Growing demand for warehouse automation requires Unmanned Aerial Vehicles (UAVs), particularly quadcopters, to operate autonomously with a high level of precision and reliability. However, indoor localization poses unique challenges due to the absence of Global Positioning System (GPS) signals, making alternative sensors and robust control strategies essential. This study proposes an indoor UAV navigation system that integrates camera and LiDAR sensors with Fuzzy–Sliding Mode Control (Fuzzy-SMC) to enhance stability and reduce the chattering effects commonly associated with Sliding Mode Control. In the proposed method, the camera provides better accuracy for real-time position tracking compared to LiDAR, while fuzzy logic adaptively adjusts the Sliding Mode Control parameters, which serve as the main controller for stabilizing the quadcopter’s nonlinear dynamics. Research methodology includes mathematical modeling of the UAV quadcopter, the design of the Fuzzy-SMC controller, and simulation-based testing for trajectory tracking in indoor environments. Results show that the developed system achieves high accuracy, with error values ranging from 0 to 4.044%, remaining below the acceptable threshold of 5%. These findings demonstrate that integration of a camera with Fuzzy-SMC provides an effective and reliable solution for indoor quadcopter UAV navigation, while future research will focus on optimizing the fuzzy rule base and conducting hardware validation in real warehouse scenarios.

Fuzzy sliding mode control strategy for synchronous reluctance motor

Malignant melanoma fractional-order mathematical model with stabilized fuzzy sliding mode control.

An advanced Energy Management System (EMS) is developed for a standalone hybrid microgrid byintegrating photovoltaic units (PVUs), andPermanent Magnet Synchronous Generator (PMSG) based Wind Turbine Systems (WTS), with battery storage. The PV unit employs a Hybrid Whale Optimization with Perturb and Observe (HWOPO) algorithm, which improves MPPT performance by extracting 8–10% more power than PSO and 4–6% more than Grey Wolf Optimization (GWO) under similarpartial shading, with a swift convergence time of 0.17–0.22 s. The two-mass WTS model combined with Sliding Mode Control (SMC) toensure accurate torque tracking across wind speed variations (41 N · m → 19 N · m → 30 N · m), outperforms conventional single-mass models. Inverter synchronization at the Point of Common Coupling (PCC) is managed by 3rd Order Degree of Freedom-Proportional-Integral-Derivative (3DOF-PID) controllers. Under unbalanced and nonlinear loading, the proposed controller maintains voltage symmetry and limits Total Harmonic Distortion (THD) to 3.84%. It is significantly better than backstepping (4.52%) andcan be comparable to fuzzy SMC (3.10%) with reduced tuning complexity. ByValidating OPAL-RT simulations, it is clear thatthe EMS delivers energy withhigh efficiency, improvespower quality,and system stability. Thus, this research supports Sustainable Development Goals (SDGs) 7 (Affordable and clean Energy), 9 (Industry, Innovation and Infrastructure), 12 (Responsible Consumption and Production), and 13 (Climate Action) through enhanced renewable energy adoption and reduced environmental impact.

Abstract This study introduces two novel strategies for regulating the chaotic dynamics of the Belousov–Zhabotinsky (BZ) reaction: a smoothed sliding mode controller (SMC‐Proposed), designed to reduce chattering while preserving robustness, and an adaptive fuzzy sliding mode controller (SMC‐Fuzzy), applied to the BZ system for the first time. These approaches are compared against a classical sign‐based sliding mode controller (SMC‐sign) in terms of tracking accuracy, convergence speed, and chattering suppression. Simulation results show that while SMC‐sign achieves the lowest tracking error (RMSE = 0.00001), it produces severe chattering (973.4 Hz). In contrast, the SMC‐Fuzzy controller reduces chattering to 79.2 Hz, with good accuracy (RMSE = 0.00107) and faster stabilization. The SMC‐Proposed model offers a balanced trade‐off, achieving moderate accuracy while significantly reducing high‐frequency energy without relying on fuzzy logic. Frequency‐domain analysis using power spectral density (PSD) confirms the chattering suppression capability of both proposed methods. These findings highlight the practical advantages of the SMC‐Fuzzy and smoothed SMC controllers for robust and efficient control of chaotic chemical systems.

