Search Results (91)

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. Full article

Hydraulic manipulators are vital for heavy-duty applications such as rescue robotics due to their high power density, yet these scenarios increasingly demand safe and compliant physical interaction. Impedance control is a key enabling technology for such capabilities. However, a significant challenge arises when implementing impedance control on Independent Metering Systems (IMS), which are widely adopted for their energy efficiency. The inherent multi-mode operation of IMS relies on discrete switching logic. Crucially, when mode switching occurs during physical interaction with the environment, the unpredictable external forces can trigger frequent and abrupt switching between operating modes (e.g., resistive and overrunning), leading to severe chattering. This phenomenon not only undermines the smooth interaction that impedance control aims to achieve but also jeopardizes overall system stability. To address this critical issue, this paper proposes a seamless control framework based on a Takagi–Sugeno (T-S) fuzzy model. Two premise variables based on the physical characteristics of the system are innovatively designed to make the rule division highly consistent with the dynamic nature of the system. Asymmetric membership functions are introduced to handle direction-dependent switching, with orthogonal functions ensuring logical exclusivity between extension and retraction, and smooth complementary functions enabling seamless transitions between resistance and overrunning modes. Experimental validation on a small hydraulic manipulator validates the effectiveness of the proposed method. The controller eliminates switching-induced instability and smooths velocity transitions, even under dynamic external force disturbances. This work provides a crucial solution for high-performance, stable hydraulic interaction control, paving the way for the application of hydraulic robots in complex and dynamic environments. Full article

Hybrid synchronization control (HSC) has recently attracted considerable attention owing to its superior transient stability and adaptability to varying grid strengths. However, existing studies on HSC employ diverse control strategies for the Phase-Locked Loop (PLL) and the voltage control loop (VCL). Since both the PLL and VCL are associated with the q-axis component of the point of common coupling (PCC) voltage, the coupling effect between these two control loops and the impact of different controller configurations on system transient stability remain to be further explored. To address this gap, this study first analyzes the transient characteristics of the system under different PLL-VCL control combinations using the power-angle curve method. Subsequently, a Lyapunov stability criterion is established based on the Takagi–Sugeno (T-S) fuzzy model, enabling the estimation of the region of asymptotic stability (RAS). By comparing the RAS of different control combinations, the influence of the proportional coefficient in HSC on transient stability is quantitatively investigated. Finally, PSCAD electromagnetic transient simulations are carried out to verify the validity and accuracy of the theoretical analysis results. Full article

This review presents a synthesis of fuzzy logic-based (FL) controllers applied to photovoltaic (PV) systems over the last decade, with a specific focus on maximum power point tracking (MPPT) and power management. These subsystems are critical for improving the efficiency of PV energy conversion, as they directly address the nonlinear, time-varying, and uncertain behavior of solar generation under dynamic environmental conditions. FL-based control has proven to be a powerful and versatile tool for enhancing MPPT accuracy, inverter performance, and hybrid energy management strategies. The analysis concentrates on three main categories, namely, Mamdani, Takagi–Sugeno (T-S), and Type-2, highlighting their architectures, operational characteristics, and application domains. Mamdani controllers remain the most widely adopted due to their simplicity, interpretability, and effectiveness in scenarios with moderate response time requirements. T-S controllers excel in real-time high-frequency operations by eliminating the defuzzification stage and approximating system nonlinearities through local linear models, achieving rapid convergence to the maximum power point (MPP) and improved power quality in grid-connected PV systems. Type-2 fuzzy controllers represent the most advanced evolution, incorporating footprints of uncertainty (FOU) to handle high variability, sensor noise, and environmental disturbances, thereby strengthening MPPT accuracy under challenging conditions. This review also examines the integration of metaheuristic algorithms for automated tuning of membership functions and hybrid architectures that combine fuzzy control with artificial intelligence (AI) techniques. A bibliometric perspective reveals a growing research interest in T-S and Type-2 approaches. Quantitatively, Mamdani controllers account for 54.20% of publications, T-S controllers for 26.72%, and Type-2 fuzzy controllers for 19.08%, reflecting the balance between interpretability, computational performance, and robustness to uncertainty in PV-based MPPT and power management applications. Full article

