Explore the vast range of titles available.

In practice, inverter dead time and digital signal processing delay are influential factors in classic predictive control performance. In this paper, delay compensation in finite-set model predictive current control (FS-MPCC) and inverter dead-time compensation in deadbeat predictive control are considered to improve permanent magnet synchronous machine control steady-state error, current and torque ripples, reduce the computational burden, correct inverter dead time-induced deviation, and reduce the sensitivity of parameter uncertainties. Deadbeat control uses a space vector modulation (SWM) block and converts the controller output voltage into duty cycles imposed on the inverter ensuring a fixed inverter switching frequency as opposed to FS-MPCC, which uses a finite set of switching states with a variable switching frequency (without modulation). The second approach looks at how a Takagi–Sugeno fuzzy logic speed controller (TS-FLC) can be applied to operate an intelligent system, offering a great current reference, which is crucial for the design of the FS-MPCC cost function and the deadbeat inner-loop control. A speed estimation observer based on a model reference adaptive system (MRAS) is suggested. By eliminating the encoder or speed sensor, the observer improves system reliability, boosts control performance, and reduces costs. The chosen FS-MPCC and deadbeat controller will be built and used in the laboratory utilizing DSpace.1104. The experimental results comparison shows that both FS-MPCC and deadbeat can be well applied to the PMSM driving system with good speed tracking performance. However, the FS-MPCC can achieve less harmonics in the stator currents and shows advantages in smaller ripples in the mechanical torque.

This article proposed a new control strategy based on Takagi–Sugeno fuzzy model for deceasing the power system oscillation. This controller is based on the parallel distributed compensation structure, the stability of the whole closed‐loop model is provided using a general Lyapunov‐Krasovski functional. Also, in this article, a new objective function has been considered to test the proposed Fuzzy Power System Stabilizer in different load conditions which increase the system damping after the system undergoes a disturbance. So, for testing the effectiveness of the proposed controller, the damping factor, damping ratio, and a combination of the damping factor and damping ratio were analyzed and compared with the proposed objective function. The effectiveness of the proposed strategy has been used over 16 machine 68 bus power system. The eigenvalue analysis and nonlinear time domain simulation results proof the effectiveness of the proposed method. © 2015 Wiley Periodicals, Inc. Complexity 21: 288–298, 2016

While type-2 fuzzy control has gained a lot of attention in the recent years, several challenges still remain with respect to controller design. The footprint of uncertainty (FoU) plays a very important role in designing type-2 fuzzy controllers, since the performance of type-2 fuzzy controllers largely depends on the choice of FoU. In this paper, we propose a simplified model of interval type-2 (IT2) fuzzy two-term controllers of Takagi–Sugeno (TS) type with only two rules in the rule base. The controller model is derived with varying FoUs based on only two type-2 input fuzzy sets (the simplest case). An extension to multiple input fuzzy sets is also presented. We investigate the variation in control surface and computational aspects of the IT2 fuzzy two-term controller as the FoU is varied. The performance of the proposed IT2 fuzzy controller with respect to varying FoUs is evaluated in the simulation study.

This paper develops a mathematically grounded neuro-fuzzy control framework for IoT-enabled irrigation systems in precision agriculture. A discrete-time, physically motivated model of soil moisture is formulated to capture the nonlinear water dynamics driven by evapotranspiration, irrigation, and drainage in the crop root zone. A Mamdani-type fuzzy controller is designed to approximate the optimal irrigation strategy, and an equivalent Takagi–Sugeno (TS) representation is derived, enabling a rigorous stability analysis based on Input-to-State Stability (ISS) theory and Linear Matrix Inequalities (LMIs). Online parameter estimation is performed using a Recursive Least Squares (RLS) algorithm applied to real IoT field data collected from a drip-irrigated orchard. To enhance prediction accuracy and long-term adaptability, the fuzzy controller is augmented with lightweight artificial neural network (ANN) modules for evapotranspiration estimation and slow adaptation of membership-function parameters. This work provides one of the first mathematically certified neuro-fuzzy irrigation controllers integrating ANN-based estimation with Input-to-State Stability (ISS) and LMI-based stability guarantees. Under mild Lipschitz continuity and boundedness assumptions, the resulting neuro-fuzzy closed-loop system is proven to be uniformly ultimately bounded. Experimental validation in an operational IoT setup demonstrates accurate soil-moisture regulation, with a tracking error below 2%, and approximately 28% reduction in water consumption compared to fixed-schedule irrigation. The proposed framework is validated on a real IoT deployment and positioned relative to existing intelligent irrigation approaches.

Showing superior performance in many areas, interval type-2 (IT2) fuzzy control has received wide acceptability in the last few years. The improved performance is mainly by virtue of the footprint of uncertainty (FoU) underlying the IT2 fuzzy sets. But the design of IT2 fuzzy controllers remains a challenging task since the FoU increases the computational complexity. We propose in this paper a simplified structure of a novel three-input IT2 Takagi–Sugeno (TS) fuzzy PID controller, a parallel combination of fuzzy PI and fuzzy PD controllers having two inputs, respectively. A novel TS rule base is presented which incorporates uncertainty in both input IT2 fuzzy sets and the rule consequent parameters. The rule base consists of two rules that are logically connected with algebraic product (AP) triangular norm and bounded sum (BS) triangular co-norm. AP and BS operators enable the simplification of the input plane, which in turn reduces the complexity of the controller structure. The effect of FoU on the controller structure is fully investigated. Further, we present the simulation studies on magnetic levitation (MagLev) and cart-pole systems to demonstrate the applicability of the proposed fuzzy PID controller.

