Search Results (199)

This paper focuses on designing an event-triggered dynamic output feedback controller for discrete-time linear systems subject to actuator and sensor constraints as well as external disturbances. A dynamic event-triggered condition with two generalized weighting parameters is introduced to regulate sensor-to-controller communication. By integrating generalized sector conditions, Lyapunov analysis, and linearization techniques, sufficient conditions are derived in terms of linear matrix inequalities, ensuring bounded closed-loop trajectories, prescribed $H_{\infty }$ performance, and asymptotic stability in the disturbance-free case. Furthermore, optimization problems are formulated to maximize the event-triggering rate while preserving the desired system performance. Simulation results show that, compared to time-triggered control, the event-triggered control effectively reduces the communication frequency, thereby significantly conserving communication resources. Compared with existing results, this work presents the first event-triggered dynamic output feedback scheme for discrete-time linear systems with dual saturation constraints. The inclusion of generalized weighting parameters and the use of generalized sector conditions allow the design to be carried out within a flexible local framework with reduced conservatism. Full article

This study develops and validates an Extended Analytical Dynamic Model (EADM) of the UR16e 6-Degree-of-Freedom (DoF) industrial robot, incorporating actuator dynamics and a friction model to address the lack of dynamic information provided by the manufacturer. A two-stage validation methodology is proposed using a Multibody Physical Model (MPM) developed in MATLAB^® R2024b/Simscape Multibody^TM as a reference. In the first stage, the Analytical Dynamic Model (ADM) without actuators or friction is evaluated by comparing its inverse dynamics torque with the torque required by the MPM under identical joint references. In the second stage, the EADM and the MPM are tested under a Proportional-Derivative Computed Torque Control (PD-CTC) scheme using Cartesian trajectories, comparing joint torques and positions. The methodology incorporates torque-level validation, a demanding criterion since torque is determined by the dynamic formulation, whereas position may be influenced by closed-loop control. The results show small torque errors in the first stage ($e_{\tau }$ in the range of ${10}^{-17}$ to ${10}^{-13}$ Nm) and bounded position and torque errors in the second stage ($e_q\le 4\times {10}^{-4}$ rad; $e_{\tau } \le 0.4$ Nm in $q_1$–$q_3$ and $e_{\tau }\le 0.05$ Nm in $q_4$–$q_6$). The methodology provides a systematic validation framework and demonstrates that the EADM accurately matches the MPM’s dynamic behavior. Full article

The authors frame greenhouse operation as a Controlled Environment Agriculture (CEA) challenge involving multiple interdependent targets: air temperature and humidity, CO₂ enrichment, photoperiod-constrained lighting, and irrigation under dynamic and limited energy availability. We propose a knowledge-driven, multi-objective Model Predictive Controller whose cost function integrates expert priorities elicited via an online Analytic Hierarchy Process (AHP) survey; these AHP-derived weights parameterize the controller’s objectives and are solved over two 72 h seasonal episodes, so the MPC can anticipate renewable availability and coordinate HVAC, (de)humidification, CO₂ dosing, LED lighting, and irrigation alongside dispatch from photovoltaic and wind sources, battery storage, and the grid. By embedding the physical interdependence of climate variables directly into the decision layer, the controller schedules energy-intensive actions around renewable peaks and avoids counterproductive actuator conflicts. Seasonal case studies (summer/high solar and winter/low solar) demonstrate robust performance: temperature tracking errors of SMAPE 2.25%/3.05% and CO₂ SMAPE 3.72–3.92%; humidity control with SMAPE 7.04–8.56%; lighting and irrigation following setpoints with low NRMSE (0.08–0.14). Summer energy was 59% renewable; winter was only 13%, increasing grid reliance to 77.5% (peaks: 4.57 kW/6.92 kW for 197.7/181.5 kWh). Under water or energy scarcity, the controller degrades gracefully, protecting high-priority agronomic variables while allowing bounded relaxation on lower-priority targets. This expert-informed, predictive, and resource-aware orchestration offers a scalable route to precision greenhouse control within the food–water–energy nexus. Full article

This paper proposes an adaptive fuzzy fixed-time control (AF-FxT-C) scheme for a piezoelectric-driven microinjector. The inherent hysteresis of the piezoelectric actuator is treated as an unknown nonlinearity. A fuzzy logic system is employed to approximate this hysteresis, along with other lumped disturbances, while an adaptive law is designed to improve approximation accuracy. To address the challenge of inconsistent initial states caused by frequent start-stop operations, a fixed-time control law is developed via a second-order backstepping approach. This guarantees that the upper bound of the system’s settling time is independent of the initial conditions, which is a claim rigorously substantiated by a theoretical stability analysis. The simulation and experimental results validate the effectiveness of the proposed method. The method also maintains robust tracking performance across reference signals of varying frequencies and amplitudes, demonstrating its potential for industrial microinjection applications. Full article

