Search Results (1,375)

In this article, the motion control of ferromagnetic particles through varying a non-invasive magnetic field is addressed. Within an experimental test bench, three experiments are proposed to verify motion control, which consist of control of the distance between electromagnets, retention of particles over the flow, and manipulation of the direction of particle flow at a “Y”-type bifurcation emulating an “OR” gate. At each experimental stage, instrumented test benches were integrated with current, distance, and flow sensors, enabling measurement and feedback of the system’s physical variables. These benches were configured using pulse-width-modulation (PWM) and Proportional–Integral–Derivative (PID) controllers to regulate the current supplied to the electromagnets and, thereby, control the intensity of the induced electromagnetic field according to the requirements of each experiment. Different study cases were defined to analyze the operational limits of the system by varying the current influencing the electromagnetic field and the configuration of the electromagnets. The results describe the response of the magnetic field, the induced force, and the behavior of the suspended particles under each condition, providing elements to characterize the performance of the electromagnetic system in operational scenarios and contributing to the understanding of the phenomena associated with the non-invasive manipulation of ferromagnetic particles by means of controlled magnetic fields. Full article

The increasing need for energy-efficient and environmentally friendly electricity generation has led to the extensive use of hybrid electric systems. These systems integrate different energy sources in an effort to take advantage of the positives of each technology, as using a single source of energy comes with many limitations and disadvantages; hence, the popularity of hybrids has increased in recent times. In this regard, this paper proposes a lithium-ion battery (LIB) and ultracapacitor (UC)-based hybrid architecture considering an optimal energy management framework. In the transportation sector, hybrid vehicles (LIB and UC-based vehicles) effectively utilize the high energy density and power density of LIBs and UCs. This LIB and UC-based hybrid architecture provides an efficient power management solution considering the high power density of the LIB for smooth road profiles, and the high power density of the UC is driven during sudden spikes in load demand because the LIB will not function optimally during the sudden spikes due to lower power density. Furthermore, in order to achieve efficient utilization of the proposed hybrid system, an optimal energy management framework is used. In this regard, in this study, a fractional-order proportional–integral–derivative (FOPID) controller has been designed for effective and optimal energy management. Furthermore, the designed FOPID has been optimized using a metaheuristic technique, namely particle swarm optimization (PSO), to enhance LIB and UC-based hybrid electric vehicle energy management performance. Employing dynamic and optimal energy flow control, the FOPID-based system improves energy consumption, extends LIB life, and improves overall system performance and reliability. Full article

To address the strong nonlinearity, coupling, and time-delay characteristics in greenhouse environmental regulation, as well as the large overshoot and limited robustness of conventional proportional–integral–derivative (PID) control, while considering the practical constraint that complex intelligent control methods are difficult to deploy directly on low-cost industrial controllers, this study proposes a predictive fuzzy PID control method for greenhouse environments under programmable logic controller (PLC)-based edge deployment. An integrated remote monitoring and control system with a “PLC–human–machine interface (HMI)–cloud–mobile” architecture was also developed. Based on the intelligent greenhouse experimental platform of Yunnan Agricultural University, the proposed method was validated for greenhouse temperature and air humidity regulation through MATLAB simulations, PLC deployment, and on-site operation tests. The results showed that all four control strategies were able to effectively track the setpoints of greenhouse temperature and humidity, while predictive PID and predictive fuzzy PID achieved better overall performance than conventional PID and fuzzy PID. Predictive fuzzy PID performed best in the humidity channel, whereas its performance in the temperature channel was close to that of predictive PID but with more stable disturbance recovery and better overall balance. On-site operation results further showed that, under typical operating conditions, the tracking error of the actual greenhouse temperature relative to the target temperature could be maintained within approximately ±1 °C, while the error of the actual air humidity relative to the target humidity remained within approximately −2% to 3% RH. These results verify the engineering feasibility of the proposed method on resource-constrained industrial PLC platforms. The proposed method can provide a useful reference for the lightweight and intelligent upgrading of small- and medium-sized greenhouse environmental control systems. Full article