In practice, robots operate as nonlinear systems and often encounter factors like nonlinear friction, load variations, and external disturbances during tasks. To address these challenges, a smart control approach has been developed that combines the strengths of fuzzy logic and sliding mode control (SMC) for precise robot manipulator positioning. The key benefit of SMC lies in its robustness, maintaining stability despite noise or parameter changes in the system. However, designing an SMC system often faces difficulties due to practical limitations, making deployment not always feasible in real-world applications. Additionally, a large control law amplitude can lead to chattering around the sliding surface. To overcome these issues, the study introduces a fuzzy logic-based method to adaptively estimate the control law's magnitude, guided by Lyapunov stability principles. This control scheme is tested on a four-degree-of-freedom robot manipulator, with simulation results confirming its effectiveness in MATLAB.

Optimal fractional fuzzy sliding-mode control for fractional-order fuzzy systems based on actor–critic reinforcement learning scheme

The two-degree-of-freedom aero flight control simulator is a nonlinear, unstable, and multi-input multi-output system with gravitational disturbance in its pitch dynamics. Its attitude control is a challenging task with linear controllers. The fuzzy controller by parallel distributed compensation uses a combination of linear controllers. It is a simple method, but exhibits poor tracking performance under disturbance. This study presents a design of a fixed structure fuzzy sliding mode controller to track the desired trajectory for this system. A sliding mode controller is combined with the fuzzy controller using an integral sliding surface to overcome gravitational disturbance and track the attitude. Lyapunov’s method verifies the stability of the closed-loop system. To validate the proposed design, numerical simulations are carried out and compared with existing methods. The tracking responses of yaw and pitch point out fast convergence of error with minimum settling time in the presence of matched disturbances.

<div>Internal combustion engine torque control presents a persistent challenge due to pronounced nonlinearities, parametric uncertainties, and time-varying dynamics. While conventional controllers like the proportional–integral derivative (PID) are widely implemented, they often struggle to deliver high-performance results under transient conditions. To address this gap, this work introduces and experimentally validates a novel torque controller with fuzzy sliding-mode controller (FSMC) architecture, a hybrid control not previously applied to the domain of engine torque regulation. The proposed FSMC is specifically engineered to systematically mitigate the effects of system nonlinearities by integrating the robustness of sliding-mode theory with the adaptive, chattering-suppression capabilities of fuzzy logic. This study details the controller’s development, implementation, and rigorous experimental validation on an ethanol-fueled engine via a dynamometer test bench. The controller’s performance was benchmarked against a standard PID controller, demonstrating the FSMC’s capacity for high-fidelity reference tracking, achieving mean rise and fall times up to 1.4 s and a mean absolute error not exceeding 0.2 Nm. These results signify a substantial advance in control performance and engine safety, filling the identified gap in the literature and underscoring the potential of the proposed fuzzy sliding-mode strategy as an effective and robust solution for advanced torque control in internal combustion engines.</div>

Glaucoma, a progressive neurodegenerative ocular disease, is primarily driven by elevated intraocular pressure (IOP), which results in optic nerve damage and irreversible vision loss. This study introduces a novel fractional-order mathematical model to capture the intricate dynamics of aqueous humor production, drainage, and the associated deterioration of the optic nerve in glaucoma. Building on this framework, this work proposes a Quantum-Inspired Neural Fuzzy Sliding Mode Control (QINF-SMC) framework, designed to address the nonlinear and time-varying nature of IOP regulation. The model highlights that persistent elevation in IOP leads to continuous optic nerve damage and disease progression, while impaired outflow resistance exacerbates glaucoma. Conversely, stable aqueous humor dynamics maintain normal IOP, preventing disease advancement. The proposed QINF-SMC framework integrates fractional-order calculus, fuzzy logic, and quantum-inspired optimization to achieve precise and adaptive control of IOP, mitigate optic nerve damage, and optimize aqueous humor dynamics. The framework achieves near-perfect 97.9% convergence, with excellent control stability and tightly regulated parameters, combining fast global optimization with precise refinement through advanced fractional-order dynamics. This approach offers a robust and innovative strategy for managing glaucoma, with potential implications for improving therapeutic outcomes and preserving vision.

