Search Results (145)

Unmanned helicopters, due to their agility and strong dependence on environmental conditions, require using advanced control techniques in order to ensure precise trajectory tracking in various states of flight. The following paper presents a methodology for the design of an unmanned helicopter flight controller. The proposed solution involves the use of the Model Predictive Control framework enhanced with the Takagi–Sugeno inference algorithm. The designed system uses a Parallel Distributed Compensation architecture and utilizes multiple linear dynamics models to precisely model the helicopter’s response in transitioning from hovering to forward flight. The proposed control system was developed for the ARCHER unmanned rotorcraft, which was designed at Warsaw University of Technology. In order to evaluate control efficiency, simulation tests were conducted using the helicopter mathematical model built in the FLIGHTLAB environment, fully integrated with the Matlab/Simulink platform. The control system test results, including system step responses and performance during flight over a predefined path, highlight the differences between the conventional Model Predictive Control regulator and its fuzzy-enhanced variant. 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

Previous works by our group evidenced stability problems associated with flight control law design for flexible aircraft regarding gain scheduling. This paper proposes an output feedback fuzzy-based gain scheduling approach to adequate closed-loop response in a broader range of the flight envelope. This method applies a variation of the controller gains based on the membership function design for all the varying parameters, such as dynamic pressure. It aims for performance improvement while enforcing global stability gain scheduling. The technique was demonstrated for the flexible ITA X-HALE aircraft nonlinear model and compared to the classical interpolation-based gain scheduling technique. The results revealed that fuzzy-based gain scheduling can effectively handle high-order systems while ensuring global system stability, leading to an overall improvement in performance. 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 study proposes an enhanced Sugeno–Takagi interval type-2 fuzzy logic system (SIT2FLS) to find the best values for two important parameters in Bee Colony Optimization (BCO). The aim of this study was to develop a stable controller for a mobile robot utilizing BCO in the fuzzy controller and to determine the best membership functions (MFs) in a type-1 fuzzy logic system (T1FLS) for control. Another objective was to use an SIT2FLS to find the best $\alpha$ and $\beta$ parameters for BCO to enhance the robot trajectory, which was evaluated through an analysis of the mean squared errors. Three types of perturbations were analyzed and simulated. The performance of the SIT2FLS-FBCO was evaluated and compared to that of the T1FLS-FBCO. In addition, a comparative study was performed to demonstrate that the improved BCO works well when there are disturbances affecting the controller. Finally, it was compared with the Mamdani approach, and an FBCO with an interval type-3 FLS was also developed. Full article

The UAV bicopter is a double-propeller system whose main objective is to stabilize a rod at a given angle by precisely controlling the rotation speed of each propeller. This mechanism generates asymmetric thrust forces that induce a torque on the bar, thus allowing its pitch angle to be modified. Since its dynamics involve complex interactions between the thrust generated by the rotors, aerodynamic effects, and the pendulum behavior of the system, the bicopter is classified as a highly nonlinear system sensitive to external disturbances. To address this complexity, the implementation of a fuzzy Takagi–Sugeno–Kang (TSK) controller is proposed. This controller decomposes the nonlinear dynamics into multiple local linear models associated with a specific operating condition, such as different pitch angles and rotor speeds. The control strategy provides accurate trajectory tracking and effectively handles disturbances and varying conditions, making this approach a practical solution for both dynamic and uncertain environments. This strategy ensures precise trajectory tracking and demonstrates robust performance compared to other control methods, such as PID and LQR, which often struggle with disturbances and system nonlinearities. The TSK controller has proven its effectiveness in experimental trajectory tracking tests, achieving root mean square errors (RMSEs) of 0.2049, 0.3269, 0.3899, 0.3335, and 0.2494, which evaluate the average error in degrees of the system concerning the target position, for tracking trajectories of −10 to 10, −12 to 12, −15 to 15, −17 to 17, and −20 to 20 degrees, respectively. Full article

