Search Results (1,642)

We report the development of a high-power, cost-effective, and rapidly tunable laser system optimized for rubidium optical pumping in spin-exchange optical pumping (SEOP) applications. The system combines a spectrally narrowed diode laser bar with a low-cost yet high-stability thermal-management architecture based on consumer-grade CPU liquid-cooling components. Wavelength narrowing and fast tuning are achieved by linearly translating a chirped volume Bragg grating (CVBG), providing mode-hop-free, continuous wavelength control without relying on slow thermal tuning mechanisms. Long-term wavelength stability is ensured through a terminal proportional–integral–derivative (PID) feedback loop that locks the laser directly to the rubidium absorption spectrum in the pumping cell, rather than to an internal reference. Operating near 795 nm, the laser delivers up to 40 W of optical power with a measured linewidth of approximately 0.15 nm. The system supports rapid wavelength agility over a continuous tuning range of $794.73\pm 0.24$ nm and exhibits stable spectral performance during extended operation. Owing to its compact design, fast response, and substantially lower cost than conventional volume-grating-based systems, this laser architecture provides a practical and scalable solution for SEOP and other precision atomic and spectroscopic applications that require high power, a narrow linewidth, and robust wavelength stability. Full article

With the continuous increase in coal mining depth, rock burst occurs frequently, which poses a serious threat to coal mine safety production. As the key equipment to ensure the stability of coal mine working face, the response characteristics of the hydraulic support safety valve are directly related to the life safety of coal miners and the protection of equipment. To address the problem that the traditional hydraulic support safety valve has a slow response and cannot release pressure rapidly, a new control strategy of a hydraulic support safety valve based on the magnetorheological effect is proposed. The fixed current control strategy and the fuzzy PID strategy based on grey predictive control are studied to improve the response speed and pressure relief efficiency of the safety valve. The effectiveness of the control strategy is verified by AMESim and Simulink co-simulation. The simulation results show that the new control strategy can significantly improve the dynamic response characteristics of the safety valve, shorten the response time and enhance the pressure relief performance. The superiority of the magnetorheological effect safety valve in improving the impact resistance of the coal mine hydraulic support is verified. This study provides a new technical path and theoretical basis for the optimal design of the safety valve of coal mine hydraulic support and the safety protection under rock burst. Full article

The automation of petroleum extraction columns requires robust and adaptive control due to the highly nonlinear nature of the heat and mass transfer processes involved. In this study, a hybrid control system integrating conventional fuzzy logic with quantum-inspired computational optimization is proposed to enhance the control of temperature and flow rates in industrial extraction columns. The hybrid quantum-inspired fuzzy controller is applied to a petroleum extraction column. The controller adopts fuzzy rule weights using a quantum-inspired optimization algorithm. Compared with classical PID and fuzzy controllers, it reduces settling time and solvent consumption. A MATLAB/Simulink-based simulation model of the extraction column was developed to validate the approach. Experimental tests were conducted using synthetic data and varying operational parameters to evaluate control performance. The hybrid controller achieved a 0.7% reduction in phenol consumption and reduced temperature deviations by 2.2% compared to a baseline fuzzy controller. Energy savings ranged from 1% to 2% depending on the operating scenarios. These results were confirmed through repeated simulations and statistical analysis. The proposed system demonstrates the potential of quantum-inspired fuzzy control to enhance process efficiency, reduce energy use, and improve product quality in complex chemical extraction applications. The statistical evaluation was based on repeated simulation runs and comparative performance metrics rather than physical experiments. Full article

