Control Method Research Articles

Wideband coherent microwave conversion via magnon nonlinearity in a hybrid quantum system

Frequency conversion is a widely realized physical process in nonlinear systems of optics and electronics. As an emerging nonlinear platform, spintronic devices have the potential to achieve stronger frequency conversion. Here, we demonstrated a microwave frequency conversion method in a hybrid quantum system, integrating nitrogen-vacancy centers in diamond with magnetic thin film CoFeB. We achieve a conversion bandwidth ranging from 0.1 to 12 GHz, presenting an up to 25th order frequency conversion and further display the application of this method for frequency detection and qubits coherent control. Distinct from traditional frequency conversion techniques based on nonlinear electric response, our approach employs nonlinear magnetic response in spintronic devices. The nonlinearity, originating from the symmetry breaking such as domain walls in magnetic films, presents that our method can be adapted to hybrid systems of other spintronic devices and spin qubits, expanding the application scope of spintronic devices and providing a promising on-chip platform for coupling quantum systems.

Design of shortcuts to adiabaticity for Bose-Einstein condensates dynamics in soliton Josephson junctions

Abstract This article primarily establishes a two-soliton system and employs the Lewis-Riesenfeld invariant inverse control method to achieve shortcuts to adiabaticity (STA) technology. We study on the atomic soliton Josephson junctions (SJJs) device and subsequently compare and analyze it with atomic bosonic Josephson junctions (BJJs). Moreover, we use higher-order expressions of the auxiliary equations to optimize the results and weaken the detrimental effect of the sloshing amplitude. We find that in the adiabatic shortcut evolution of two systems with time-containing tunnelling rates, the SJJs system is more robust over a rather short time evolution. In comparison with linear ramping, the STA technique is easier to achieve with the precise modulation of the quantum state in the SJJs system.

A novel robust MPC scheme established on LMI formulation for surge instability of uncertain compressor system with actuator constraint and piping acoustic

This paper presents a new control method for surge instability of the compressor system. Due to the importance of the active control approach in improving the efficiency of the compressor system, a new scheme based on the model predictive control (MPC) technique has been considered to control surge which gives the benefits of control signal optimization by considering the constraints on states and input. Because of designing a novel MPC by using of linear matrix inequality scheme, the optimization problem is solved in less time and with less complexity. The proposed method is able to consider the limitation of the close coupled valve actuator and also covers the uncertainty on the model. In addition, the developed model describes the nonlinear behaviour of the compressor system and handles the effects of the pipe on surge instability. Using Lyapunov theory, the stability of the closed-loop system under the proposed robust active controller is shown. The simulation results show the high pressure and high efficiency operation of the system under study in different operating conditions.

An extended hysteresis observer-based adaptive robust control method for nonlinear macro–micro motion system

In this paper, for macro–micro composite motion platform with piezoelectric hysteresis, an finite-time adaptive robust control method based on extended hysteresis observer is proposed. The dynamic model of macro–micro system is constructed at first. An extended hysteresis observer was designed to estimate the actual displacement and speed of motion system. Then, an adaptive robust control law is designed to eliminate the uncertain hysteresis model parameters. After this, exponential convergence result of the proposed control method is given and proved. By setting the expected bandwidth of macro–micro system, the gain adjustment process of the control method can be reduced in computation. The effectiveness of the proposed control method is demonstrated by comparison with other control methods in simulation, and the proposed control method has more stable tracking effect and smaller tracking error.

A fuzzy coefficient deep flux weakening algorithm for IPMSM without out-of-control

A fuzzy coefficient q-axis current increment flux weakening (FCCIFW) control method is proposed in interior permanent magnet synchronous motor (IPMSM) drives. The proposed FCCIFW not only simplifies the calculation of flux weakening coefficient according to the derived formula, but also avoids the saturation of current regulator. In FCCIFW, the theoretical calculation formula of conversion coefficient is derived firstly, and then the fuzzy rule base is established according to the formula. FCCIFW not only refrains the use of complex flux weakening calculation formula or fixed control parameters, but also refrains the problem of out-of-control in the process of deep flux weakening control, so as to improve the overall performance in the process of flux weakening. The results show that the proposed method has an effective and correct calculation process and results, Compared with different control methods in the operation process, it shows that the proposed method has more stable control effect, which has great application value in engineering.

