Hybrid fuzzy control for the goethite process in zinc production plant combining type-1 and type-2 fuzzy logics

Xie, Shiwen; Xie, Yongfang; Li, Fanbiao; Jiang, Zhaohui; Gui, Weihua

doi:10.1016/j.neucom.2019.06.089

Cited by 21 publications

(10 citation statements)

References 32 publications

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“…Naderi [22] used two rule-based fuzzy reasoning systems based on the Mamdani-type and TSK model to predict oil economic variables and confirmed the performance of the model using the RMSE metric. Xie [23] proposed a hybrid fuzzy control method by combining a type-1 fuzzy logic controller and a type-2 fuzzy logic controller and confirmed the performance of the model in terms of the RMSE metric. Altunkaynak [24] predicted river levels using combined DWT-fuzzy and CWT-fuzzy models and confirmed the resultant model's performance using the RMSE metric.…”

Section: Of 17mentioning

confidence: 80%

Performance Evaluation of Automobile Fuel Consumption Using a Fuzzy-Based Granular Model with Coverage and Specificity

Yeom

Kwak

2019

Symmetry

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The predictive performance of different granular models (GMs) was compared and analyzed for methods that evenly divide linguistic context in information granulation-based GMs and perform flexible partitioning. GMs are defined by input and output space information transformations using context-based fuzzy C-means clustering. The input space information transformation is directly induced by the output space context. Usually, the output space context is evenly divided. In this paper, the linguistic context was flexibly divided by stochastically distributing data in the output space. Unlike most fuzzy models, this GM yielded information segmentation. Their performance is usually evaluated using the root mean square error, which utilizes the difference between the model’s output and ground truth. However, this is inadequate for the performance evaluation of information innovation-based GMs. Thus, the GM performance was compared and analyzed using the linguistic context partitioning by selecting the appropriate performance evaluation method for the GM. The method was augmented by the coverage and specificity of the GMs output as the performance index. For the GM validation, its performance was compared and analyzed using the auto MPG dataset. The GM with flexible partitioning of linguistic context performed better. Performance evaluation using the coverage and specificity of the membership function was validated.

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Section: Of 17mentioning

confidence: 80%

Performance Evaluation of Automobile Fuel Consumption Using a Fuzzy-Based Granular Model with Coverage and Specificity

Yeom

Kwak

2019

Symmetry

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“…Step 3. SMA is integrated using steps SMA1 to SMA7 and (28) in solving the optimization problem defined in (11) leading to the optimal parameter vector * in (12) and three of the optimal parameters of TSK PI-FC, namely * , B * e and * . Step 4.…”

Section: Sma and Fuzzy Controller Tuning Approachmentioning

confidence: 99%

“…Such challenging optimization problems are those specific to the optimal (parameter) tuning of fuzzy (logic) controllers, where both the process and the controller are nonlinear and deterministic algorithms are not successful. The following metaheuristic algorithms have been applied most recently to the optimal tuning of fuzzy controllers in representative examples: adaptive weight Genetic Algorithm (GA) for gear shifting control [3], GA-based multiobjective optimization for electric vehicle powertrain control [4], GA for hybrid power systems control [5], engines control [6], energy management in hybrid vehicles [7], servo system control [2], wellhead back pressure control systems [8], micro-unmanned helicopter control [9], Particle Swarm Optimization (PSO) algorithm with compensating coefficient of inertia weight factor for filter time constant adaptation in hybrid energy storage systems control [10], set-based PSO algorithm with adaptive weights for optimal path planning of unmanned aerial vehicles [11], PSO algorithm for zinc production [12] and inverted pendulum control [13], hybrid PSO-Artificial Bee Colony algorithm for frequency regulation in microgrids [14], Imperialist Competitive Algorithm for human immunodeficiency control [15], Grey Wolf Optimizer (GWO) algorithms for sun-tracker systems [16] and servo system control [2], PSO, Cuckoo Search and Differential Evolution (DE) for gantry crane systems position control [17], Whale Optimization Algorithm (WOA) for vibration control of steel structures [18], Grasshopper Optimization Algorithm for load frequency control [19], DE for electro-hydraulic servo system control [20], Gravitational Search Algorithm (GSA) and Charged System Search (CSS) for servo system control [2].…”

Section: Introductionmentioning

confidence: 99%

Slime Mould Algorithm-Based Tuning of Cost-Effective Fuzzy Controllers for Servo Systems

Precup¹,

David²,

Roman³

et al. 2021

IJCIS

101

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This paper suggests five new contributions with respect to the state-of-the-art. First, the optimal tuning of cost-effective fuzzy controllers represented by Takagi-Sugeno-Kang proportional-integral fuzzy controllers (TSK PI-FCs) is carried out using a fresh metaheuristic algorithm, namely the Slime Mould Algorithm (SMA), and a fuzzy controller tuning approach is offered. Second, a relatively easily understandable formulation of SMA is offered. Third, a real-world application of SMA is given, focusing on the optimal tuning of TSK PI-FCs for nonlinear servo systems in terms of optimization problems that target the minimization of discrete-time cost functions defined as the sum of time multiplied by squared control error. Fourth, using the concept of improving the performance of metaheuristic algorithms with information feedback models, proposed by Wang and Tan, Improving metaheuristic algorithms with information feedback models, IEEE Trans. Cybern. 49 (2019), 542-555, Gu and Wang, Improving NSGA-III algorithms with information feedback models for large-scale many-objective optimization, Fut. Gen. Comput. Syst. 107 (2020), 49-69, and Zhang et al., Enhancing MOEA/D with information feedback models for large-scale many-objective optimization, Inf. Sci. 522 (2020), 1-16, new metaheuristic algorithms are introduced in terms of inserting the model F1 in SMA and other representative algorithms, namely Gravitational Search Algorithm (GSA), Charged System Search (CSS), Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA). Fifth, the real-time validation of the cost-effective fuzzy controllers and their tuning approach is performed in the framework of angular position control of laboratory servo system. The comparison with other metaheuristic algorithms that solve the same optimization problem for optimal parameter tuning of cost-effective fuzzy controllers suggestively highlights the superiority of SMA. Experimental results are included.

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“…Ref. [32] designed a hybrid fuzzy control strategy for the goethite process and showed better control performance than PID controller. Different form our research, we mainly study the interaction between systems to achieve couple-group consensus.…”

Section: Introductionmentioning

confidence: 99%

Couple-Group Consensus for First Order Discrete-Time Multiagent Systems With Competition-Cooperation and Input Saturation Constraints

et al. 2021

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Hybrid fuzzy control for the goethite process in zinc production plant combining type-1 and type-2 fuzzy logics

Cited by 21 publications

References 32 publications

Performance Evaluation of Automobile Fuel Consumption Using a Fuzzy-Based Granular Model with Coverage and Specificity

Performance Evaluation of Automobile Fuel Consumption Using a Fuzzy-Based Granular Model with Coverage and Specificity

Slime Mould Algorithm-Based Tuning of Cost-Effective Fuzzy Controllers for Servo Systems

Couple-Group Consensus for First Order Discrete-Time Multiagent Systems With Competition-Cooperation and Input Saturation Constraints

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