Search Results (231)

Autonomous agricultural robots require accurate trajectory tracking to perform precision field operations such as seeding, fertilization, and pruning. Classical kinematic models fail to capture the nonlinear dynamics inherent to real field conditions, limiting the performance of model-based control strategies. This work proposes an optimal hybrid methodology integrating Takagi–Sugeno (T–S) fuzzy model identification and Pure Pursuit (PP) control within a Particle Swarm Optimization (PSO) framework for a simulated pruning tractor. Data-driven T–S fuzzy models for incremental displacements ${\mathcal{M}}_{\Delta x}$ and ${\mathcal{M}}_{\Delta y}$ are identified using Fuzzy C-Means and parameterized via PSO. These fuzzy models are embedded in a PP feedback control scheme with discrete-time PI velocity and PD steering controllers, whose four gains are tuned by a second PSO instance. The fuzzy models achieve identification Root-Mean-Square Errors (RMSEs) of 10.598 × 10⁻³ m and 8.125 × 10⁻³ m. Integrated into the control loop, the system yields a lateral RMSE of 6.6 × 10⁻³ m on the training path and generalizes effectively across twelve complex agricultural coverage trajectories, maintaining a lateral RMSE below 12 × 10⁻³ m and heading RMSE under 1 degree. This interpretable, fuzzy rule-based approach provides an accurate and replicable simulation baseline for future experimental implementation on physical platforms. Full article

Speed controllers for electric micromobility (EMM) drives are increasingly developed with Model-Based Design and deployed as automatically generated code, yet the cost that a given control law actually imposes on the target, and the mechanism by which competing laws differ once deployed, are seldom reported. This paper addresses both questions on an EMM-class test bench built around a 36 V, 250 W in-wheel BLDC motor. A proportional-integral (PI) regulator and a first-order Takagi-Sugeno (TS) fuzzy regulator are specified in Simulink, auto-coded to ANSI-C by Embedded Coder, and deployed unchanged on an STM32F446RE target driving a custom three-phase inverter through six-step Hall commutation. Over a six-step, 180 s duty cycle reaching 21.1 km/h, the two regulators are shown to occupy opposite ends of the speed-versus-damping trade-off. On the 30 to 100 RPM ascending step under load, the PI reaches the set-point in $0.4\pm 0.1$ s with $21.6\%$ overshoot and the TS in $2.7\pm 0.1$ s with $1.5\%$ overshoot, both quoted at the resolution of the 10 Hz acquisition, and over the complete duty cycle, a window that also contains segments on which neither regulator has control authority, the TS lowers the tracking RMSE by 9.4%. A structural analysis of the deployed firmware excludes the realisation form as the cause. The positional and incremental forms are algebraically equivalent while the command is unsaturated, which is the regime of the step above. Under saturation, the incremental accumulator of the TS is not clamped and winds up exactly as the positional PI integrator does. The two loops are also shown to share the same unfiltered speed feedback and the same command saturation limits. The difference is traced instead to the effective gains realised by the seven consequents. Far from the set-point, the TS applies an integral gain three to twelve times weaker than the PI for a comparable proportional gain. A fixed-gain PI in that range is predicted to reproduce the response for one eighth of the Flash. The embedded cost of both regulators is then quantified on the target from the linker map, the fuzzy controller occupying 2325 Bytes of Flash against 266 Bytes for the PI, a factor of 8.7, and 200 Bytes of stack against 32 Bytes, a factor of 6.3, rising to 248 Bytes against 32 Bytes when the complete call tree is counted, for 0.45% of the available Flash. The complete platform, comprising the inverter, the Hall front end, the auto-generated firmware, and a Python supervisory interface, is described together with its deployed timing, PWM, and saturation parameters. Full article

This study addresses an integrated security control and communication co-design problem for nonlinear CPSs subject to coexisting FDI attacks and actuator faults. A novel adaptive discrete event-triggered communication scheme (ADETCS) is proposed. Its triggering threshold adapts to the system state. State estimation, fault estimation, and attack detection are all migrated to the control unit. Based on this framework, a closed-loop T–S fuzzy model is established for active defense against actuator faults and dual-end FDI attacks. A robust augmented observer is then developed via Lyapunov stability theory to jointly estimate system states, actuator faults, and FDI attacks. Sufficient conditions are further derived for an integrated security controller that unifies attack tolerance and fault tolerance. Simulation results on a quadruple-tank system show that the proposed method effectively counteracts coexisting attacks and faults while significantly reducing resource consumption. Over an 800-s horizon, data transmissions drop to 712 (8.9% transmission rate). The sensor-node computational load is also reduced from 8000 time-triggered executions to 712 event-triggered ones. Full article