ABSTRACTThis article presents a robust control technique for a Mobile Manipulator (MM), consisting of a robotic manipulator mounted on a mobile robot capable of operating in diverse environments such as land, air, space, or underwater. By leveraging the platform's mobility, the workspace of the manipulator is significantly expanded, allowing for optimal placement and enhanced task execution. To simultaneously control the end‐effector motion and platform velocity, a two‐step control approach is proposed. First, kinematic velocity control generates desired trajectories for the system. Second, a fuzzy sliding mode torque controller, integrated with backstepping, ensures the end‐effector position and platform velocity converge to these trajectories. The control parameters are optimized using Particle Swarm Optimization (PSO), with stability guaranteed through Lyapunov theory. Simulation results in MATLAB/SIMULINK demonstrate that the Optimized Backstepping Fuzzy Sliding Mode Control (OBFSMC) outperforms the Backstepping Sliding Mode Control (BSMC) in tracking accuracy, achieving a 31.6% performance improvement. The proposed controller effectively mitigates external disturbances and tolerates parametric uncertainties, confirming its robustness and efficiency in trajectory tracking under challenging conditions.

Adaptive fuzzy sliding mode control for improving the efficiency of filtration systems

Adaptive sliding mode controller based on improved Sparrow search algorithm for tracking control of human lower limb exoskeleton.

This study investigates an asynchronous sliding-mode control (SMC) strategy tailored for interval type-2 (IT2) fuzzy switching systems, specifically addressing challenges posed by cyber-attacks. Distinct from existing stochastic switching strategies, a novel duration-time-based switching rule is proposed that integrates both sojourn probability and mode duration, significantly reducing computational complexity and aligning more closely with practical requirements. To mitigate mode-switching-induced chattering and enhance robustness against uncertainties and disturbances, an innovative fuzzy SMC law with a learning mechanism is developed. Notably, a recursive sliding-mode learning controller is introduced, replacing abrupt switching actions with iterative learning adjustments to progressively guide system states onto the sliding surface, thereby significantly improving control smoothness and reducing chattering. To effectively handle cyber-attacks disrupting mode transmission, a comprehensive mismatched model that dynamically synchronizes the modes of the system and the controller is introduced, offering improved resilience compared to traditional fixed mismatch approaches. Utilizing the proposed learning-based SMC and Lyapunov stability theory, sufficient conditions ensuring mean-square stability of the system are derived. Finally, the practical effectiveness and distinct superiority of the proposed methods are demonstrated through simulations using mass-spring-damper and tunnel diode circuit models.

This article aims to develop a fuzzy adaptive SMC technique that incorporates a variable boundary layer and adaptive switching gain for speed control of a three-phase induction motor (IM) drive. The boundary-layer characteristics are chosen to offer the best compromise between control robustness and chattering elimination or reduction. In addition, this technique offers many advantages for uncertain dynamic systems. The chattering phenomenon, a well-known problem in classical SMC, arises when the switching function attracts the state trajectory toward the sliding surface. Several methods and approaches have been tried to eliminate this phenomenon. We propose here an approach that involves fuzzy logic to avoid this undesirable effect so that the stability condition according to Lyapunov is verified. This fuzzy adaptation system associated with the SMC (FASMC) towards the obtained results forms a robust tool for chattering reduction. Finally, an experimental prototype setup tests the robustness of the fully developed control structure. The experimental results obtained from the FASMC for speed IM drives demonstrate the highest effectiveness and robustness when compared to the conventional SMC controller.