This paper is devoted to the consensus control of a networked system constituted by multiple underactuated marine surface vessels (MSVs) with input saturation. Compared with the related works on this topic, two remarkable features are involved in the system under investigation: (1) Input saturation of each follower MSV is considered in the paper but ignored in most of the related works. (2) More coarse information is allowed about the network since more severer uncertainties (external disturbance joint with unknown system parameters) are involved in each follower MSV, and moreover, the output of the leader MSV is not necessarily second-order continuously differentiable while its time derivatives are not necessarily available for feedback. The above two aspects lead to the incapability of the traditional control schemes on this topic. To solve the control problem, a novel adaptive control scheme is proposed by adaptive dynamic compensation technique combining with certain methods for the handling of saturation input and under-actuation. Specifically, a smooth function is introduced to approximate the saturation input, by which and a couple of state transformations, a new system is obtained with a skillful injection of an auxiliary input for the handling of under-actuation. Then, an iterative procedure is given to derive an adaptive controller which ensures that all the signals of the closed-loop system are bounded while the output of the follower MSVs practically tracks that of the leader. Finally, simulation results are provided to validate the effectiveness of the proposed theoretical results. Full article

Dip coating is a widely used laboratory method for depositing thin films and functional coatings. However, commercial dip-coaters remain costly and often exceed the needs of teaching labs and early-stage research. This paper presents a simple, low-cost automated dip-coater capable of delivering repeatable rise–dwell–fall motion for benchtop applications. The system integrates a 3D-printed PLA structure, a stepper-lead-screw actuator, and a PC-hosted graphical user interface that learns and executes user-specified trajectories without additional hardware controls. A compact mathematical model generates triangular and trapezoidal profiles and maps them to step pulses via the steps-per-millimeter factor. The mechatronic design and sequential control are described, and the prototype is validated through simulations and experiments. Non-contact measurements demonstrate high repeatability, accurate dwell timing, and bounded accelerations with minor deviations at switching instants. The bill of materials is 50 USD (≈1–2% of entry-level commercial systems), underscoring stability, robustness, and accessibility for instructional and resource-constrained settings. These results indicate strong potential for routine laboratory use and a clear path to future enhancements. Full article

In this study, we introduce a novel adaptive fault-tolerant sliding mode control strategy for the finite-time control of symmetric robotic manipulators subjected to uncertainties, disturbances and actuator failures. Firstly, we design a novel type of sliding mode manifold termed Practical Fast Terminal Sliding Mode (P-FTSM). P-FTSM exhibits the capability to accelerate convergence speed while ensuring the finite-time convergence of the system. Subsequently, the P-FTSM is integrated with the super-twisting algorithm (STA) to mitigate the chattering of control input. Additionally, a novel ${\mathcal{K}}_{\infty }$ function is introduced to serve as the gain of the STA. This strategy, which does not require knowledge of the upper bound of the disturbance and fault information, ensures that the gain is tuned according to the disturbance and fault variations, mitigating the adverse effects of high gain and further weakening of the chattering. Simulation results on a two-link symmetric manipulator verify that the proposed method achieves outstanding quantitative performance. The proposed method achieves convergence times of 0.22 and 0.12 s for the joint errors, with root mean square errors (RMSE) of 0.036 and 0.095. The integral absolute errors (IAE) are 0.049 and 0.086, and the total control energy is 943.46. The total variations (TV) of the control signals are $2.86\times {10}^3$ and $1.64\times {10}^3$, indicating effectively suppressed chattering. Overall, the proposed strategy ensures high precision, rapid convergence, and strong fault-tolerant capability. Full article

This research presents a harmonized optimal preview control strategy for a semi-active suspension system (SASS) with a controlled damper varied between the upper and lower bounds of the damping coefficient and an active aerodynamic surface (AAS) control. The preview control algorithm is based on a simplified bilinear 2-DOF quarter-car model to address the tradeoff between passenger ride comfort and road holding capabilities. While the active suspension with the actuator requires a significant amount of energy to provide control force, the semi-active suspension system with a variable damping coefficient mechanism consumes minimal energy to adapt quickly to the real-time operating conditions. Moreover, the dynamic performance of semi-active suspension with the preview controller in conjunction with the active aerodynamic surface is significantly improved. MATLAB^® (R2025b)-based numerical simulations for different road excitations were carried out for the evaluation of the proposed system. Both time-domain and frequency-domain results demonstrate enhanced vehicle dynamic performances in response to road bumps, asphalt road excitations, and harmonic input signals. The simulation performance results indicate that the proposed system extraordinarily reduced the variation in the mean-squared value of the car body vertical acceleration. At the same time, the system enhanced the wheel-road holding metric by decreasing the variation in the gripping force on the ground surface, while maintaining the necessary suspension rattle space constraints within the prescribed limit. Full article