Control method for the ship track and speed in curved channels

Due to natural and external influences in curved channels, ships frequently require adjustments in course and speed, posing challenges for existing control methods. Particularly lacking are speed control methods suitable for curved channel navigation. This study initially developed a three-degree-of-freedom MMG model incorporating external interference. It introduced an OP-PID heading controller merging optimal control strategies with traditional PID, adaptable to both external conditions and ship speed, validated through heading control simulations. The study analysed the ship's speed change process, deriving a mathematical expression for advance distance, and proposed a dichotomy-based speed control method to determine speed change points, addressing differential equations with unknown integrands. To mitigate uncertainty errors like parameter inaccuracies in ship maneuvering models and dynamic environmental disturbances, the study proposed a comprehensive control approach. This approach integrates model predictive control, feedback compensation, segment identification, and an enhanced line-of-sight (LOS) guidance method alongside the OP-PID course controller and dichotomy-based speed control. Simulation experiments in the Dongboliao Channel compared the proposed and existing methods. Results demonstrate the proposed method's capability to handle frequent course and speed adjustments effectively, even under model errors and external interference, showcasing superior track deviation and course control accuracy over existing methods.

Prediction of engine combustion chamber outlet temperature field based on deep Learning: Application in aero-engine life extension control

One of the current research hotspots is the prediction of combustion chamber flow field based on deep learning methods, but how to effectively utilize these prediction results is a key issue that needs to be addressed urgently. This paper further proposes a study on the predictive control method for engine life extension considering the dangerous point temperature of turbine guide vane based on the prediction of combustion chamber outlet temperature field. Firstly, a combustion chamber outlet temperature distribution prediction model is established through Computational Fluid Dynamics (CFD) numerical simulation and inception deconvolution network, and integrated into the variable cycle engine component level model to achieve real-time prediction of combustion chamber outlet temperature distribution. Furthermore, based on this, the dangerous point temperature of the high-pressure turbine (HT) guide vanes is introduced as a constraint into the prediction optimization, and a predictive control method for engine life extension considering the thermal mechanical fatigue life (TMFL) of the guide vanes is proposed. The simulation results show that compared to the traditional LEC control method, the temperature at the critical point of the blade is reduced by 12.28 K, and the TMFL of the blade is increased by 29.23 %. The research results of this article provide a good application example for predicting the temperature distribution at the outlet of the combustion chamber, expanding the application scenarios for predicting the flow field parameters inside the engine.

Precise pose control of shaft boring machine considering the characteristic of stratum

The shaft boring machine (SBM) is innovatively applied to shaft engineering with depth and large diameter for the advantages of efficiency, adaptability, and safety. The manual and rough pose control of SBM blocks the improvement of engineering quality and efficiency. This article presents a precise pose control method for SBM considering the characteristics of strata. The kinematic model of SBM and the mechanical-hydraulic-rock coupled dynamic model of grippers actuator are established considering the characteristics of different surrounding rock, respectively. A test rig is established to validate these models. The stratum-adapted double-layer controller is designed for the pose control from the practical aspects, including the deviation and inclination of the SBM center. The upper layer devotes to the kinematic planning between pose and displacement of grippers. The linear active disturbance rejection control (LADRC) method is used to control each gripper in the lower layer. The compensation control strategy is proposed to improve the reliability of the system under the different strata conditions. The stratum-adapted property is validated with the simulation of different surrounding rock characteristics. The control test under hard rock condition is performed with the test rig. The results indicate that the displacement control error of the single gripper cylinder is less than 1.8%, and the corresponding control error of deviation and inclination angle of the SBM center is less than 2.1% and 2.3% respectively. The proposed method can cover the precise pose control under different strata conditions, and it can be applied in the shaft infrastructure construction practically.