This paper introduces a novel strategy for the diagnosis and fault-tolerant control (FTC) of inter-turn short-circuit (ITSC) faults in the stator windings of Doubly Fed Induction Generator (DFIG)-based wind turbines. ITSC faults are among the most common electrical issues in rotating machines: early detection is therefore essential to reduce maintenance costs and prevent severe damage to the wind turbine system. To address this, a Fault Detection and Diagnosis (FDD) approach is proposed to identify and assess the severity of ITSC faults in the stator windings. A state-space model of the DFIG under ITSC fault conditions is first developed in the (d,q) reference frame. Based on this model, an Unknown Input Observer (UIO) structured using Takagi–Sugeno (T-S) fuzzy models is designed to estimate the fault level. To mitigate the impact of the fault and ensure continued operation under degraded conditions, a T-S fuzzy fault-tolerant controller is synthesized. This controller enables natural decoupling and optimal power extraction across a wide range of rotor speed variations. Since the effectiveness of the FTC relies on accurate fault information, a Proportional-Integral Observer (PIO) is employed to estimate the ITSC fault level. The proposed diagnosis and compensation strategy is validated through simulations performed on a 3 kW wind turbine system, demonstrating its efficiency and robustness. Full article

This study presents a novel optimal fuzzy logic control (FLC) strategy based on feedback linearization for the regulation of multivariable nonlinear systems. Building upon an enhanced Takagi–Sugeno (T-S) model previously developed by the authors, the proposed method incorporates a refined parameter-weighting scheme to optimize both local and global approximations within the T-S framework. This approach enables improved selection and minimization of the performance index. The effectiveness of the control strategy is validated through its application to a two-link serial robotic manipulator. The results demonstrate that the proposed FLC achieves robust performance, maintaining system stability and high accuracy even under the influence of noise and load disturbances, with well-damped system behavior and negligible steady-state error. Full article

This work presents the design of a control algorithm based on an augmented incremental state-space model, emphasizing its compatibility with Takagi–Sugeno (T–S) fuzzy models for nonlinear systems. The methodology integrates key components such as incremental modeling, fuzzy system identification, discrete Linear Quadratic Regulator (LQR) design, and state observer implementation. To optimize controller performance, the Extremum Seeking Control (ESC) technique is employed for the automatic tuning of LQR gains, minimizing a predefined cost function. The control strategy is formulated within a generalized framework that evolves from conventional discrete fuzzy models to a higher-order incremental-N state-space representation. The simulation results on a nonlinear multivariable thermal mixing tank system validate the effectiveness of the proposed approach under reference tracking and various disturbance scenarios, including ramp, parabolic, and higher-order polynomial signals. The main contribution of this work is that the proposed scheme achieves zero steady-state error for reference inputs and disturbances up to order N−1 by employing the incremental-N formulation. Furthermore, the system exhibits robustness against input and load disturbances, as well as measurement noise. Remarkably, the ESC algorithm maintains its effectiveness even when noise is present in the system output. Additionally, the proposed incremental-N model is applicable to fast dynamic systems, provided that the system dynamics are accurately identified and the model is discretized using a suitable sampling rate. This makes the approach particularly relevant for control applications in electrical systems, where handling high-order reference signals and disturbances is critical. The incremental formulation, thus, offers a practical and effective framework for achieving high-performance control in both slow and fast nonlinear multivariable processes. Full article