Precise trajectory tracking is essential for achieving autonomous locomotion of a tracked robot, necessitating an advanced controller to manage system nonlinearity, terrain uncertainty, and track slippage. To address these challenges, we proposed an improved dynamic model-based fuzzy sliding mode control (FSMC) approach. In this approach, initially, a controlled input velocity vector was created to achieve an asymptotically stable tracking error. Then, a sliding mode control with a fuzzy logic-based switching law was developed to ensure the robot's real velocity converged to the controlled velocity input. The more robust and chatter-free responses were observed due to the integration of the fuzzy switching law and control gain optimisation using a Genetic Algorithm. Comparative analysis shows that FSMC outperforms proportional-integral-derivative (PID) control, traditional sliding mode control (SMC), and non-singular integral-based SMC (NISMC). Finally, the proposed control system was experimentally validated through real-time experiments with a tracked robot under various terrain conditions.

Fuzzy sliding mode control for trajectory tracking of quadrotor aircraft based on adaptive event-triggered mechanism

Abstract The dependence of photovoltaic system performance on variable weather conditions influences their reliability and efficiency. In order to contribute to solving these problems, a hybrid algorithm combining the basic perturb and observe (P&O) technique and Integral Backstepping Controllers (IBSC) for the control of the boost converter and the single-phase inverter has been proposed and validated using the Matlab/Simulink platform. The proposed control strategies give a good extraction of the photovoltaic Maximum Power Point (MPP) with a DC-DC conversion efficiency of 99.19% and 99.97% for non-linear and linear loads respectively at the solar irradiation of 900 W m−2. The sine wave of the inverter output voltage with a fixed reference has minimized a tracking error to 0 V, while its THD is limited to 0.05% and 0.16% for linear and nonlinear loads, respectively. A comparison of the simulation results with standard Backstepping control, sliding mode control, and hybrid fuzzy-sliding mode control exhibits the effectiveness, superiority, and satisfactory performance of the proposed control schemes in minimizing harmonics under variable irradiance conditions regardless of the load type.

This work introduces a control approach for a mobile manipulator unmanned aerial vehicle (MMUAV) that is affected by numerous actuator defects, parametric uncertainties and external disturbances. The technique is based on a disturbance observer and adaptive type-2 fuzzy sliding mode control strategy. The suggested control method is unique among unmanned aerial vehicle (UAV) control strategies in that it can concurrently adjust for actuator defects, parametric uncertainties and external disturbances. The proposed control strategy utilizes adaptive control parameters in both continuous and discontinuous control components. This allows for the generation of suitable control signals to handle actuator faults and parametric uncertainties without entirely depending on the robust discontinuous control strategy of sliding mode control. Next, in order to preserve the tiny value of the discontinuous control gain of sliding mode control, a nonlinear disturbance observer is built and incorporated to reduce the impact of external disturbances. Furthermore, the stability study using Lyapunov’s theory of the suggested control strategy demonstrates that it can maintain system tracking performance and minimize tracking errors in the given circumstances. Comparative simulation results of MMUAV under various failing and uncertain conditions show the efficiency of the suggested control approach.

The application of photovoltaic (PV) system in different sectors increases dramatically since it is clean, sustainable, and easy to maintain. However, PV systems have a nonlinear voltage-current characteristic, with a distinct maximum power point (MPP), which depends on environmental factors like temperature and irradiation. Maximum power point tracking (MPPT) is crucial for PV power systems to consistently extract the maximum power from solar panels as it optimizes power output under varying conditions. In this paper, a standalone PV-powered water pumping system is designed for Bahir Dar University Health Science College. Then fuzzy sliding mode control (FSMC) is designed for MPPT. The proposed controller is simulated in MATLAB/ SIMULINK and the controller's performance for optimizing the system's power output under different environmental and load conditions is evaluated. The effectiveness of the proposed MPPT algorithm is validated by comparing its performance with fuzzy logic control (FLC) and sliding mode control (SMC). Based on the simulation result FSMC has an MPPT efficiency of 99.13% compared with 80.21% in FLC and 97.81% in SMC.