Optimized backstepping‐based finite‐time containment control for nonlinear multi‐agent systems with prescribed performance

AbstractIn this article, a finite‐time optimal containment control method is proposed for nonlinear multi‐agent systems with prescribed performance. First, a neural network‐based reinforcement learning algorithm is developed under the optimized backstepping framework. The algorithm employs an identifier‐critic‐actor architecture, where the identifiers, critics and actors are used to estimate the unknown dynamics, evaluate the system performance, and optimize the system, respectively. Subsequently, in order to guarantee the transient performance of the tracking error, the original system is converted into an equivalent unconstrained system. Then, the tracking errors are allowed to converge to a prescribed set of residuals in finite time by combining prescribed performance control and finite‐time optimal control techniques. Furthermore, by using the Lyapunov stability theorem, it is verified that all signals are semi‐globally practical finite‐time stable, and all followers can converge to a convex region formed by multiple leaders. Finally, the effectiveness of the proposed scheme is demonstrated by a practical example.

Inconsistency of in vitro exsheathment triggers for gastrointestinal nematode parasites of sheep, cattle and deer

Exsheathment is crucial in the transition from free-living to parasitic phase for most strongyle nematode species. A greater understanding of this process could help in developing new parasitic control methods. This study aimed to identify commonalities in response to exsheathment triggers (heat acclimation, CO2 and pH) in a wide range of species (Haemonchus contortus, Trichostrongylus spp., Cooperia spp., Oesophagostomum spp., Chabertia ovina, and members of the subfamily Ostertagiinae) from sheep, cattle and farmed deer. The initial expectation of similarity in pH requirements amongst species residing within the same organ was not supported, with unexpected pH preferences for exsheathment of Trichostrongylus axei, Trichostrongylus vitrinus, Trichostrongylus colubriformis and Cooperia oncophora. We also found differences between species in their response to temperature acclimation, with higher exsheathment in response to heat shock observed for H. contortus, Ostertagia ostertagi, T. axei, T. vitrinus and Oesophagostomum sikae. Furthermore, some species showed poor exsheathment under all experimental conditions, such as Cooperia curticei and the large intestinal nematodes C. ovina and Oesophagostomum venulosum. Interestingly, there were some significant differences in response depending on the host from which the parasites were derived. The host species significantly impacted on the exsheathment response for H. contortus, Teladorsagia circumcincta, T. vitrinus and T. colubriformis. Overall, the data showed variability between nematode species in their response to these in vitro exsheathment triggers, highlighting the complexity of finding a common set of conditions for all species in order to develop a control method based on triggering the exsheathment process prematurely.

Novel resistance control scheme for mitigating current sharing mismatches in parallel dual active bridge converters for DC fast charging stations

Abstract Dual Active Bridge (DAB) converters have gained popularity in electric vehicle charging stations due to their high efficiency and electrical isolation. As the demand for high-powered devices and large-capacity energy storage systems grows, charging systems that integrate multiple interconnected DAB modules are emerging as a promising solution. However, prolonged operation of these modules at high power levels can cause parameter deviations from the initial DAB circuit, resulting in power variations between modules. To overcome parameter deviations, this study presents an enhanced power control approach based on output resistance adjustment, intending to achieve consistent output capacity for multiple DAB modules. In the proposed enhanced power control method, the output resistance of the DAB module is considered to be controllable, and the current-sharing mismatches among DAB modules are fed back to tune the converter output resistance for mitigating current mismatches between modules. Thanks to the proposed control method, each DAB module can operate autonomously and balance the charging current between modules. When one DAB module is suddenly cut out of the system, the other DAB modules still maintain their stability with fully guaranteed load capacity. To demonstrate the feasibility of the enhanced control approach, the small signal model of the DAB system with three modules is derived together with its frequency-amplitude diagram. Then, the effect of virtual resistance on current balancing is comprehensively tested, and the proper control signal with virtual resistance is added to the DAB voltage control loop. The simulation results have demonstrated the reliability of the proposed control method with the ability to balance the charging current between modules and stabilize the system when a single DAB module fails.