This study proposes an adaptive fault-tolerant control strategy for parametric strict-feedback systems subject to actuator faults and unknown dead-zone nonlinearities, a combination that presents significant challenges for controller design. First, a novel prescribed-performance fault-tolerant control framework is developed by incorporating a funnel function, a barrier Lyapunov function, and a bounded estimation mechanism to address the issue of multiple constrained nonlinear disturbances. Second, the proposed strategy offers two key improvements: (1) adequate compensation for the coupled effects of actuator faults and dead-zone nonlinearities, and (2) guaranteed globally prescribed transient performance, making the settling time and tracking accuracy independent of initial conditions and design parameters. Lastly, simulation results verify the approach’s effectiveness, showing rapid convergence within 0.8 s and a tracking error bounded by $\pm 0.05$, thus surpassing traditional methods. Full article

This paper addresses the cooperative formation control problem for a heterogeneous unmanned system composed of Unmanned Surface Vehicles (USVs) and Autonomous Underwater Vehicles (AUVs) under coexisting constraints of actuator faults, time-varying communication topology, and communication delay. First, a unified dynamic model is established under the Euler–Lagrange framework. Building on this, a novel distributed adaptive fault-tolerant control (DAFTC) framework is proposed. This framework integrates a Dynamic Event-Triggered Mechanism (DETM) to address communication bandwidth limitations, alongside an adaptive fault-tolerant strategy to enhance system robustness. The novelty lies in the cohesive integration of DETM for communication efficiency and adaptive laws for online fault compensation (both loss of effectiveness and bias), while rigorously handling communication delays via Lyapunov–Krasovskii analysis. It is proven via Lyapunov stability analysis that the proposed control protocol ensures all signals in the closed-loop system remain semi-globally uniformly ultimately bounded, with the formation tracking error converging to an adjustable compact set. Simulation results demonstrate the framework’s effectiveness. Compared to periodic communication (0.1 s interval), the proposed DETM reduces the communication load by over 99.6%. Even when subjected to a 25% effectiveness fault and a 5 Nm bias fault, the root-mean-square (RMS) tracking error is maintained below 0.15 m, validating the system’s high performance and robustness. Full article

An adaptive fault-tolerant containment control approach is developed for nonlinear multi-agent systems to address issues related to both communication link and actuator faults. This approach achieves fault-tolerant containment control through the introduction of a convex hull signal estimator and a fault compensation mechanism. First, a leader–follower network model with communication link faults is constructed, and distributed containment errors are established. The proposed framework involves three key components: the design of an adaptive backstepping control law, the introduction of a nonlinear filter for boundary error elimination, and the application of a radial basis function neural network (RBFNN) for the approximation of unknown nonlinear terms. Meanwhile, an adaptive convex hull estimator is designed to estimate the signals formed by the leaders, and an actuator fault estimator is constructed to compensate for fault signals online. Additionally, Lyapunov stability analysis demonstrates that all containment errors remain uniformly bounded. To support simulation teaching and validation, numerical simulations and autonomous underwater vehicle (AUV) simulations are used to not only to confirm the efficacy of the presented control technique but also to provide illustrative cases for educational purposes. Full article

Large-scale production systems (LSPS) operate under growing complexity driven by digital transformation, tighter environmental regulations, and the demand for resilient and resource-efficient operation. Conventional control strategies, particularly PID and isodromic regulators, remain dominant in industrial automation due to their simplicity and robustness; however, their capability to achieve near-optimal performance is limited under constraints on control amplitude, rate, and energy consumption. This study develops an analytical–computational approach for the approximate realization of optimal nonlinear control using standard regulator architectures. The method determines switching moments analytically and incorporates practical feasibility conditions that account for nonlinearities, measurement noise, and actuator limitations. A comprehensive robustness analysis and simulation-based validation were conducted across four representative industrial scenarios—energy, chemical, logistics, and metallurgy. The results show that the proposed control strategy reduces transient duration by up to 20%, decreases overshoot by a factor of three, and lowers transient energy losses by 5–8% compared with baseline configurations, while maintaining bounded-input–bounded-output (BIBO) stability under parameter uncertainty and external disturbances. The framework provides a clear implementation pathway combining analytical tuning with observer-based derivative estimation, ensuring applicability in real industrial environments without requiring complex computational infrastructure. From a broader sustainability perspective, the proposed method contributes to the reliability, energy efficiency, and longevity of industrial systems. By reducing transient energy demand and mechanical wear, it supports sustainable production practices consistent with the following United Nations Sustainable Development Goals—SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production). The presented results confirm both the theoretical soundness and practical feasibility of the approach, while experimental validation on physical setups is identified as a promising direction for future research. Full article

