Search Results (29)

This paper focused on the motion control of an underwater vehicle installed on a linear guide system, which is driven by two electric winches with wire ropes. The vehicle is subject to complex nonlinear time-varying disturbances and actuator input saturation effects during motion. A coupled dynamic model, incorporating an underwater vehicle, winches, and wire ropes, was established. Particular attention was paid to the nonlinear time-varying hydrodynamic disturbances acting on the underwater vehicle. The Kelvin–Voigt model was introduced to characterize the nonlinear dynamic behavior of the wire ropes, enabling the model to capture the dynamic response characteristics of traction forces. To tackle cross-coupling within the towing system, a differential tension coordination control method was proposed that simultaneously regulates system tension during motion control. For the vehicle dynamics model, a nonsingular fast-terminal sliding-mode (NFTSM) controller was designed to achieve high-precision position tracking control. An auxiliary dynamic compensator was incorporated to mitigate the impact of actuator input saturation. To handle time-varying disturbances, a fuzzy adaptive nonlinear disturbance observer (FANDO) is developed to perform feedforward compensation. Stability proof of the proposed algorithms was provided. Extensive numerical simulations demonstrate the effectiveness of the control strategies. Compared to the NFTSM without the disturbance observer the absolute mean value of the tracking error decreased by 76%, the absolute maximum value of the tracking error decreased by 67%, and the mean square error decreased by 93.5%. Full article

In this study, we propose an innovative inner–outer loop control framework for a quadcopter unmanned aerial vehicle (UAV) that significantly enhances the trajectory-tracking speed and accuracy while enhancing robustness against external disturbances. The inner loop employs a Linear Active Disturbance Rejection Controller (LADRC) and the outer loop a proportion integral differential (PID) controller, unified within a fuzzy control scheme. We introduce a Self-Adaptive Bald Eagle Search Optimization algorithm to optimize the initial controller settings, thereby accelerating convergence and improving parameter-tuning precision. Simulation results show that our proposed controller outperforms the conventional two-loop cascade PID configuration, as well as alternative strategies combining an outer-loop PID with a second-order inner-loop LADRC or a fuzzy-enhanced PID-LADRC approach. Moreover, the system maintains the desired position and attitude under external perturbations, underscoring its superior disturbance-rejection capability and stability. Full article

Uncertainty quantification in cloud models requires simultaneous characterization of fuzziness (via Entropy, En) and randomness (via Hyper-entropy, He), yet existing similarity measures often neglect the stochastic dispersion governed by He. To address this gap, we propose HECM-Plus, an algorithm integrating Expectation (Ex), En, and He to holistically model geometric and probabilistic uncertainties in cloud models. By deriving He-adjusted standard deviations through reverse cloud transformations, HECM-Plus reformulates the Hellinger distance to resolve conflicts in multi-expert evaluations where subjective ambiguity and stochastic randomness coexist. Experimental validation demonstrates three key advances: (1) Fuzziness–Randomness discrimination: HECM-Plus achieves balanced conceptual differentiation (δC₁/C₄ = 1.76, δC₂ = 1.66, δC₃ = 1.58) with linear complexity outperforming PDCM and HCCM by 10.3% and 17.2% in differentiation scores while resolving He-induced biases in HECM/ECM (C₁–C₄ similarity: 0.94 vs. 0.99) critical for stochastic dispersion modeling; (2) Robustness in time-series classification: It reduces the mean error by 6.8% (0.190 vs. 0.204, *p* < 0.05) with lower standard deviation (0.035 vs. 0.047) on UCI datasets, validating noise immunity; (3) Design evaluation application: By reclassifying controversial cases (e.g., reclassified from a “good” design (80.3/100 average) to “moderate” via cloud model using HECM-Plus), it resolves multi-expert disagreements in scoring systems. The main contribution of this work is the proposal of HECM-Plus, which resolves the limitation of HECM in neglecting He, thereby further enhancing the precision of normal cloud similarity measurements. The algorithm provides a practical tool for uncertainty-aware decision-making in multi-expert systems, particularly in multi-criteria design evaluation under conflicting standards. Future work will extend to dynamic expert weight adaptation and higher-order cloud interactions. Full article