Permanent magnet synchronous motor (PMSM)-based servo actuators are fundamental to high-performance electromechanical systems. However, in energy-sensitive aerospace applications, the impact of tracking error on system-level efficiency remains insufficiently quantified. This paper establishes an energy-oriented analytical framework linking PMSM tracking accuracy to vehicle-level energy consumption and flight range. By employing a specific mechanical energy formulation, we demonstrate that tracking deviations modify aerodynamic drag and introduce additional dissipative work. Specifically, the accumulated dissipation is shown to admit a lower bound proportional to the integral of the squared tracking error, from which a range degradation bound is derived. These results reveal that “tracking-error energy” imposes a fundamental limit on achievable flight distance. A Lyapunov-based analysis further proves that minimizing this error energy reduces total aerodynamic dissipation without requiring modifications to propulsion scheduling or guidance laws. Numerical simulations comparing a conventional sliding mode controller with an advanced fuzzy-adaptive nonsingular terminal sliding mode controller confirm that enhanced servo precision directly improves velocity retention and range performance. This framework offers practical insights for designing energy-aware PMSM control strategies in energy-constrained aerospace platforms. Full article

Accurate temperature regulation is essential for ensuring product quality, operational safety, and energy efficiency in industrial electric furnace systems. However, the inherent thermal inertia, time-delay effects, and nonlinear dynamics of furnace processes often make precise temperature control a challenging task. Motivated by these challenges, this study proposes an optimization-based control framework aimed at improving the temperature regulation performance of electric furnace systems. The proposed approach integrates a proportional–integral–derivative (PID) controller with the recently developed gazelle optimization algorithm (GOA) for automatic tuning of the controller parameters. First, a mathematical model of the electric furnace is established to describe the dynamic relationship between the control input and the furnace temperature output. Based on this model, a PID controller is implemented to regulate the furnace temperature. The parameters of the PID controller are then optimized using GOA, a nature-inspired metaheuristic algorithm that mimics the adaptive predator–prey survival strategies observed in gazelle herds. In order to achieve a balanced improvement in both steady-state and transient performance, a composite objective function is introduced. The proposed performance index combines the integral of absolute error with additional transient performance indicators related to maximum overshoot and settling time. The effectiveness of the proposed GOA-based tuning framework is evaluated through extensive simulation studies and statistical analyses conducted over multiple independent optimization runs. The results demonstrate stable convergence behavior, with the optimization process achieving a minimum objective value of 2.4251, a maximum value of 2.5347, and an average value of 2.4674 across 25 runs. The optimized control system exhibits improved dynamic characteristics, including a rise time of 1.8509 s, a settling time of 3.6834 s, and a low overshoot of 1.5104%. To further assess its effectiveness, the proposed GOA–PID control strategy is compared with several widely used controller tuning methods reported in the literature, including genetic algorithm, Ziegler–Nichols, Cohen–Coon, Nelder–Mead, and direct synthesis approaches. Comparative results indicate that the proposed method achieves a superior balance between response speed, stability, and temperature tracking accuracy. Full article

Addressing challenges such as the non-Newtonian fluid characteristics of melt, significant system hysteresis, and rheological thermal drift in large-scale glass fiber-reinforced polypropylene (PP-GF) screw-extrusion additive manufacturing (SEAM), this paper proposes a composite pressure control strategy based on inverse model feedforward and variable-structure feedback (VSFC-Smith). This strategy establishes a dynamic pressure benchmark through an inverse rheological model, utilizes a Smith predictor to compensate for time delay, and introduces dead-zone variable-structure feedback to smoothly suppress thermal drift. Experimental results demonstrate that, compared to traditional PID (Proportional-Integral-Derivative) controller, the VSFC-Smith strategy reduces the step pressure overshoot from 23.37% to 17.37%, decreases steady-state screw speed fluctuation by approximately 50%, and limits the error within ±0.04 MPa during complex trajectory tracking. In practical molding validation, this strategy effectively suppressed surface ripples, reducing the surface roughness (Sa) by 14.5% to 124.41 μm; simultaneously, the Z-directional interlayer tensile strength reached 12.63 MPa (a 22.5% improvement compared to open-loop control). This study successfully overcomes the limitations of traditional high-gain feedback, achieving synergistic optimization of the macroscopic morphology and microscopic mechanical properties of composite parts. Full article