This study presents advanced control and energy management strategies for uncertain wind energy systems using a Takagi–Sugeno (T-S) fuzzy modeling framework. To address key challenges, such as system uncertainties, external disturbances, and input delays, the study integrates a fuzzy H∞ robust control approach with a neural network-based delay compensation mechanism. A fuzzy observer-based H∞ tracking controller is developed to enhance robustness and minimize the impact of disturbances. The stability conditions are rigorously derived using a quadratic Lyapunov function, H∞ performance criteria, and Young’s inequality and are expressed as Linear Matrix Inequalities (LMIs) for computational efficiency. In parallel, a neural network-based controller is employed to compensate for the input delays introduced by online learning processes. Furthermore, an energy management layer is incorporated to regulate the power flow and optimize energy utilization under varying operating conditions. The proposed framework effectively combines control and energy coordination to improve the systems’ performance. The simulation results confirm the effectiveness of the proposed strategies, demonstrating enhanced stability, robustness, delay tolerance, and energy efficiency in wind energy systems. Full article

This paper investigates the event-triggered dissipative fuzzy tracking control problem of nonlinear networked systems with dynamic quantization and stochastic deception attacks, where the Takagi–Sugeno (T-S) fuzzy system theory is utilized to represent the studied nonlinear networked systems. The event-triggered scheme and the dynamic quantization scheme with general online adjustment rule are employed to significantly decrease the data transmission amount and achieve the rational use of the limited communication and computation resources. A stochastic variable satisfying the Bernoulli random binary distribution is utilized to model the phenomenon of the stochastic deception attacks. The main purpose of this paper is to develop a secure event-triggered quantized tracking control scheme. This scheme guarantees the stochastic stability and prescribed dissipative tracking performance of the closed-loop system under stochastic deception attacks. Moreover, the design conditions for the desired static output feedback tracking controller are formulated in the form of linear matrix inequalities based on the matrix inequality decoupling strategy. Finally, two examples are exploited to illustrate the effectiveness of the developed tracking control scheme. Full article

To improve the transient performance of speed tracking control while ensuring stability and considering actuator constraints in heavy-haul train systems, this paper proposes a novel cruise control method based on a nonparallel distributed compensation (non-PDC) fuzzy-based composite nonlinear feedback (CNF) technique. First, a low-dimensional nonlinear multi-particle error dynamics model is established based on the fencing concept, simplifying the model significantly. To facilitate controller design, a Takagi–Sugeno (T-S) fuzzy model is derived from the nonlinear model. Subsequently, sufficient conditions for the non-PDC fuzzy-based CNF controller are provided in terms of linear matrix inequalities (LMIs), with the controller design addressing asymmetric constraints on control inputs due to differing maximums of traction and braking forces. Simulations based on MATLAB/Simulink are conducted under different maneuvers to validate the effectiveness and superiority of the proposed method. The simulation results demonstrate a notable enhancement in transient performance (over 22.3% improvement in settling time) and steady-state cruise control performance for heavy-haul trains using the proposed strategy. Full article

This paper presents a new method for the simultaneous estimation of system states and unknown inputs in fractional-order Takagi–Sugeno (FO-TS) systems with unmeasurable premise variables (UPVs), by introducing a fractional-order proportional-integral unknown input observer (FO-PIUIO) based on partial state augmentation. This approach permits the estimation of both states and unknown inputs, which are essential for system monitoring and control. Partial state augmentation allows the integration of unknown inputs into a partially augmented model, ensuring accurate estimates of both states and unknown inputs. The state estimation error is formulated as a perturbed system. The convergence conditions for the state estimation errors between the system and the observer are derived using the second Lyapunov method and the $L_2$ approach. Compared to traditional integer-order unknown input observers or fuzzy observers with measurable premise variables, in our method, fractional-order dynamics are combined with partial state augmentation uniquely for the persistent estimation of states along with unknown inputs in unmeasurable premise variable systems. Such a combination allows for robust estimation even under uncertainties in systems and long memory phenomena and is a significant step forward from traditional methods. Finally, a numerical example is provided to illustrate the performance of the proposed observer. Full article