This paper aims to present a hybrid steering control method combining the self-guidance capability of a wheelset and fuzzy logic controller (FLC), which were applied to our new micro-autonomous railway inspection vehicle, enhancing the vehicle’s stability. The vehicle features intelligent inspection systems and a suspension system with variable damping capability that uses smart magnetorheological fluid to control vertical oscillations. A mathematical model of the steering dynamic system was developed based on the vehicle’s unique structure. Two simulation models of the vehicle were built on Simpack and Simulink to evaluate the lateral dynamic capability of the wheelset, applying Hertzian normal theory and Kalker’s linear theory. The hybrid steering control was designed to adjust the torque differential of the two front-wheel drive motors of the vehicle to keep the vehicle centered on the track during operation. The control simulation results show that this hybrid control system has better performance than an uncontrolled vehicle, effectively keeps the car on the track centerline with deviation below 10% under working conditions, and takes advantage of the natural self-guiding force of the wheelset. In conclusion, the proposed hybrid steering system controller demonstrates stable and efficient operation and meets the working requirements of intelligent track inspection systems installed on vehicles. Full article

The rapid development of renewable energy presents challenges to the security and stability of power systems. Aiming at addressing the power system scheduling problem with load demand and wind power uncertainty, this paper proposes the establishment of different error fuzzy sets based on the Wasserstein probability distance to describe the uncertainties of load and wind power separately. Based on these Wasserstein fuzzy sets, a distributed robust chance-constrained scheduling model was established. In addition, the scheduling model was transformed into a linear programming problem through affine transformation and CVaR approximation. The simplex method and an improved differential evolution algorithm were used to solve the model. Finally, the model and algorithm proposed in this paper were applied to model and solve the economic scheduling problem for the IEEE 6-node system with a wind farm. The results show that the proposed method has better optimization performance than the traditional method. Full article

A variable pressure differential fuzzy control method is proposed based on the online identification method for key parameters and the fuzzy subset inference fuzzy control method of the chilled water system network model. Firstly, a phase plane fuzzy identification method is proposed for the most unfavorable thermal loop. The study focuses on analyzing the trend of room temperature deviation and deviation change in different quadrants in the phase plane. Furthermore, we establish a chilled water pipe network model that recalculates flow variation in both the main pipe and each branch pipe section to eliminate the most unfavorable thermal loop. Finally, the test platform for the fan coil variable flow air conditioning water system was designed and constructed to meet the requirements of energy-saving regulation. Additionally, the network monitoring system for the test platform was completed. The calibration and debugging results demonstrate that the monitoring error is within ±5.0%, ensuring precise control of room temperature at the end of the branch within ±0.5 °C. Results demonstrate that our novel method exhibits superior stability in room temperature control compared to traditional linear variable pressure differential set point controls while achieving energy saving ranging from 4.7% to 6.5%. Full article

This paper presents a mathematical modeling approach utilizing a fuzzy modeling framework for fixed-wing aircraft systems with the goal of creating a highly desirable mathematical representation for model-based control design applications. The starting point is a mathematical model comprising fifteen non-linear ordinary differential equations representing the dynamic and kinematic behavior applicable to a wide range of fixed-wing aircraft systems. Here, the proposed mathematical modeling framework is applied to the AIRBUS A310 model developed by ONERA. The proposed fuzzy modeling framework takes advantage of sector non-linearity red techniques to recast all the non-linear terms from the original model to a set of combined fuzzy rules. The result of this fuzzification is a more suitable mathematical description from the control system design point of view. Therefore, the combination of this fuzzy model and the wide range of control techniques available in the literature for such kind of models, like parallel and non-parallel distributed compensation control laws using linear matrix inequality optimization, enables the development of control algorithms that guarantee stability conditions for a wide range of operations points, avoiding the classical gain scheduling schemes, where the stability issues can be extremely challenging. Full article