Maintaining optimal control of heating boiler systems using intelligent control strategies remains a significant challenge due to strong nonlinearities, time delays, and unpredictable variations in fuel quality and thermal load. Conventional fuzzy logic controllers, while effective under nominal conditions, often exhibit limited robustness when exposed to abrupt parameter changes. To address this limitation, this study proposes a novel Quantum-Fuzzy Adaptive Intelligent Proportional-Integral-Derivative (QFAI-PID) control architecture, in which probabilistic inference mechanisms inspired by quantum principles are implemented algorithmically within a classical computing framework and validated through MATLAB/Simulink simulations. The proposed approach enhances the adaptability of fuzzy rule-based control by enabling probabilistic superposition and dynamic activation of control rules, allowing the knowledge base to self-organize in real time. The control system is evaluated using a nonlinear heating boiler model developed in MATLAB/Simulink under realistic industrial disturbances, including ±25% fuel flow variations, up to 30% changes in thermal demand, and measurement delays of 5–8 s. Simulation results demonstrate that the proposed controller achieves up to 36% improvement in control stability, 30% faster response time, and 22% reduction in energy-related control effort compared with conventional fuzzy control systems. These results confirm that the proposed quantum-inspired fuzzy approach provides a robust, energy-efficient, and practically implementable solution for intelligent control of nonlinear thermal energy systems. Full article

Airborne electromagnetic detection systems are highly susceptible to low-frequency motion-induced noise, which significantly degrades the extraction of weak geological signals. Conventional signal processing methods alone are often insufficient to suppress mechanically induced vibration noise, resulting in signal distortion and reduced detection reliability. To address this limitation, this study proposes an active noise suppression strategy that integrates mechanical vibration isolation with advanced signal processing. A pneumatic vibration isolation platform based on a cable-driven parallel robot (CDPR) architecture is developed to achieve precise orientation correction and effective vibration isolation. The system employs kinematic modeling and a servo-controlled pneumatic cylinder driven by a proportional directional valve to enable accurate dynamic regulation. Numerical simulations conducted in the Advanced Modeling and Simulation Environment (AMESim), combined with proportional–integral–derivative (PID) control, demonstrate that piston displacement overshoot is constrained within 0.2 mm. Furthermore, targeted filtering techniques are applied to enhance signal quality. Experimental results show that the response time for continuous step input is 0.18–0.2 s, with a steady-state error below 0.3 mm, confirming robust control performance. The proposed framework provides an effective low-noise solution for airborne electromagnetic detection and can improve survey reliability in deep resource exploration. Full article

Continuous impact vibrations caused by uneven road surfaces (such as speed bumps) can significantly reduce the trajectory tracking accuracy of mobile manipulator. This study proposes for the first time an integrated framework combining a semi-active magnetorheological elastomer (MRE) intelligent isolation system with an active trajectory tracking controller to improve the operational accuracy of mobile manipulator under continuous impact excitation, and numerically evaluates the effect of the MRE isolation system. The working principle and design method of the MRE isolation system for mobile manipulators are described, and a multi-layer MRE isolator is fabricated and experimentally characterized. A semi-active control strategy is developed to adaptively adjust the stiffness and damping of the isolator based on continuous impact input. To further compensate for residual disturbances transmitted through the isolator, an enhanced computational torque control (CTC) and proportional-derivative (PD) controller with predefined-time disturbance observer (DOB) is designed for the mobile manipulator. This ensures that the disturbance estimate converges within a predefined time window, thereby improving the robustness of the closed-loop system. By constructing a comprehensive multibody dynamics model coupling the vehicle, the MRE isolator, and the manipulator, vibration transmission is analyzed and trajectory tracking performance is evaluated. Simulation results under continuous road impact excitation demonstrate that the proposed semi-active MRE intelligent isolation system can significantly suppress base vibration and greatly improve the trajectory tracking accuracy of the mobile manipulator end-effector and its joints. This study proves the feasibility of the semi-active MRE isolation system in the trajectory tracking application of mobile manipulator and provides a new approach for the collaborative design of intelligent vibration isolation and control strategies for mobile robot systems operating in harsh and frequently impacted environments. Full article