Digitally controlled DC–DC converters are vulnerable to sensor-side spoofing, motivating plant-level anomaly detection that respects the converter physics. We present a physics-informed LSTM (PI–LSTM) autoencoder for a 24→12 V buck converter. The model embeds discrete-time circuit equations as residual penalties and uses a fixed decision rule ($\tau =\mu +3\sigma$, $N=3$ consecutive samples). We study three voltage-sensing attacks (DC bias, fixed-sample delay, and narrowband noise) in MATLAB/Simulink. We then validate the detection path on a TMS320F28379 DSP. The detector attains F1 scores of 96.12%, 91.91%, and 97.50% for bias, delay, and noise (simulation); on hardware, it achieves 2.9–4.2 ms latency with an alarm-wise FPR of ≤1.2%. We also define a unified safety box for DC rail quality and regulation. In simulations, we evaluate a confusion index (CI) policy for safety-bounded performance adjustment. A operating point yields $\mathrm{CI}\approx 0.25$ while remaining within the safety limits. In hardware experiments without CI actuation, the $V_{r,pp}$ and IRR stayed within the limits, whereas the ±2% regulation window was occasionally exceeded under the delay attack (up to ≈2.8%). These results indicate that physics-informed detection is deployable on resource-constrained controllers with millisecond-scale latency and a low alarm-wise FPR, while the full hardware validation of CI-guided deception (safety-bounded performance adjustment) under the complete safety box is left to future work. Full article

Hybrid rule-based and reinforcement-learning (RL) signal control is gaining traction for urban coordination by pairing interpretable cycles, splits, and offsets with adaptive, data-driven updates. However, systematic evidence on their architectures, safeguards, and deployment prerequisites remains scarce, motivating this review that maps current hybrid controller designs under corridor coordination. Searches across major databases and arXiv (2000–2025) followed PRISMA guidance; screening is reported in the flow diagram. Eighteen studies were included, nine with quantitative comparisons, spanning simulation and early field pilots. Designs commonly use rule shields, action masking, and bounded adjustments of offsets or splits; effectiveness is assessed via arrivals on green, Purdue Coordination diagrams, delay, and travel time. Across the 18 studies, the majority reported improvements in arrivals on green, delay, travel time, or related coordination metrics compared to fixed-time or actuated baselines, while only a few showed neutral or mixed effects and very few indicated deterioration. These results indicate that hybrid safeguards are generally associated with positive operational gains, especially under heterogeneous traffic conditions. Evidence specific to Indonesia remains limited; this review addresses that gap and offers guidance transferable to other developing-country contexts with similar sensing, connectivity, and institutional constraints. Practical guidance synthesizes sensing choices and fallbacks, controller interfaces, audit trails, and safety interlocks into a deployment checklist, with a staged roadmap for corridor roll-outs. This paper is not only a systematic review but also develops a practice-oriented framework tailored to Indonesian corridors, ensuring that evidence synthesis and practical recommendations are clearly distinguished. Full article

This paper investigates the attitude control problems of spacecraft subject to external disturbances and compound actuator faults, including both additive and multiplicative components. To address these problems, an improved learning observer (ILO) is proposed. Compared to traditional learning observers (TLOs), the improved learning observer incorporates the previous-step state estimation error as an iterative term. Based on the observer’s outputs, a robust adaptive fault-tolerant attitude control scheme is developed using the backstepping method, under a prescribed performance bound (PPB). This control framework guarantees that the attitude tracking error adheres to prescribed transient performance specifications, such as bounded overshoot and accelerated convergence. Unlike conventional control schemes, the proposed approach ensures that system trajectories remain strictly within the desired bound throughout the transient process. A comprehensive Lyapunov-based analysis rigorously demonstrates the global uniform ultimate boundedness of all closed-loop signals. Numerical simulations substantiate the efficacy of the proposed approach, highlighting the enhanced disturbance estimation capability of the ILO in comparison to the TLO, as well as the superior transient tracking performance of the PPB-based control strategy relative to existing methods. Full article