In this work, we present the design of an observer for Takagi–Sugeno fuzzy systems with unmeasurable premise variables. Moving away from Lipschitz-based and ${\mathcal{L}}_2$ attenuation-based methods—which fall short in eliminating the mismatching terms in the estimation error dynamics—we leverage the differential mean value theorem. This approach not only removes these terms but also streamlines the factorization of the estimation error dynamics, making it directly proportional to the estimation error. To ensure the asymptotic convergence of the estimation error, we apply the second Lyapunov theorem, which provides sufficient stability conditions described as linear matrix inequalities. A numerical example applied on a three-tank hydraulic system is presented to demonstrate the observer’s effectiveness. Full article

This paper investigates the Static Output (SO) control issue of the disturbed nonlinear stochastic system, which achieves passivity. Through the application of fuzzy sets and the stochastic differential equation, a Takagi–Sugeno (T-S) fuzzy model with the terms of multiplicative noise and external disturbance can be constructed to describe the considered systems. Furthermore, the Parallel Distributed Compensation (PDC) concept is used to design a fuzzy controller exhibiting an SO feedback scheme structure. To attenuate the effect of external disturbance, the PDC-based SO fuzzy controller is designed to exhibit passivity. During the derivation of some sufficient conditions, a line-integral Lyapunov function is utilized to avoid the conservative term produced using the derivative membership function. Using converting technologies, a stability criterion belonging to Linear Matrix Inequality (LMI) forms is proposed such that the derived conditions are convex hull problems and are solved through an optimization algorithm. Then, the proposed criterion is used to discuss the problem of SO controller design of ship fin stabilizing systems with added disturbance and noise. Full article

Currently, there is uncertainty in the modeling techniques of cyber-physical systems (CPS) when faced with the multiple possibilities and distributions of complex system behavior. This uncertainty leads to the system’s inability to handle uncertain data correctly, resulting in lower reliability of the system model. Additionally, existing technologies struggle to verify the activity and safety of CPS after modeling, lacking a dynamic verification and analysis approach for uncertain CPS properties.This paper introduces a generalized possibility decision process as a system model. Firstly, the syntax and semantics of generalized possibility temporal logic with decision processes are defined. Uncertain CPS is extended by modeling it based on time-based differential equations and uncertainty hybrid time automaton. After that, model checking is performed on the properties of activity and safety using fuzzy linear time properties. Finally, a cold–hot hybrid constant-temperature system model is used for simulation experiments. By combining theory and experiments, this paper provides a new approach to the verification of uncertain CPS, effectively addressing the state explosion problem. It plays a crucial role in the design of uncertain CPS and offers a key solution for model checking in the presence of uncertainty. Full article

This manuscript contains several new notions including intuitionistic fuzzy $N_b$ metric space, intuitionistic fuzzy quasi-$S_b$-metric space, intuitionistic fuzzy pseudo-$S_b$-metric space, intuitionistic fuzzy quasi-$N$-metric space and intuitionistic fuzzy pseudo $N_b$ fuzzy metric space. We prove decomposition theorem and fixed-point results in the setting of intuitionistic fuzzy pseudo $N_b$ fuzzy metric space. Further, we provide several non-trivial examples to show the validity of introduced notions and results. At the end, we solve an integral equation, system of linear equations and nonlinear fractional differential equations as applications. Full article

This paper presents a study on the modeling and control of an aero-engine within the full flight envelope using the Takagi–Sugeno (T-S) fuzzy theory. A highly accurate aero-engine small deviation state variable model (SVM) was developed using the adaptive differential evolution, based on numerous successes through history, with the linear population size reduction (L-SHADE) algorithm. The affinity propagation (AP) clustering algorithm was then implemented to realize the division of flight envelopes based on the gap metric between the SVMs at each working point. By solving the membership parameters using the L-SHADE algorithm, the T-S fuzzy model was obtained, which has flight conditions as premises and engine linear SVM as consequences. Furthermore, based on the established T-S fuzzy model and T-S control theory, a controller design method is proposed. The simulation results show that the T-S fuzzy model has high accuracy and good generalization capability within the flight envelope, and the proposed control method can guarantee the asymptotic stability of the system, subject to external disturbance and time delay. Full article