High-precision X–Y positioning systems driven by stepper motors are widely used in industrial automation, manufacturing, and scientific instrumentation. However, fixed feedforward–feedback controllers may degrade when operating conditions vary, particularly as step frequency changes and the risk of synchronism loss increases. This work proposes an adaptive fuzzy feedforward–feedback controller for stepper-motor-driven X–Y positioning systems. The controller uses a Takagi–Sugeno (T–S) fuzzy inference system to adjust the proportional, derivative, and feedforward actions according to the tracking error, step frequency, and an auxiliary error-based adaptation variable. The control law is integrated with the inverse kinematics of the platform to generate synchronized step-domain commands, and a practical synchronism-preservation condition is established. Experimental validation on a NEMA 17-based X–Y platform showed accurate trajectory tracking, with a steady-state error of approximately $1.6\left[\mathsf{\mu }\mathrm{m}\right]$ for a trapezoidal profile. For a multi-segment trajectory, the RMSE was $0.0749\left[\mathrm{mm}\right]$ without load and $0.0760\left[\mathrm{mm}\right]$ under a $7.5\left[\mathrm{kg}\right]$ external load. Compared with a conventional PID controller, the proposed method reduced the RMSE from $0.1741\left[\mathrm{mm}\right]$ to $0.0749\left[\mathrm{mm}\right]$, while preserving motor synchronism. Full article

Existing approaches for admissibility analysis of T-S fuzzy descriptor time delay systems fail to balance conservatism reduction and computational complexity. This paper proposes a low-conservatism analysis and stabilization method based on the symmetric Lyapunov–Krasovskii (L-K) functional. By exploiting the boundedness of membership function derivatives, and combining Jensen’s integral inequality with auxiliary slack matrices to achieve tight bounding of nonlinear terms, we derive an admissibility criterion for open-loop systems with significantly reduced conservatism. A well-suited L-K functional is constructed targeting the structural characteristics of fuzzy singular matrices $E_{\xi }$, a state feedback controller is designed via the parallel distributed compensation (PDC) strategy, and solvable sufficient conditions for the admissibility of closed-loop systems are established. Numerical examples demonstrate that the maximum allowable delay upper bound obtained by the proposed method outperforms that of existing state-of-the-art approaches while balancing conservatism and computation cost and verifying the superiority of the proposed method. Full article

We carried out a study on adjusting the voltage at the output terminals of a three-phase synchronous generator using the voltage encoder EMBSIN 121u. The purpose of this study was to increase the quantity and quality of the electrical energy produced by the generator. This paper is innovative as the author generates three models in MATLAB-Simulink to study voltage adjustment in a three-phase synchronous generator with electromagnetic excitation in two distinct cases: case 1, running the three-phase synchronous generator with a variable load and constant frequency, and case 2, running this generator with a constant load and variable frequency. In the first case, the voltage is adjusted through an automatic voltage adjustment system equipped with a proportional integrative (PI) controller (model 1) or through a fuzzy logic (FL) controller (model 2). The voltage is adjusted in the second case through an automatic voltage adjustment system equipped with a PI controller (model 3). In the case of the automatic voltage adjustment system with a fuzzy logic controller, the electrical energy supplied by the three-phase synchronous generator will be higher than in the case of the automatic voltage adjustment system equipped with a PI controller (at the moment, t = 6 s: $S_{gen\_PI}=158.2$ (VA) and $S_{gen\_FL}=230.7$ (VA)). Moreover, to implement the adjustment algorithm of the three-phase synchronous generator voltage through the voltage encoder EMBSIN 121u, the author has created a program in the programming environment Arduino IDE. The results of this study could also be used for three-phase synchronous generators with electromagnetic excitation used to construct wind power stations. Full article

This paper investigates uncertainty-resilient stabilization of an inverted pendulum on a cart (IPOC) using an interval type-2 Takagi–Sugeno (IT2 T–S) fuzzy model and an LQR-based control framework. The IPOC dynamics are represented as a weighted combination of local linear subsystems, where interval firing strengths derived from upper and lower membership functions capture modeling uncertainties. An LQR state-feedback controller is designed for each subsystem, and the final control input is obtained by blending the local controllers according to the normalized firing strengths. To analyze stability, an LMI-based verification condition is established as a sufficient condition for the subsystem LQR controllers. Simulation results show that this condition is satisfied only in a limited operating region, while the closed-loop system can still remain stable even when the condition is violated, demonstrating the reduced conservatism and flexibility of the proposed approach. Furthermore, comparisons with the conventional PDC structure confirm that the proposed method provides greater design flexibility and enables a trade-off between robustness and transient-state performance. Full article

