A New Data-Driven Control System for MEMSs Gyroscopes: Dynamics Estimation by Type-3 Fuzzy Systems

Alattas, Khalid A.; Mohammadzadeh, Ardashir; Mobayen, Saleh; Aly, Ayman A.; Felemban, Bassem F.

doi:10.3390/mi12111390

Cited by 22 publications

(6 citation statements)

References 47 publications

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“…T3FL is used to design controllers that can adapt to changing environmental conditions, adjusting their parameters based on real-time information. This allows for more robust and flexible control in situations where systems are nonlinear, unpredictable, or subject to abrupt changes [16][17][18][19][20]. In fields such as robotics, industrial automation, and traffic control systems, T3FL is being explored to enhance the adaptability and responsiveness of systems to changing and variable conditions.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Control Systems through Type-3 Fuzzy Logic Optimization

Ochoa,

Peraza,

Melin

et al. 2024

Mathematics

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The advancement of new tools in the field of control systems is a contemporary development. This work introduces the utilization of Type-3 fuzzy logic, a relatively recent concept that has been applied across various disciplines. In our case, a Type-3 fuzzy system is designed to enhance the optimization of parameters within the harmony search algorithm, specifically tailored for a control problem. Through a series of experiments, the efficacy of this novel Type-3 fuzzy logic tool is put to the test. Previous studies have primarily explored Type-1 and Type-2 fuzzy logic. To assess the performance of this new Type-3 fuzzy logic tool, a comparative analysis of results is conducted using statistical testing. The introduction of Type-3 fuzzy logic in the control domain represents a novel and innovative approach. This approach extends beyond the conventional Type-1 and Type-2 fuzzy logic, showcasing the dynamic evolution in the field. Results obtained through experimentation are analyzed, and statistical tests are employed to determine whether the Type-3 fuzzy logic tool yields superior outcomes compared to its predecessors. By doing so, this study contributes to the growing body of research that explores the potential benefits of Type-3 fuzzy logic and its application in control systems, offering new perspectives and opportunities for further advancements in the field. We have to mention that the utilization of Type-3 fuzzy logic in enhancing metaheuristics is a relatively new trend, and in this work, this research has extended this to the realm of harmony search. In addition, the application of the optimal design of the ball-and-beam fuzzy controllers has not been previously carried out with the Type-3 fuzzy harmony search algorithm, which is the novelty of this study.

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Section: Introductionmentioning

confidence: 99%

Enhancing Control Systems through Type-3 Fuzzy Logic Optimization

Ochoa,

Peraza,

Melin

et al. 2024

Mathematics

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“…In recent years, 2019-2022, important and novel papers were developed [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35],…”

Section: Introductionmentioning

confidence: 99%

A New Method for the Design of Interval Type-3 Fuzzy Logic Systems With Uncertain Type-2 Non-Singleton Inputs (IT3 NSFLS-2): A Case Study in a Hot Strip Mill

Mendez¹,

López-Juárez

Dorantes³

et al. 2023

IEEE Access

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This paper presents a new method for the construction and training of interval type-3 fuzzy logic systems whose inputs are uncertain type-2 non-singleton numbers (IT3 NSFLS-2). The proposed methodology is divided in two processes: 1) The novel construction of the structure of the IT3 NSFLS-2 systems based on: a) The level-alpha-0 of the interval type-2 fuzzy logic system (IT2-alpha-0 FLS), and on b) The secondary membership function using Gaussian modeling to construct each rule of the alpha-k fuzzy rule base (FRB), the firing intervals of the antecedent and the centroids of the consequent, and 2) The training methodology based on gradient descent algorithm to train the antecedent and consequent parameters of the alpha-0 FRB. The primary membership functions (MF) of the antecedents of the IT3 NSFLS-2 system are modeled as Gaussians with uncertain means and with common standard deviation. The proposal was applied and tested with the prediction of a transfer bar's surface temperature in an industrial hot strip mill facility located in Monterrey México. The modeling results show that the proposal supports the stability required by this critical process and shows the best performance when compared with similar methods.INDEX TERMS General type-2 fuzzy systems, gradient descent algorithm, hot strip mill, interval type-2 fuzzy systems, interval type-3 fuzzy systems, temperature prediction, type-2 non-singleton inputs.

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“…Furthermore, advanced control methods for MEMS gyroscopes can greatly enhance the control effectiveness. These methods include the data-driven control scheme, the nonsingular terminal sliding mode control method, an approximation-based adaptive fractional sliding mode control scheme, and so on [ 28 , 29 , 30 ]. Hence, research on stiffness coupling and system phase error is crucial for further constraining the SF nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

Interactive Errors Analysis and Scale Factor Nonlinearity Reduction Methods for Lissajous Frequency Modulated MEMS Gyroscope

Li,

Wang,

Yan

et al. 2023

Sensors

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Although the Lissajous frequency modulated (LFM) mode can improve the long-term and temperature stability of the scale factor (SF) for mode mismatch MEMS gyroscopes, its SF nonlinearity poses a significant limitation for full-scale accuracy maintenance. This paper examines the interaction effects among stiffness coupling, system phase delay, readout demodulation phase shift, and velocity amplitude mismatch within the control process. Based on the completion of frequency difference control and demodulation phase matching, we clarify that the remaining stiffness coupling and residual system phase error are the primary factors influencing SF nonlinearity. Furthermore, SF nonlinearity is reduced through error compensation. On one hand, this paper suppresses stiffness coupling through the observation of the instantaneous frequency difference and the application of the quadrature voltage. On the other hand, system phase error is compensated by observing the amplitude control force and tuning the reference in the Phase-Locked Loops (PLLs). Subsequent simulations of these methods demonstrated a remarkable 97% reduction in SF nonlinearity within the measurement range of ±500°/s. In addition, an observed rule dictates that maintaining a sufficiently large frequency split effectively constrains the SF nonlinearity.

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A New Data-Driven Control System for MEMSs Gyroscopes: Dynamics Estimation by Type-3 Fuzzy Systems

Cited by 22 publications

References 47 publications

Enhancing Control Systems through Type-3 Fuzzy Logic Optimization

Enhancing Control Systems through Type-3 Fuzzy Logic Optimization

A New Method for the Design of Interval Type-3 Fuzzy Logic Systems With Uncertain Type-2 Non-Singleton Inputs (IT3 NSFLS-2): A Case Study in a Hot Strip Mill

Interactive Errors Analysis and Scale Factor Nonlinearity Reduction Methods for Lissajous Frequency Modulated MEMS Gyroscope

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