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

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

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.

Optimal type-3 fuzzy control and analysis of complicated financial systems

The financial systems have complicated dynamics and are perturbed by various uncertainties and disturbances. Chaos theory provides a practical approach to analyzing financial systems. The chaotic systems have unpredictable random characteristics that help to analyze the financial systems better. Recently, type-3 (T3) fuzzy logic systems (FLSs) have been developed for high-uncertain systems. T3-FLSs provide a reliable tool to cope with high-noisy environments. In T3-FLSs, the upper/lower bounds of uncertainties are fuzzy values. This property results in a strong tool to model more levels of uncertainties. Control, modeling, and forecasting accuracy in financial systems are so important. Then, better systems with higher accuracy are required. In this paper, a new T3-FLS based controller is introduced for chaotic financial systems. By solving a Riccati equation, sufficient conditions are concluded for optimality and robustness. T3-FLSs are learned to minimize the error and stabilize the whole system. A new optimal learning rules are extracted for T3-FLSs. Various benchmark chaotic model of financial systems are considered for examining the efficacy of the introduced approach, and the excellent response and superiority of the suggested approach is verified. Also, a comparison with other methods demonstrates the better efficiency of the suggested scheme.

Interval Type-3 Fuzzy Adaptation of the Bee Colony Optimization Algorithm for Optimal Fuzzy Control of an Autonomous Mobile Robot

In this study, the first goal is achieving a hybrid approach composed by an Interval Type-3 Fuzzy Logic System (IT3FLS) for the dynamic adaptation of α and β parameters of Bee Colony Optimization (BCO) algorithm. The second goal is, based on BCO, to find the best partition of the membership functions (MFs) of a Fuzzy Controller (FC) for trajectory tracking in an Autonomous Mobile Robot (AMR). A comparative with different types of Fuzzy Systems, such as Fuzzy BCO with Type-1 Fuzzy Logic System (FBCO-T1FLS), Fuzzy BCO with Interval Type-2 Fuzzy Logic System (FBCO-IT2FLS) and Fuzzy BCO with Generalized Type-2 Fuzzy Logic System (FBCO-GT2FLS) is analyzed. A disturbance is added to verify if the FBCO-IT3FLS performance is better when the uncertainty is present. Several performance indices are used; RMSE, MSE and some metrics of control such as, ITAE, IAE, ISE and ITSE to measure the controller’s performance. The experiments show excellent results using FBCO-IT3FLS and are better than FBCO-GT2FLS, FBCO-IT2FLS and FBCO-T1FLS in the adaptation of α and β parameters.

Interval Type-3 Fuzzy Control for Automated Tuning of Image Quality in Televisions

In this article, an intelligent system utilizing type-3 fuzzy logic for automated image quality tuning in televisions is presented. The tuning problem can be formulated as controlling the television imaging system to achieve the requirements of production quality. Previously, the tuning process has been carried out by experts, by manually adjusting the television imaging system on production lines to meet the quality control standards. In this approach, interval type-3 fuzzy logic is utilized with the goal of automating the tuning of televisions manufactured on production lines. An interval type-3 fuzzy approach for image tuning is proposed, so that the best image quality is obtained and, in this way, meet quality requirements. A system based on type-3 fuzzy control is implemented with good simulation results. The validation of the type-3 fuzzy approach is made by comparing the results with human experts on the process of electrical tuning of televisions. The key contribution is the utilization of type-3 fuzzy in the image tuning application, which has not been reported previously in the literature.

A Type-2 Fuzzy Controller for Floating Tension-Leg Platforms in Wind Turbines

This paper proposes a type-2 fuzzy controller for floating tension-leg platforms in wind turbines. Its main objective is to stabilize and control offshore floating wind turbines exposed to oscillating motions. The proposed approach assumes that the dynamics of all units are completely unknown. The latter are approximated using the proposed Sugeno-based type-2 fuzzy approach. A nonlinear Kalman-based algorithm is developed for parameter optimization, and linear matrix inequalities are derived to analyze the system’s stability. For the fuzzy system, both rules and membership functions are optimized. Additionally, in the designed approach, the estimation error of the type-2 fuzzy approach is also considered in the stability analysis. The effectiveness and performance of the proposed approach is assessed using a simulation study of a tension leg platform subject to various disturbance modes.