Proportional–integral–derivative (PID) controllers are always a preferred choice of control strategy in industrial and biomedical systems due to their simplicity, reliability, and easy implementation. However, the systematic tuning of PID parameters for nonlinear, constrained, and safety-critical systems remains challenging, particularly in the presence of disturbances and actuator limitations. This paper presents a unified surrogate-based optimization framework for tuning PID controllers for linear and nonlinear dynamical systems. The tuning problem is formulated as a constrained optimization task, where performance objectives and safety requirements are explicitly incorporated into the cost function. A surrogate-based optimization via clustering (SBOC) approachis employed to efficiently explore the PID parameter space while reducing the number of expensive closedloop simulations. The proposed framework is first applied to the first- and second-order linear time-invariant systems to check its feasibility and then to the nonlinear systems to demonstrate its robustness under nonlinearity and saturation. The approach is further applied to safety-critical systems considering the case of glucose regulation in type 1 diabetes under realistic meal disturbances and insulin delivery constraints. The simulation results show that the surrogate-optimized PID controller achieves stable regulation with improved tracking performance while strictly satisfying safety requirements, including control effort penalties to limit actuator wear and the avoidance of hypoglycemia and hyperglycemia in glucose regulation problems. Full article

Tea garden irrigation suffers from time delays, nonlinear interference, and phenological biomass fluctuations caused by plucking, leading to the failure of traditional Proportional–Integral–Derivative (PID) and fixed-threshold models in precise water supply. This study proposes a precision irrigation system for smart tea gardens integrating Phenology-Aware Collaborative Decision-Making and an Adaptive Gain Predictive Super-Twisting Sliding Mode Control (AG-PSTC) algorithm. A “temperature–time–water” phenological reference model was constructed, and Crop Water Stress Index (CWSI) was introduced to decouple shoot density changes into phenology-driven and water stress components, realizing dynamic target soil moisture (W_target) setting. The AG-PSTC algorithm combined an improved Smith predictor for phase compensation and a barrier function-based adaptive super-twisting term for chattering elimination and finite-time convergence. Simulations showed AG-PSTC reduced rise time by 78% and steady-state error by four orders of magnitude compared with PID, with robust performance under ±40% time-delay perturbation. Field tests confirmed the system suppressed false irrigation during plucking, with soil moisture standard deviation within 1.51%. This study provides a vertical integration framework from crop physiological models to precision control, promoting the transition of tea garden irrigation from experience-based to demand-based. Full article

This work presents the design of an attitude control experiment for onboard OPS-SAT-1 satellite execution, conceived with inherent extensibility to future mission architectures. OPS-SATs are ESA nanosatellite mission series designed as an in-orbit testbed for validating novel software and control techniques under real space conditions, OPS-SAT-1 being the first mission. Equipped with an advanced payload computer, OPS-SAT-1 enabled experimentation with innovative mission operations, including real-time attitude control strategies. Two attitude control algorithms, a modified Proportional–Integral–Derivative (mPID) and a fuzzy logic controller, were designed and implemented for the OPS-SAT-1. The design methodology applied to these controllers consisted of (i) modelling the space environment and satellite characteristics, (ii) assessing actuator feasibility, (iii) determining the operational ranges for attitude error and angular velocity, (iv) parametrizing controllers within these ranges, (v) fine-tuning controllers using multi-objective genetic optimization, and (vi) robustness analysis using the Monte Carlo method. Despite the technical issues related to communication with the OPS-SAT-1 hardware, which prevented the execution of the experiment in orbit, this work presents the simulation results that were obtained. These results indicate that fuzzy logic controllers may outperform PID controllers in terms of the accumulated error, settling time and steady-state error, whereas power efficiency appears to be less robust than in the PID. This suggest that a large uncertainty in the model could lead the PID to become more efficient. Near the nominal scenario, the fuzzy controller achieves superior error–cost trade-offs, enabling precise attitude stabilization with lower energy consumption. These findings suggest the potential advantages of modern control approaches compared to classical methods, which will be further assessed through future in-orbit experiments. Full article