Reinforcement learning optimal control method for multi chiller HVAC system in an existing office building

Abstract Given that buildings consume approximately 33% of global energy, and HVAC systems contribute nearly half of a building’s total energy demand, optimizing their efficiency is imperative for sustainable energy use. Many existing buildings operate HVAC systems inefficiently, displaying non-stationary behavior. Current reinforcement learning (RL) training methods rely on historical data, which is often obtained through costly modeling or trial-and-error methods in real buildings. This paper introduces a novel reinforcement learning construction framework designed to improve the robustness and learning speed of RL control while reducing learning costs. The framework is specifically tailored for existing office buildings. Applying this framework to control HVAC systems in real office buildings in Beijing, engineering practice results demonstrate: during the data collection phase, energy efficiency surpasses traditional rule-based control methods from the previous year, achieving significantly improved energy performance (a 17.27% reduction) with minimal comfort sacrifices. The system achieves acceptable robustness, learning speed, and control stability. Reduced ongoing manual supervision leads to savings in optimization labor. Systematic exploration of actions required for RL training lays the foundation for RL algorithm development. Furthermore, by leveraging collected data, a reinforcement learning control algorithm is established, validating the reliability of this approach. This construction framework reduces the prerequisites for historical data and models, providing an acceptable alternative for systems with insufficient data or equipment conditions.

Event-triggered learning-based robust tracking control for robotic manipulators with uncertain dynamics and non-zero equilibrium

This paper presents a novel optimal tracking control method for robotic manipulators (RMs) with system uncertainties and non-zero equilibrium points. The existing tracking control methods typically require the precise system dynamics and assume the existence of a zero equilibrium point, which may not always be true in practice. To overcome this issue, a new cost function with respect to the first derivative of system states and a known upper bound on the system’s mismatched disturbances is designed, and then an optimal control problem of RMs is formulated. Furthermore, to solve this optimization, an event-triggered critic learning control framework is proposed to estimate the solution of Hamilton–Jacobi-Bellman equation, reducing the computational burden. In the critic learning control designs, novel weight updating laws related to system stability information are proposed to train the critic neural networks, which eliminates the initial stabilization requirement. Theoretical analysis shows that the stability of the closed-loop system in the sense of uniform ultimate boundedness is ensured. It should be emphasized that the proposed control method offers a simplification of the tracking controller design process and removes the restriction on system dynamics. Finally, simulation results are given to demonstrate the effectiveness of the proposed method.

Finite-time compensation control with dead-zone estimation for a rehabilitative walker considering internal disturbance forces

This study discusses a finite-time compensation tracking control method for a rehabilitative training walker. The dynamic model with input dead zone was constructed to describe the walker, and a finite-time disturbance forces observation method was proposed based on the impact mechanism on tracking performance. This approach is novel in that the disturbance forces were observed in reverse through their effects on tracking performance, thus successfully obtaining the disturbance forces of the walker. To ensure the practical finite-time stability of the system, the nonlinear finite-time compensation tracking controller with stochastic configuration networks (SCN) dead-zone estimation was built for the rehabilitative walker. Simulation results and comparative analyses confirmed that the proposed compensation control method effectively restrains dead zone and internal disturbance forces.

Dynamic wetting characteristics of two droplets impacting a spherical dust particle

The removal of dust based on water misting is one of the most effective measures for dust control, and the basis of this method lies in the liquid–solid impact behavior. To further improve the dust removal efficiency, in this work, the removal mechanism was studied at the microscopic level, and the dynamic impact between two water droplets and a spherical dust particle was simulated using the coupled level-set/volume-of-fluid (CLSVOF) method. The optimal parameter ranges for micron-level dust removal were determined through the method of variable control, and the reliability of the obtained results was experimentally verified. When two droplets continuously or simultaneously impact a spherical particle, the liquid film formed undergoes deformation phenomena such as fusion, rupture, stretching, contraction, and fragmentation. The liquid film is more likely to break at higher droplet velocities, leading to the filling of the gas film and hindering the wetting process. The optimum contact angle and velocity range of the liquid droplets to completely encapsulate the dust particles were found to be θ = 2 and V = 10–20m/s, respectively. The experimental results also showed that for the same dust particle size, the dust removal efficiency initially increased but then decreased with increasing dust concentration. The optimum dust removal efficiency of 98.1% was achieved for θ = 2 and V = 10m/s. This study sheds new light on the dust removal mechanism of water mist-based dust removal technologies and can aid in the design and parameter optimization of more effective dust suppression systems.