This paper deals with the stabilization problem of a nonlinear system described by a Takagi–Sugeno fuzzy (TSF) model with unmeasurable premise variables via a robust controller. Applying the sector nonlinearity techniques, the nonlinear system is represented by a decoupled fuzzy model. Then, we design a robust observer-based controller for the obtained fuzzy system by utilizing the differential mean value approach. The observer and controller gains are obtained by the separation principle, in which the problem is solved in the sum of linear matrix inequalities (LMIs). The paper presents two main contributions: A state feedback controller is designed using differential mean value (DMVT) which ensures robust stabilization of the nonlinear system. Additionally, the Luenberger observer is extended to the Takagi–Sugeno fuzzy models. The second contribution is to reduce conservatism in the obtained conditions, a non-quadratic Lyapunov function (known as the line integral Lyapunov fuzzy candidate (LILF)) is employed. Two examples are provided to further illustrate the efficiency and robustness of the proposed approach; specifically, the Takagi–Sugeno fuzzy descriptor of an induction motor is derived and a robust observer-based controller applied to the original nonlinear system. Full article

Very well-drained lands could have a positive impact in various soil health indicators such as soil erosion and soil texture. A drainage system is responsible for properly aerated soil. Until today, in order to design a drainage system, a big challenge remained to find the subsurface drain spacing because many of the soil and hydraulic parameters present significant uncertainties. This fact also creates uncertainties to the overall physical problem solution, which, if not included in the preliminary design studies and calculations, could have bad consequences for the cultivated lands and soils. Finding the drain spacing requires the knowledge of the unsteady groundwater movement, which is described by the linear Boussinesq equation (Glover-Dumm equation). In this paper, the Adomian solution to the second order unsteady linear fuzzy partial differential one-dimensional Boussinesq equation is presented. The physical problem concerns unsteady drain spacing in a semi-infinite unconfined aquifer. The boundary conditions, with an initially horizontal water table, are considered fuzzy and the overall problem is translated to a system of crisp boundary value problems. Consequently, the crisp problem is solved using an Adomian decomposition method (ADM) and useful practical results are presented. In addition, by application of the possibility theory, the fuzzy results are translated into a crisp space, enabling the decision maker to make correct decisions about both the drain spacing and the future soil health management practices, with a reliable degree of confidence. Full article

Recently, the concept of green building has become popular, and various renewable energy systems have been integrated into green buildings. In particular, the application range of fuel cells (FCs) has become widespread due to the various government plans regarding green hydrogen energy systems. In particular, proton exchange membrane fuel cells (PEMFCs) have proven superiority over other existing FCs. However, the uniqueness of the operating maximum power point (MPP) of PEMFCs represents a critical issue for the PEMFC control systems. The perturb and observe, incremental conductance/resistance, and fuzzy logic control (FLC) represent the most used MPP tracking (MPPT) algorithms for PEMFC systems, among which the FLC-based MPPT methods have shown improved performance compared to the other methods. Therefore, this paper presents a modified FLC-based MPPT method for PEMFC systems in green building applications. The proposed method employs the rate of change of the power with current ($dP/dI$) instead of the previously used rate of change of power with voltage ($dP/dV$) in the literature. The employment of $dP/dI$ in the proposed method enables the fast-tracking of the operating MPP with low transient oscillations and mitigated steady-state fluctuations. Additionally, the design process of the proposed controller is optimized using the enhanced version of the success-history-based adaptive differential evolution (SHADE) algorithm with linear population size reduction, known as the LSHADE algorithm. The design optimization of the proposed method is advantageous for increasing the adaptiveness, robustness, and tracking of the MPP in all the operating scenarios. Moreover, the proposed MPPT controller can be generalized to other renewable energy and/or FCs applications. The proposed method is implemented using C-code with the PEMFC model and tested in various operating cases. The obtained results show the superiority and effectiveness of the proposed controller compared to the classical proportional-integral (PI) based $dP/dI$-based MPPT controller and the classical FLC-based MPPT controller. Moreover, the proposed controller achieves reduced output waveforms ripple, fast and accurate MPPT operation, and simple and low-cost implementation. Full article