Temperature regulation of gas-phase ethylene polymerization fluidized bed reactors (FBRs) is challenging due to strong nonlinearities, highly exothermic reaction kinetics, and frequent process disturbances. Conventional Proportional–Integral–Derivative (PID) control often exhibits limited robustness under such conditions, while advanced strategies such as Nonlinear Model Predictive Control (NMPC) may suffer from sensitivity to model mismatch and disturbances. In this study, an Adaptive Integral Sliding Mode Control (AISMC) strategy is proposed for temperature control of nonlinear gas-phase FBRs. The controller integrates adaptive gain adjustment with an integral sliding surface to improve disturbance rejection and steady-state accuracy while mitigating chattering. The performance of the proposed approach is evaluated through closed-loop simulations over an 18 h dynamic operating scenario involving multiple setpoint changes, catalyst activity variations, and feed flow disturbances. Simulation results demonstrate that AISMC achieves the best overall tracking performance, with a mean absolute error (MAE) of 0.092 K and the lowest maximum temperature deviation among the evaluated controllers. Compared to PID (MAE = 0.794 K) and conventional sliding mode control (MAE = 0.179 K), AISMC provides substantial improvements in transient and steady-state behaviors. In contrast, NMPC exhibits degraded tracking performance (MAE = 0.809 K) under the considered disturbance conditions. All controllers demonstrate sub-millisecond execution times; however, AISMC attains superior accuracy without excessive computational cost. These results indicate that AISMC offers an effective balance between robustness, accuracy, and real-time feasibility for industrial gas-phase polymerization reactors. Full article

Fractional order controllers are more frequently encountered in industrial applications due to their robustness and the improved performance they offer to the system. A large majority of research papers focus on methods for tuning controllers that are robust to gain variations. A novel approach to the design of a robust fractional order PID controller to variations in the time constant is studied in this manuscript. The procedure mentioned is developed for a first order plus time delay system. The robustness criterion used in the control algorithm is based on partial derivatives. The nonlinear system of equations obtained from all the imposed performance criteria is solved using the graphical method. To prove the efficiency of the proposed strategy, numerical simulations and experimental validation of the resulting controller are performed on a model of the DC servo system. The experimental results explicitly prove that the controller is robust to time-constant variations within the range of $\pm 70\%$. Full article

Underwater vehicles performing sampling tasks often encounter significant buoyancy variations due to payload adjustments and environmental changes, which severely challenge the stability and accuracy of controllers. To address this issue, this paper proposes a hybrid control framework that integrates Proximal Policy Optimization (PPO) with adaptive PID tuning. The framework employs PPO to dynamically adjust PID parameters online while incorporating output saturation, stepwise quantization, and dead zone filtering to ensure control safety and actuator longevity. A dual-error state representation—combining instantaneous error and its derivative—along with actuator command buffering is introduced to compensate for system lag and inertia. Comparative simulations and experimental tests demonstrate that the proposed method achieves faster convergence, lower steady-state error, and smoother control signals compared to both conventional PID and pure PPO-based control. The framework is validated through pool tests and field trials, confirming its robustness under realistic hydrodynamic disturbances. This work provides a practical and safe solution for adaptive depth control of sampling-capable AUVs operating in dynamic underwater environments. Full article

Steer-by-wire (SBW) systems in wheel-steering vehicles enhance maneuverability by eliminating mechanical linkages. However, they are susceptible to delays between steering input and pinion response, which can compromise control precision and safety. To mitigate these delays, we developed a machine learning-based compensation method employing a hybrid architecture of convolutional neural networks (CNNs) and gated recurrent units (GRUs) to predict and adjust pinion behavior in real time. The model was trained using experimental data collected from a four-wheel steering test platform, including steering angle inputs, motor signals, and pinion position feedback. By learning the relationship between steering commands and rack force, the model enables dynamic delay correction under both nominal and fault conditions. The system is implemented on an NXP microcontroller and validated through experimental testing, and compared with other hybrid model configurations for performance evaluation. The results demonstrate that the CNN–GRU approach reduces the average steering delay to 3 ms, outperforming conventional PID tuning methods while maintaining high accuracy and system stability. Full article

Modern robotic tasks often require interaction with the surrounding elements in the workspace. In some high-precision tasks, it is essential to stabilize the contact force on a smooth yet rigid surface, which can be modeled as a unilateral constraint. This challenge becomes increasingly complex in the presence of disturbances. This study addresses these issues using a robust fuzzy force-position controller that combines the approximation capabilities of fuzzy inference systems with the nonlocal properties of fractional operators. The proposed approach extends the error integration to include proportional-integral-derivative (PID) components of the position error, along with the integral of the contact force error. This formulation leverages the orthogonality between force and velocity subspaces to achieve accurate force-position stabilization. Additionally, an adaptive mechanism enhances closed-loop performance and robustness. The effectiveness of the proposed controller is validated through analytical derivations and simulations, thereby demonstrating its reliability in constrained environments. Full article