Background/Objectives: Widely known for its therapeutic benefits, hydrotherapy utilizes water’s physical properties, such as temperature, hydrostatic pressure, and viscosity, to influence physiological responses. Among these, body temperature modulation plays a crucial role in enhancing circulatory function, muscle relaxation, and metabolic processes. While hydrotherapy can improve systemic health, particularly cardiac function, improper temperature control poses risks, especially for vulnerable populations such as the elderly or individuals with thermoregulatory impairments. Therefore, accurately predicting post-bath body temperature is essential for ensuring safety and optimizing therapeutic outcomes. Methods: This study explored the use of fuzzy inference systems to predict post-bath body temperature, leveraging an adaptive neuro-fuzzy inference system, evolutionary fuzzy inference system (EVOFIS), and enhanced Takagi-Sugeno fuzzy system. These models were compared with random forest and support vector machine models using hydrotherapy-related data. Results: The results show that EVOFIS outperformed other models, particularly in predicting deep body temperature, which is clinically significant as it is closely linked to core physiological regulation. Conclusions: The ability to accurately forecast deep-temperature dynamics enables proactive management of hyperthermia risk, supporting safer hydrotherapy practices for at-risk groups. These findings highlight the potential of FIS-based models for non-invasive temperature prediction, contributing to enhanced safety and personalization in hydrotherapy applications. Full article

The increasing complexity of nonlinear multivariable systems poses significant challenges for effective modeling and control. Fuzzy modeling and control typically use fuzzy inference with one-dimensional membership functions. However, the use of multidimensional membership functions can provide significant benefits in optimizing and reducing the computational cost of a fuzzy controller. In this work, we propose the use of fuzzy clustering techniques to adjust and design multidimensional membership functions. These techniques represent a well-developed and comprehensive framework, though they are often disconnected from traditional fuzzy modeling and control methodologies. Thus, this work also seeks to combine fuzzy techniques of different applications with a single ultimate goal, namely, to optimize the modeling and control of nonlinear systems. Our main objective is system identification, modeling, and control using the Takagi–Sugeno method based on one-dimensional and multidimensional membership functions. Moreover, a comparison of various fuzzy clustering techniques for the design of multidimensional membership functions is carried out to demonstrate the effectiveness of the proposed methods in optimizing control performance and reducing computational cost. Full article

The Takagi-Sugeno (T-S) fuzzy control is a nonlinear method that uses a combination of linear controllers as its control law. This method has been applied in various fields of scientific research: buck converters, biomedicine, civil engineering, etc. To the best of the authors’ knowledge, although works on traditional fuzzy control and optical interferometry have already been published, this is the first time that T-S fuzzy (specifically) is applied to demodulate interferometry signals. Through a proof-of-concept experiment, the paper describes the fusion of an open-loop interferometer with an external closed-loop digital observer based on T-S fuzzy (both simple and inexpensive), which actuates like a closed-loop interferometer (but without its drawbacks). The observer design is based on stability conditions using linear matrix inequalities (LMIs) solutions. The system is maintained at the optimal ${90}^{\circ }$ operation point (compensating for environmental drifts) and enables the demodulation of optical phase signals with low modulation index. Simulations and measurements were performed by using a Michelson interferometer, verifying that the method demodulates signals up to $\pi /2$ rad amplitudes and higher than 100 Hz frequencies (with maximum error of 0.45%). When compared to the important arc tangent method, both presented the same frequency response for the test PZT actuator. Full article

This study introduces a novel control framework based on the Takagi–Sugeno–Kang wavelet fuzzy neural network, integrating brain imitated network and cerebellar network. The proposed controller demonstrates high robustness, making it an excellent candidate for handling intricate nonlinear dynamics, effectively mapping input–output relationships and efficiently learning from data. To enhance its performance, the controller’s parameters are fine-tuned using Lyapunov stability theory. Compared to existing approaches, the proposed model exhibits superior learning capabilities and achieves outstanding performance metrics. Furthermore, the study applies this synchronization technique to the secure transmission of medical images. By encrypting a medical image into a chaotic trajectory before transmission, the system ensures data security. On the receiving end, the original image is successfully reconstructed using chaotic trajectory synchronization. Experimental results confirm the effectiveness and reliability of the proposed neural network model, as well as the encryption and decryption process. Specifically, the average_RMSE of the Takagi–Sugeno–Kang fuzzy wavelet brain cerebral controller (TFWBCC) method is 2.004 times smaller than the cerebellar model articulation controller (CMAC) method, 1.923 times smaller than the RCMAC method, 1.8829 times smaller than the TSKCMAC method, and 1.8153 times smaller than the brain emotional learning controller (BELC) method. 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

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