The main objective of this paper is to design a robust multi-objective H2/H∞ delayed feedback controller for load frequency control of a multi-area interconnected power system by taking into account all theoretical and practical constraints. To achieve more precise modelling and analysis, the limitation of valve position, governor, and transmission delay are considered to guarantee of LFC system’s stability in practical applications. The nonlinear delayed system is approximated by the Takagi–Sugeno fuzzy model. Then, a parallel distributed compensation scheme is utilized for designing the control system of the overall system. The proposed multi-objective and robust H2/H∞ controller simultaneously minimizes the H2 and H∞ control performance indexes. Finally, simulation results verify the robustness and effectiveness of the proposed scheme in dealing with the impact of load disturbances, model uncertainties, transmission time delays, and nonlinearities in the model.

This paper deals with piecewise constant set-points tracking control of nonlinear discrete-time systems represented by Takagi-Sugeno models under actuators’ saturation. To this end, a fuzzy Proportional Integral-like (PI-like) discrete-time control scheme is considered, which consists of a proportional state feedback, an integral action over the tracking error, and an anti-windup action. All the control gains are obtained through a convex optimization procedure formulated in term of Linear Matrix Inequalities (LMIs). The proposed method yields a Parameter Distributed Compensation (PDC) PI-like control and a non-PDC anti-windup action structure. Due to the actuators’ saturation, a local approach is considered with a fuzzy Lyapunov function to ensure the local closed-loop stability, to provide an estimate of the region of attraction, and to compute the amplitude bounds of set-points changes. This latter issue allows delivering operational security by providing a bounded range for the set-points variation. To validate and illustrate the performance of the proposed tracking control approach, real-time experiments has been performed on an industrial oriented process consisting on the nonlinear level control of two interactive tanks.

In recent years, applications of inverse model-based control techniques have experienced significant growth in popularity and have been widely used in engineering applications, mainly in nonlinear control system design problems. In this study, a novel fuzzy internal model control (IMC) structure is presented for single-input-single-output (SISO) nonlinear systems. The proposed structure uses the forward and inverse dynamic Takagi–Sugeno (D-TS) fuzzy models of the nonlinear system within the IMC framework for the first time in literature. The proposed fuzzy IMC is obtained in a two-step procedure. A SISO nonlinear system is first approximated using a D-TS fuzzy system, of which the rule consequents are linearized subsystems derived from the nonlinear system. A novel approach is used to achieve the exact inversion of the SISO D-TS fuzzy model, which is then utilized as a control element within the IMC framework. In this way, the control design problem is simplified to the inversion problem of the SISO D-TS fuzzy system. The provided simulation examples illustrate the efficacy of the proposed control method. It is observed that SISO nonlinear systems effectively track the desired output trajectories and exhibit significant disturbance rejection performance by using the proposed control approach. Additionally, the results are compared with those of the proportional-integral-derivative control (PID) method, and it is shown that the proposed method exhibits better performance than the classical PID controller.

This paper focuses on the robust stability and the memory feedback control problems of uncertain switched fuzzy systems with mode-dependent multiple time-varying delays. Thus, by constructing a novel Lyapunov functional, the aggregation techniques, and the Borne and Gentina criterion, new algebraic robust stability and stabilization conditions under arbitrary switching have been established. Compared with existing results, the proposed criteria are explicit and simple to apply without searching a Common Lyapunov Function (CLF) through Linear Matrix Inequalities (LMIs), considered a difficult matter in this case. Finally, the practicality and effectiveness of the proposed theory are demonstrated through both a simulation example and a real example (robot arm).

Hybrid optical systems with integrated control mechanisms enable a dynamic adjustment of optical components, ensuring real-time optimization, adaptability to changing conditions, and precise functionality. This control requirement enhances their performance in applications demanding responsiveness, such as autonomous systems, adaptive optics, and advanced imaging technologies. This research introduces a novel approach, employing a dynamic-free Takagi-Sugeno fuzzy sliding mode control (TS-fuzzy SMC) technique, to regulate and stabilize a specific category of chaotic fractional-order modified hybrid optical systems. The method addresses uncertainties and input-saturation challenges within the system. Leveraging a novel fractional calculus definition along with the non-integer type of the Lyapunov stability theorem and linear matrix inequality principle, the TS-fuzzy SMC approach was applied to effectively mitigate and regulate the undesired behavior of the fractional-order chaotic-modified hybrid optical system. Notably, this scheme achieved control without experiencing undesirable chattering phenomena. The paper concludes by offering concrete examples and comparisons, demonstrating how the theoretical findings are applied in real-world scenarios. This provides practical insights into the effectiveness of the proposed approach in diverse applications.