This article addresses the challenges of regulation and trajectory tracking in a nonlinear, multivariable drone quadrotor system using a discrete-time Generalized Proportional Integral (GPI) controller, which is the discrete-time version of its continuous-time counterpart. The discrete-time formulation offers several advantages, including simplified trajectory planning by eliminating time derivatives, reduced computational demands, and lower complexity in nominal feed-forward input functions. The proposed GPI controller ensures asymptotic exponential stability for both attitude and position, enabling effective trajectory tracking. Its effectiveness has been validated through numerical simulations, which demonstrate excellent stabilization and tracking performance even in the presence of atmospheric disturbances and measurement noise. Full article

Recent space missions demand higher pointing accuracy, smoother attitude transitions and lower energy consumption than those typically achievable with conventional control approaches. This motivates the exploration of intelligent and nonlinear control methods. The FuzzyBuzz experiment investigates the application of fuzzy logic for spacecraft attitude control using NASA’s Astrobee robotic system aboard the International Space Station. Unlike traditional control methods, fuzzy logic introduces a rule-based approach capable of handling uncertainties and nonlinearities inherent in space environments, making it particularly suited for autonomous operations in microgravity. The objective of FuzzyBuzz is to evaluate the effectiveness of fuzzy controllers compared to traditional linear ones, such as Proportional–Integral–Derivative (PID) and $H_{\infty }$ controllers. In addition, a comparison with a nonlinear controller based on a Model Predictive Control (MPC) strategy is considered. The controllers will be tested through predefined attitude maneuvers, evaluating precision, energy efficiency, and real-time adaptability. This work presents the design of the FuzzyBuzz experiment, including the software architecture, simulation environment, experiment protocol, and the development of a fuzzy logic-based attitude control system for Astrobee robots. The proposed fuzzy controller and a PID controller are optimized using a Multi-Objective Particle Swarm Optimization (MOPSO) method, providing a range of operational points with different trade-offs between two metrics, related to convergence time and energy consumption. Results show that the PID controller is better suited for scenarios demanding low convergence times, whereas the fuzzy controller provides smoother responses, reduced steady-state error, and maintains convergence under significant parametric uncertainties. Results from $H_{\infty }$ and MPC controllers will be reported once the in-orbit experiment is performed. Full article

Optical coherence tomography (OCT) is a non-invasive, high-resolution imaging technique widely used in medical diagnosis, biomedical research and other fields. It plays an important role in the early detection and accurate diagnosis of diseases. The superluminescent light-emitting diode (SLED) is the ideal light source for OCT systems, where the stability of its drive current and operating temperature directly determines the imaging quality of OCT. Existing driving and temperature control schemes for similar light sources predominantly rely on microcontrollers or field programmable gate arrays (FPGAs), a reliance which often results in complex system architectures and difficulties in balancing simplicity with control precision. To address these issues, a stable and compact SLED source driver module designed for OCT was developed in this study, integrating both a constant-current drive circuit and a temperature control circuit. The negative feedback control and improved current-limiting protection are employed in the constant-current drive circuit to maintain stable SLED operation and reduce the circuit footprint. A miniature dedicated temperature control chip is adopted in the temperature control circuit. The operating temperature of the SLED is acquired by linearizing the negative temperature coefficient (NTC) thermistor value and regulated through a proportional-integral-derivative (PID) compensation circuit. The size of the fabricated module (including casing) is less than 10 × 8 × 3 cm³. Experimental results show that the driver module achieves a drive current control accuracy of 0.1% and a temperature control accuracy of 0.01 °C. The output optical power fluctuation is less than 0.005 mW and the average axial resolution for OCT is 6.5992 μm with a standard deviation of 0.0107 μm. This light source driver module successfully balances control precision with structural simplicity, demonstrating excellent applicability in OCT systems. Full article