A novel self-learning fuzzy predictive control method for the cement mill: Simulation and experimental validation

Ma, Tianyu; Li, Zhipeng; Liu, Jinping; Alkhateeb, Abdulhameed F.; Jahanshahi, Hadi

doi:10.1016/j.engappai.2023.105868

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…Mohammed et al (2023) applied decision matrices in intelligent e-tourism systems and proposed an extended new formula called spherical fuzzy rough weighted zero inconsistency, which is used to handle situations of different categories. Ma et al (2023) proposed a self-learning fuzzy predictive control algorithm for calculating the adjustment variables in the cement grinding process. This algorithm is based on step response trained on differential data and utilizes Apriori algorithm and strength prediction model to mine fuzzy rules.…”

Section: Related Workmentioning

confidence: 99%

Multi-task and multi-stage commodity sorting algorithm for distributed e-commerce logistics system

Gao,

Yang,

2024

Preprint

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With the increasing scale of e-commerce and logistics, factors such as the types, quantities, and volumes of goods sorted in logistics have made the sorting process more difficult. A single serial logistics workshop sorting method cannot meet the growing business needs, with low sorting speed and inability to make reasonable use of sorting machine resources. This article proposes a distributed logistics system commodity sorting algorithm, which sorts multiple types of goods through multiple machines and divides the sorting task into multiple stages for processing. We present the Distributed Dynamic Programming Memetic System (DDPMS): (1) Parallel sorting process architecture realizes multi-stage parallel acceleration, and dynamic programming algorithm plans the execution order of commodity sorting tasks to reduce total sorting time. (2) The meme algorithm first initializes the sorting machine scheduling scheme through an improved dragonfly algorithm, and the mutation operator prevents the solution from falling into local optima. (3) And a local reinforcement search optimization solution based on fuzzy Q-Learning was designed, using the Softmax Green strategy to cope with more complex machine resource sizes and quantities. Improve resource utilization, reduce sorting time, and ensure system load balancing. Through experimental verification, the execution efficiency of distributed sorting technology has been improved by 2 and 4 times compared to traditional algorithms; By comparing the improved Dragonfly algorithm with different numbers of sorting machines and single machine instances, the algorithm converges to a more optimal solution compared to the baseline algorithm. Fuzzy Q-Learning also has advantages in terms of search ability and efficiency.

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Section: Related Workmentioning

confidence: 99%

Multi-task and multi-stage commodity sorting algorithm for distributed e-commerce logistics system

Gao,

Yang,

2024

Preprint

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“…Numerous methods falling under the umbrella of predictive control have been presented so far, leading to better, more accurate development and treatment. The most significant claim of this algorithm is its applicability to non-linear processes and its capability to maintain control [20]. Its ability to manage non-linear functions, which may vary over time, and under conditions with various restrictions on process variables, distinguishes these controllers as unique and superior compared to other methods.…”

Section: Introductionmentioning

confidence: 99%

A Constrained Fuzzy Control for Robotic Systems

Xue,

Zhou,

Chen

et al. 2024

IEEE Access

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The use of wheeled mobile robots (MRs) with symmetrical structure in engineering is rapidly increasing, with applications in various fields, such as industry, agriculture, forestry, healthcare, mining, rehabilitation, search and rescue, household tasks, remote locations, and entertainment. As MRs become more common, researchers are focusing on developing better ways to model and control these robots to improve their performance and adaptability. The main challenges in this area include uncertain dynamics, non-holonomic constraints, and various perturbations, which complicate the design of the control system. This paper presents a new predictive control scheme for MRs that is independent of the dynamics and the robot's working environment. A Type-3 fuzzy logic system is developed to identify the MR dynamics online. The designed predictive scheme improves accuracy and speeds up convergence, while also addressing uncertainties and considering constraints on control input. Additionally, a chaotic-based system is proposed for secure path planning, generating a complex and unpredictable reference trajectory that is useful for patrol MR applications. The effectiveness of the suggested controller is demonstrated through simulations and experiments.

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Adaptive sliding mode controller based on fuzzy rules for a typical excavator electro-hydraulic position control system

Feng,

Jiang,

Chang

et al. 2023

Engineering Applications of Artificial Intelligence

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A novel self-learning fuzzy predictive control method for the cement mill: Simulation and experimental validation

Cited by 6 publications

References 26 publications

Multi-task and multi-stage commodity sorting algorithm for distributed e-commerce logistics system

Multi-task and multi-stage commodity sorting algorithm for distributed e-commerce logistics system

A Constrained Fuzzy Control for Robotic Systems

Adaptive sliding mode controller based on fuzzy rules for a typical excavator electro-hydraulic position control system

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