This article investigates the event-based dissipative filtering problem for nonlinear networked systems with dynamic quantization. The nonlinear plant is represented using a discrete-time Takagi–Sugeno (T–S) fuzzy model. The main idea of this article is that a novel dynamic event-triggered mechanism as well as a dynamic quantization strategy combined with a general online adjustment rule are introduced to comprehensively decrease the amount of data involved in network communication and realize the rational utilization of limited communication resources. This article aims to design an event-based quantized filter such that the asymptotic stability and the specified dissipative filtering performance of the filtering error system can be ensured. The design conditions for the desired filter are provided in the form of linear matrix inequalities. Lastly, the effectiveness of the proposed filter design method is demonstrated through the simulation results of a practical example. Full article

This paper provides a comprehensive survey on advanced control techniques for Takagi-Sugeno (T-S) fuzzy systems that are subject to input and output performance constraints. The focus is on addressing practical applications, such as actuator saturation and output limits, which are often encountered in industries like aerospace, automotive, and robotics. The paper discusses key control methods such as model predictive control, anti-windup compensators, and Linear Matrix Inequality (LMI)-based control, emphasizing their effectiveness in handling input and output constraints. These techniques ensure system stability, robustness, and performance even under strict physical limitations. The survey also highlights the importance of T-S fuzzy systems, which provide a flexible framework for modeling and controlling nonlinear systems by breaking them down into simpler linear models. Additionally, recent developments in robust and adaptive control strategies are explored, particularly in handling time delays, disturbances, and uncertainties. These methods are crucial for real-time applications where the system must remain stable and safe despite unmeasured states or external disturbances. By reviewing these advanced techniques, the paper aims to identify research gaps and future directions, particularly in scalable solutions and integrating data-driven approaches with T-S fuzzy control frameworks. Full article

The Hopfield Recurrent Neural Network (HRNN) is a single-point descent metaheuristic that uses a single potential solution to explore the search space of optimization problems, whose constraints and objective function are aggregated into a typical energy function. The initial point is usually randomly initialized, then moved by applying operators, characterizing the discrete dynamics of the HRNN, which modify its position or direction. Like all single-point metaheuristics, HRNN has certain drawbacks, such as being more likely to get stuck in local optima or miss global optima due to the use of a single point to explore the search space. Moreover, it is more sensitive to the initial point and operator, which can influence the quality and diversity of solutions. Moreover, it can have difficulty with dynamic or noisy environments, as it can lose track of the optimal region or be misled by random fluctuations. To overcome these shortcomings, this paper introduces a population-based fuzzy version of the HRNN, namely Gaussian Takagi–Sugeno Hopfield Recurrent Neural Network (G-TS-HRNN). For each neuron, the G-TS-HRNN associates an input fuzzy variable of d values, described by an appropriate Gaussian membership function that covers the universe of discourse. To build an instance of G-TS-HRNN(s) of size s, we generate s n-uplets of fuzzy values that present the premise of the Takagi–Sugeno system. The consequents are the differential equations governing the dynamics of the HRNN obtained by replacing each premise fuzzy value with the mean of different Gaussians. The steady points of all the rule premises are aggregated using the fuzzy center of gravity equation, considering the level of activity of each rule. G-TS-HRNN is used to solve the random optimization method based on the support vector model. Compared with HRNN, G-TS-HRNN performs better on well-known data sets. Full article

In this work, a new nonlinear control method is proposed, which integrates the Takagi–Sugeno (T–S) fuzzy model with the Principal Component Analysis (PCA) technique. The approach uses PCA to reduce the system’s dimensionality, minimizing the number of fuzzy rules required in the T–S fuzzy model. This reduction not only simplifies the system variables but also decreases the computational complexity, resulting in a more efficient control with smooth transient responses and zero steady-state error. To validate the performance of this PCA-based approach for both system identification and control, an interconnected double-tank system was employed. The results demonstrate the method’s capacity to maintain control accuracy while reducing computational load, making it a promising solution for applications in industrial and engineering systems that require robust, efficient control mechanisms. Full article