The Takagi–Sugeno (T-S) fuzzy system is extensively applied in system identification and intelligent control due to its strong nonlinear approximation capability and model interpretability. However, traditional zero-order T-S systems encounter three critical limitations: slow convergence and susceptibility to local optima caused by random initialization, overfitting risks stemming from structural redundancy, and gradient oscillations during rule pruning when using traditional non-smooth regularizers (e.g., $L_{1/2}$). To overcome these challenges, this study proposes a novel gradient learning algorithm that integrates Fuzzy C-Means (FCM) clustering initialization with a smoothing Group Lasso regularization strategy. First, FCM data-drivenly initializes Gaussian membership centers and determines the rule quantity, optimizing the alignment between the initial network structure and underlying data distribution to accelerate training and reduce local optima traps. Second, a piecewise smoothing function is designed to approximate the Group Lasso penalty, facilitating efficient rule reduction through group sparsity constraints while completely resolving gradient oscillation issues arising from nondifferentiability. The global convergence of the proposed algorithm is rigorously established using Lagrange’s mean value theorem, Taylor expansion, and the differential mean value theorem. Comprehensive numerical experiments on nonlinear regression and classification benchmarks demonstrate substantial improvements in convergence rate, computational efficiency, and structural sparsity. Ultimately, this research delivers a theoretically sound and practically efficient framework for T-S fuzzy system optimization, significantly broadening the applicability of fuzzy neural networks in complex engineering scenarios. Full article

Adaptive cruise control (ACC) is a key component of advanced driver assistance systems, as it maintains a safe distance from preceding vehicles by regulating speed and spacing. However, vehicle dynamics, measurement uncertainty, and traffic variability pose significant challenges for conventional control methods. In this context, fuzzy logic (FL) has been widely explored for its ability to handle uncertainty and incorporate expert knowledge via linguistic rules. This article presents a systematic literature review on the application of FL in ACC systems, proposing a functional taxonomy based on the role of the fuzzy system within the control architecture. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, 103 initial records were identified, of which 87 studies were included in the final analysis. Four main categories are defined: Direct Fuzzy Control/Learning-Based, Fuzzy Supervisory Decision Control, Fuzzy Adaptive Robust Control, and Fuzzy Model-Based Control. Results indicate that Direct Fuzzy Control/Learning-Based and Fuzzy Supervisory Decision Control dominate the literature, accounting for 35.6% and 28%, respectively, while Fuzzy Adaptive Robust Control and Fuzzy Model-Based Control represent 20.7% and 14.9%. Mamdani-type systems predominate (78.16%), followed by Takagi-Sugeno (T–S) systems (17.24%), while type-2 fuzzy systems remain limited (4.60%) due to higher computational complexity. Recent trends highlight growing interest in adaptive and robust FL-based strategies. Full article

This paper investigates the reliable stabilization and $L_2-L_{\infty }$ performance control problem for discrete-time descriptor systems described by Takagi–Sugeno (T-S) fuzzy models under stochastic data dropouts and actuator faults. In view of the practical situation that system states are usually unmeasurable, a novel observer-based proportional–derivative (PD) control strategy is proposed. Different from traditional state feedback, the PD structure effectively alleviates the inherent structural constraints of descriptor systems and relaxes the conditions for system regularity and causality. By constructing a parameter-dependent Lyapunov functional and using the Schur complement lemma, sufficient conditions are derived in the form of linear matrix inequalities (LMIs) to guarantee the stochastic stability of the closed-loop system and the prescribed $L_2-L_{\infty }$ performance. The effectiveness and superiority of the proposed methodology are verified through extensive numerical simulations on two practical case studies, namely, a bio-economic system and a DC motor system. In the case of actuator faults and data dropouts the observer achieves accurate state tracking, and the peak value of the system output is strictly constrained. The research results confirm that the method has strong robustness against data dropouts and actuator faults. Full article

This paper investigates the transient stability characteristics of a DC-coupled off-grid photovoltaic hydrogen production system. A nonlinear state-space model of the system is established by integrating the photovoltaic generation unit, the energy storage unit, and the electrolyzer unit. To enhance system dynamic performance, a virtual DC machine (VDCM) control strategy is introduced for the energy storage converter. Based on the nonlinear system model, a Takagi–Sugeno (TS) fuzzy model is constructed to approximate the system dynamics, and the largest estimated domain of attraction (LEDA) is derived using Lyapunov stability theory. Simulation studies are conducted to evaluate system stability under sudden photovoltaic power fluctuations caused by environmental disturbances, and the obtained LEDA is compared with the simulated attraction domain and the power boundary derived from the Lyapunov eigenvalue method. The results show that the LEDA obtained from the TS fuzzy model can effectively estimate the stability boundary of the system, although it remains slightly conservative. Furthermore, the impacts of VDCM control parameters and electrolyzer operating states on system stability are analyzed. Simulation results demonstrate that appropriate adjustment of system parameters can enlarge the LEDA and significantly improve the transient stability of the off-grid photovoltaic hydrogen production system. Full article