To improve solar energy utilization efficiency, address control precision issues in solar panel tracking systems, and strengthen the sustainable supply capacity of clean renewable energy, this study proposes an innovative variable universe fuzzy adaptive PID control algorithm for high-precision solar tracking systems. Based on this algorithm, a fusion scheme combining a high-precision four-quadrant detector and GPS positioning is employed to achieve real-time and precise positioning of the tracking system. The azimuth and elevation angle deviations between the real-time solar rays and the system’s actual position are calculated and used as input signals for the tracking control system. These deviations are dynamically corrected by the variable universe fuzzy adaptive PID controller, which drives a stepper motor to achieve high-precision solar tracking. The results demonstrate that, under ideal operating conditions, the proposed algorithm reduces the steady-state error by 3.5–4.9°, shortens the settling time by 4.4–5.8 s, decreases the rise time by 0.6 s, lowers the overshoot by 18–19%, and reduces the disturbance recovery time by 1.3 s. These improvements significantly enhance tracking accuracy and dynamic response efficiency. Under complex operating conditions, the algorithm reduces the steady-state error by 3.2–5.9°, shortens the settling time by 5.4–6.2 s, decreases the rise time by 0.7 s, lowers the overshoot by 17.5–19%, and reduces the disturbance recovery time by 1.5 s, thereby ensuring stable and efficient solar tracking and maintaining continuous energy capture. By quantitatively optimizing multiple performance metrics, this algorithm significantly enhances the control precision of solar panel tracking and improves solar energy utilization efficiency. It holds substantial significance for promoting the transition of the energy structure toward cleaner and more sustainable sources. Full article

The vertical actuation of multi-axis seismic simulators usually requires a redundant parallel scheme for high load capacity. Due to geometric over-constraints, the internal force coupling and the nonlinear hysteresis are high; thus, waveform reproduction quality and structural fatigue may result. A displacement–force dual closed loop cooperative control mechanism can address these problems. First, a real-time kinematic model is developed to overcome the platform pose via actuator extension, and second, a dynamic force balance loop is introduced to actively redistribute the load components. In addition, a fuzzy PID controller is incorporated to optimize gain scheduling online, compensating for hydraulic nonlinearities and time-varying structural parameters. In the experiment on a 3 × 3 m 6-DOF shaking table, the presented method performs very favorably compared to traditional methods. Under broadband random excitation, the THD of acceleration waveform drops from 15.2% (single-loop control) to 3.2%, and the internal momentum oscillation amplitude is suppressed by over 70%. The results show that our proposed method eliminates internal force dependence while maintaining high precision trajectory tracking for seismic simulation. Full article

This paper presents a control architecture based on Pixhawk4 to address the transition mode control issue of a tilt-rotor tricopter. While the classical PID controller designed for the rotor mode can handle most engineering applications under normal environmental conditions, it does not fully consider disturbances such as those arising from internal perturbations or the external environment. In response, this paper proposes a controller design with disturbance observation to meet the robustness requirements of the unmanned aerial vehicle (UAV) under perturbed conditions. For the state-constrained control problem of the UAV, the original model is simplified, and a three-channel model for control purposes is introduced. By incorporating a barrier Lyapunov function, the state constraint problem of the UAV is solved, ensuring a smooth transition from rotor mode to fixed-wing mode. To address the high complexity of virtual control law derivatives in backstepping control, a filter is used to estimate the derivatives of the UAV′s virtual states, and a linear extended state observer is introduced to estimate external and transitional disturbances during flight. Lyapunov theory is employed to ensure the closed-loop stability of the control scheme. Finally, inspired by the hunting process of aquilas, the Aquila Optimizer (AO) optimization algorithm is applied to optimize the controller’s hyperparameters, further enhancing the reliability and transient performance of the control system. Full article

