Takagi-Sugeno Fuzzy Regulator Design for Nonlinear and Unstable Systems Using Negative Absolute Eigenvalue Approach

This paper introduces a Takagi-Sugeno(T-S)fuzzy regulator design using the negative absolute eigenvalue(NAE)approach for a class of nonlinear and unstable systems.The open-loop system is initially embodied by the traditional T-S fuzzy model and then,all closed-loop subsystems are combined using the proposed Max-Min operator in place of traditional weighted average operator from the controller side to lessen the coupling virtually and simplify the proposed regulator design.For each virtually decoupled closed-loop subsystem,the composite regulators(i.e.,primary and secondary regulators)are designed by the NAE approach based on the enhanced eigenvalue analysis.The Lyapunov function is utilized to guarantee the asymptotic stability of the overall T-S fuzzy control system.The most popular and widely used nonlinear and unstable systems like the electromagnetic levitation system(EMLS)and the inverted cart pendulum(ICP)are simulated for the wide range of the initial conditions and the enormous variation in the disturbance.The transient and steady-state performance of the considered systems using the proposed design are analyzed in terms of the decay rate,settling time and integral errors as IAE,ISE,ITAE,and ITSE to validate the effectiveness of the proposed approach compared to the most popular and traditional parallel distributed compensation(PDC)approach.

Geomagnetic Orbit Determination Using Fuzzy Regulating Unscented Kalman Filter

Geomagnetic orbit determination fits for nanosatellites which pursue low cost and high-density ratio,but one of its disadvantages is the poor position accuracy introduced by magnetic bias.Here,a new method,named the fuzzy regulating unscented Kalman filter(FRUKF),is proposed.The magnetic bias is regarded as a random walk model,and a fuzzy regulator is designed to estimate the magnetic bias more accurately.The input of the regulator is the derivative of magnetic bias estimated from unscented Kalman filter(UKF).According to the fuzzy rule,the process noise covariance is adaptively determined.The FRUKF is evaluated using the real-flight data of the SWARMA.The experimental results show that the root-mean-square(RMS)position error is 3.1 km and the convergence time is shorter than the traditional way.

Double-Fed Asynchronous Wind Power Generator by Fuzzy Logic

The objective of this study is the simulation of the fuzzy control of a double-fed induction wind generator （DFIG） and oriented stator flux using MATLAB/S[MULINK. The system consists of a horizontal wind turbine, coupled to a DFIG and driven by a feed consisting of two three＋phase PWM converters in double bridge in order to maximize the delivered power at different wind speeds. We used a traditional regulator and a fuzzy controller. A model of the wind speed and the wind turbine is presented followed by the modeling of double-fed asynchronous generator DFIG controlled by fuzzy logic is performed according to two scenarios： with and without MPPT.

Analysing Various Control Techniques for Manipulator Robotic System (Robogymnast)

The Robogymnast is a highly complex,three-link system based on the triple-inverted pendulum and is modelled on the human example of a gymnast suspended by their hands from the high bar and executing larger and larger upswings to eventually rotate fully.The links of the Robogymnast correspond respectively to the arms,trunk,and lower limbs of the gymnast,and from its three joints,one is under passive operation,while the remaining two are powered.The passive top joint poses severe challenges in attaining the smooth movement control needed to operate the Robogymnast effectively.This study assesses four types of controllers used for systems operation and identifies how far response stabilisation is achieved with each.The system is simulated using MATLAB Simulink,with findings generated regarding rising and settling time,as well as overshoot.The research primarily seeks to exam-ine the application of a linear quadratic regulator controller,proportional-integral-derivative controller,fuzzy linear quadratic regulator controller and linear quadratic regulator-proportional-integral-derivative controller for this type of system and comparisons between the different controllers to demon-strate successful performance,which highlights the claimed advantages of the proposed system.

Use of Artificial Intelligence in the Issue of Protection against Negative Impact of Floods

The paper follows possible specification of a control algorithm of a WS （water management system） during floods using the procedures of AI （artificial intelligence）. The issue of minimizing negative impacts of floods represents influencing and controlling a dynamic process of the system where the main regulation elements are water reservoirs. Control of water outflow from reservoirs is implicitly based on the used model （titled BW） based on FR （fuzzy regulation）. Specification of a control algorithm means dealing with the issue of preparing a knowledge base for the process of tuning fuzzy regulators based on an I/O （input/output） matrix obtained by optimization of the target behaviour of WS. Partial results can be compared with the regulation outputs when specialized tuning was used for the fuzzy regulator of the control algorithm. Basic approaches follow from the narrow relation on BW model use to simulate floods, without any connection to real water management system. A generally introduced model allows description of an outflow dynamic system with stochastic inputs using submodels of robust regression in the outflow module. The submodels are constructed on data of historical FS （flood situations）.

A PRODUCTION OPTIMIZATION MODEL OF SUPPLY-DRIVEN CHAIN WITH QUALITY UNCERTAINTY

Supply-driven chain＇s production is different from traditional demand-driven production because its supplies must guide the full production flow toward the markets and respond actively to customer demand. According to the control tlaeory, a novel multi-variable operation model of supply-driven chain is discussed here, integrating suppliers, manufacturers, distributors and market demands. Especially the coordination problem between suppliers and manufacturers is discussed where suppliers play more important role than manufacturers. Because defect is common in real production system, the production operation of supply-driven chain with imperfect quality is described on the basis of fuzzy set to model the ambiguity of quality and to provide appropriate supply coordination mechanism. In a designed numerical example, it is apparent that both response and robustness performances of supply-driven production system on demand with imperfect quality are improved by a fuzzy proportional-integral- differential regulator. The proposed model may apply to similar productions with imoerfect quality.