Vulnerabilities in conventional agri-food supply chains (CAFSCs) necessitate a shift toward resilient, localized production models. Within the Agri-Food 4.0 landscape, urban Controlled-Environment Hydroponic Agriculture (CEHA) systems address these challenges by shortening supply chains and mitigating climate-induced breakdowns. However, structurally aligning Triple Bottom Line (TBL)-oriented stakeholder needs with complex technical specifications remains a critical challenge in sustainable CEHA system design. To address this challenge, the present study proposes a novel framework integrating the Full Consistency Method (FUCOM) and Quality Function Deployment (QFD) within a Finite Interval Type-2 (FIT2) Gaussian fuzzy environment. This approach systematically translates TBL-oriented priorities into precise engineering specifications, mapping 17 stakeholder needs (SNs) to 30 technical design requirements (TDRs) while capturing linguistic uncertainty and hesitation. The findings reveal a clear strategic focus on environmental and social sustainability. Specifically, high product quality, food safety and traceability, consumer acceptance, and minimization of environmental impacts emerge as the primary drivers of CEHA adoption. The QFD translation identifies scalable IoT infrastructure, sensor maintenance and calibration, and AI-enabled decision support as the most critical TDRs. The framework’s reliability and structural robustness were rigorously validated through comprehensive analyses, including Kendall’s W test to confirm expert consensus, alongside a Leave-One-Out (LOO) approach, weight perturbations, and a structural evaluation of TDR intercorrelations. These findings provide a scientifically grounded roadmap for designing sustainable, intelligent urban agricultural systems. Ultimately, this framework offers actionable managerial implications for agribusiness stakeholders to bridge strategic TBL-oriented goals with practical engineering, significantly enhancing agri-food supply chain resilience. Full article

This research focuses on the trajectory tracking control of a Twin Rotor MIMO System (TRMS) with time-varying sinusoidal inputs. Initial design considerations include a backstepping controller integrated with a high-gain observer (HGO) to estimate unmeasured states. While the outcomes of the simulation show good accuracy of tracking, real-time implementation shows instability and performance degradation. This divergence is attributed to the static high gains of the observer that amplify measurement noise and inject inaccurate state estimates into the controller during actual deployment. To overcome this drawback without altering the core control structure, we propose a strategy of online gain tuning based on Interval Type-2 Takagi–Sugeno (TS) fuzzy logic. The proposed mechanism dynamically adjusts the observer gain based on estimation errors to balance the trade-off between convergence speed and noise sensitivity. Experimental evaluations on the physical TRMS confirm that the fuzzy-tuned observer eliminates instability in real-time. Quantitative analysis demonstrates that the proposed method reduces the Root Mean Square Error (RMSE) by 65.6% in the Pitch axis and 92.3% in the Yaw axis compared to the fixed-gain counterpart. Full article

Thermal regulation using Peltier cells presents challenges due to high inertia, memory effects, and energy constraints in embedded systems. This paper introduces the FOPID with Adaptive Supervisor (FOPID-AS) scheme, combining a PSO-optimized fractional-order controller (FOPID) with a deterministic rule-based gain-scheduling supervisor. Experimental validation compares four strategies: PID, Fuzzy-PID, static FOPID, and the proposed FOPID-AS. During the transient phase ($t<105$ s), FOPID-AS reaches the ±0.5 °C tolerance band in 31.20 s, with an ITAE of 6612.97 and transient energy consumption of 0.18 Wh, outperforming PID, Fuzzy-PID, and FOPID in speed and tracking quality. In steady state ($t\ge 105\mathrm{s}$), FOPID-AS exhibits steady-state error $e_{ss}$ = 0.08 °C, ${\sigma }_{ss}$ = 0.10 °C, and peak-to-peak ripple of 0.67 °C, with steady-state energy consumption of 0.30 Wh, showing lower dispersion than PID and comparable values to the other fractional controllers, while maintaining low computational load suitable for real-time applications. Full article

This paper investigates dynamic event-triggered control (DETC) and encoding–decoding schemes to achieve the synchronization of T-S fuzzy neural networks (FNNs). DETC allows the transmission signals to be controlled aperiodically during the actual operation of the system, enabling a rapid response to practical control tasks. Meanwhile, during the event-triggered control process, an encoding–decoding scheme with externally injected noise is used to protect the signals. First, a dynamic event-triggered control mechanism is established, and an encoding–decoding scheme is used to optimize the transmission of controller signals. Subsequently, the Lyapunov–Krasovskii functional is constructed to derive the system’s synchronization criteria and calculate the controller gains. Finally, numerical simulation experiments are conducted to verify the effectiveness and feasibility of the proposed method. Full article