Maintaining stable pressure in the oxidation–compressor section of purified terephthalic acid (PTA) plants is essential for ensuring efficient and reliable operation. Conventional single-loop proportional integral derivative (PID) controllers frequently perform inadequately because of the large pressure drop between the compressor discharge and reactor inlet, which should ideally remain at approximately 1.2 kg/cm² above the reactor pressure setpoint but can reach up to 2.8 kg/cm² due to downstream vapor-phase disturbances. Through this study, we aimed to address this issue by developing a robust cascade pressure control strategy to improve pressure stability and reduce energy losses. Dynamic process models were constructed using system identification techniques to represent real plant behavior, and the best-performing models—identified based on minimum root mean square error (RMSE)—were determined using the Wade method for pressure indicating controller PIC-101, the Lilja method for PIC-102, and the Smith method for pressure differential indicating controller PDIC-101. The proposed cascade configuration was tuned using the Lopez ISE method and evaluated under representative disturbance scenarios. The results showed that the cascade controller significantly improved pressure control, enhanced disturbance rejection, and lowered the risk of reactor shutdowns compared with the conventional proportional-integral PI-based approach. Overall, this study demonstrated that model-driven cascade control can enhance robustness, operational reliability, and energy efficiency in large-scale PTA oxidation processes. Full article

To address the increasing demands for faster response and higher control accuracy in the braking systems of electric and intelligent vehicles, a novel brake-by-wire actuation unit and its braking force control methods are proposed. The braking unit employs a permanent-magnet linear motor as the driving actuator and utilizes the lever-based force-amplification mechanism to directly generate the caliper force. Compared with the “rotary motor and motion conversion mechanism” configuration in other electromechanical braking systems, the proposed scheme significantly simplifies the force-transmission path, reduces friction and structural complexity, thereby enhancing the overall dynamic response and control accuracy. Due to the strong nonlinearity, time-varying parameters, and significant thermal effects of the linear motor, the braking force is prone to drift. As a result, achieving accurate force control becomes challenging. This paper proposes a model-free adaptive control method based on compact-form dynamic linearization. This method does not require an accurate mathematical model. It achieves dynamic linearization and direct control of complex nonlinear systems by online estimation of pseudo partial derivatives. Finally, the proposed control method is validated through comparative simulations and experiments against the fuzzy PID controller. The results show that the model-free adaptive control method exhibits significantly faster braking force response, smaller steady-state error, and stronger robustness against external disturbances. It enables faster dynamic response and higher braking force tracking accuracy. The study demonstrates that the proposed brake-by-wire scheme and its control method provide a potentially new approach for next-generation high-performance brake-by-wire systems. Full article

The global transition towards decentralized, decarbonized energy systems worldwide must include robust methods for controlling hybrid AC/DC microgrids to integrate diverse renewables and storage technologies effectively. This paper presents a Finite Control Set–Model Predictive Control (FCS-MPC) architecture for coordinated control of a hybrid microgrid comprising photovoltaic and wind generation, along with an energy storage system and MATLAB/Simulink component-level modeling. The islanded and grid-connected modes of operation are seamlessly simulated at the component level, ensuring maximum power point tracking and stability. The method has been experimentally validated through dynamic simulations across a range of operating conditions, demonstrating good performance: PV and wind MPPT efficiency > 99%, DC-link voltage control with <2% overshoot, AC voltage THD < 3%, and efficient grid synchronization. It is superior to conventional PID and sliding mode control in terms of dynamic response, voltage deviation (reduced compared to before), and power quality. The proposed FCS-MPC is an all-in-one solution to enhance the stability, reliability, and efficiency of modern hybrid microgrids. Full article

Managing frequency stability in modern interconnected power systems is a critical challenge, particularly under continuous load variations and increasing system complexity. In response to these challenges, this study introduces an Improved Grey Wolf Optimizer (IGWO)-based Proportional–Integral–Derivative (PID) controller as a solution for effective Load Frequency Control (LFC). The proposed method is tested on interconnected power systems integrating thermal (reheat and non-reheat) and hydropower plants. The simulations focus on continuous load variation and nonlinearity cases, where the GRC block is added in the model to closely mimic real-world operating conditions. The findings demonstrate that the IGWO-PID controller outperforms by achieving faster stabilization, minimizing frequency deviations, and ensuring robust performance compared to the Particle Swarm Optimization (PSO) algorithm. These results highlight the controller’s adaptability and scalability, offering a reliable approach to maintaining stability and operational efficiency in interconnected power systems. Full article