to the robust development of engineering software, virtual prototyping technology can simulate and evaluate real system performance without experiments, lowering manufacturing costs and errors while ensuring product quality [30][31][32][33][34][35][36]. The tracking control performance of the EHA is evaluated through a virtual model created using Amesim software.…”

Section: Introductionmentioning

confidence: 99%

Development of an Adaptive Fuzzy Sliding Mode Controller of an Electrohydraulic Actuator Based on a Virtual Prototyping

2023

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The EHA (electro hydraulic actuator) has a notable advantage over conventional hydraulic actuators as it uses a closed-loop circuit, reducing the size and volume of oil, and eliminates pressure losses caused by valve orifices. However, accurate control performance of EHA is difficult to achieve using a traditional PID (proportional integral derivative) controller due to the strongly nonlinear, time-varying, and unknown dynamics of the system. Hence this paper seeks to address this problem by proposing a design of an intelligent controller for the EHA. The proposed adaptive fuzzy sliding mode controller (AFSMC) is developed as a hybrid of the adaptive, fuzzy logic, and sliding mode algorithms. To reduce costs and time, a virtual prototype approach is also proposed instead of experimentations to evaluate the performance of the proposed controller. The virtual model of the EHA is built in Amesime software, and then embedded into Matlab/Simulink where the AFSMC is developed and tested to obtain the position responses of the EHA. The results show that the AFSMC is highly successful and more efficient than the traditional PID at controlling the position of the piston accurately.

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Temperature Control Design with Differential Evolution Based Improved Adaptive-Fuzzy-PID Techniques

Cited by 4 publications

References 21 publications

Differential Evolution Using Enhanced Mutation Strategy Based on Random Neighbor Selection

Differential Evolution Using Enhanced Mutation Strategy Based on Random Neighbor Selection

Estimating the Moisture Ratio Model of Cantaloupe Slices by Maximum Likelihood Principle-Based Algorithms

Development of an Adaptive Fuzzy Sliding Mode Controller of an Electrohydraulic Actuator Based on a Virtual Prototyping

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