Developing a non-destructive method for the detection of egg quality and freshness using micro-Raman spectroscopy

Considering the pivotal role of eggs in the food industry and their nutritional significance, this study employed micro-Raman spectroscopy of eggs, examining both shells and yolks to assess the quality and freshness of eggs. Raman spectra were collected at different temperatures and time intervals to investigate temperature and time effects, potentially indicating Raman peak reduction due to Maillard reaction and oxidation of proteins and lipids and carotenoid depletion, respectively. By calculating the ratio of Raman peaks, lipids, fatty acids, and choline methyl were introduced as biomarkers of temperature and time. Notable correlations were identified between Raman peaks and egg quality coefficients, including egg coefficient and peak 1002 cm−1 (protein), total weight and 1301 cm−1 (Lipids), yolk weight and 2934 and 3057 cm−1, total weight with peak 710 cm−1, and egg shape index and peak 3057 cm−1. Analysis of eggshells at different time intervals revealed Raman peak reduction during time, demonstrating Raman's effectiveness in assessing egg quality from its shell. Using the PLS-DA method, the classification of eggs at different temperatures and storage times using egg yolk Raman spectra was performed with 80% accuracy that belongs to the validation report, predominantly influenced by carotenoid peaks, showing Raman a practical, non-destructive method for egg quality and freshness control.

Epigallocatechin-3-gallate attenuates fluoride induced apoptosis via PI3K/FoxO1 pathway in ameloblast-like cells

Fluoride is a double-edged sword. It was widely used for early caries prevention while excessive intake caused a toxicology effect, affected enamel development, and resulted in dental fluorosis. The study aimed to evaluate the protective effect and mechanism of Epigallocatechin-3-gallate (EGCG) on the apoptosis induced by fluoride in ameloblast-like cells. We observed that NaF triggered apoptotic alterations in cell morphology, excessive NaF arrested cell cycle at the G1, and induced apoptosis by up-regulating Bax and down-regulating Bcl-2. NaF activated the insulin-like growth factor receptor (IGFR), and phosphatidylinositol-3-hydroxylase (p-PI3K), while dose-dependently down-regulating the expression of Forkhead box O1 (FoxO1). EGCG supplements reversed the changes in LS8 morphology, the cell cycle, and apoptosis induced by fluoride. These results indicated that EGCG possesses a protective effect against fluoride toxicity. Furthermore, EGCG suppressed the activation of p-PI3K and the down-regulation of FoxO1 caused by fluoride. Collectively, our findings suggested that EGCG attenuated fluoride-induced apoptosis by inhibiting the PI3K/FoxO1 signaling pathway. EGCG may serve as a new alternative method for dental fluorosis prevention, control, and treatment.

Accelerated MPC: A real-time model predictive control acceleration method based on TSMixer and 2D block stochastic configuration network imitative controller

Modern industrial processes demand real-time model predictive control (MPC) method within the constraints of limited computing resources. This paper introduces an accelerated MPC method to address the issue. Specifically, we employ the time series mixing model (TSMixer) to construct a predictive model capable of accurately forecasting the behavior of the system under control. Furthermore, we extend the capabilities of TSMixer by integrating a feature classification model (TSMixer with FCM). This enhanced version takes into account both future information and static variables, resulting in superior accuracy compared to the transformer model while maintaining a significantly smaller parameter count. Finally, to further enhance MPC’s responsiveness, we develop a two-dimensional block stochastic configuration network (2D-BSCN) imitative controller. This innovative network clones the behavior of rolling optimization, effectively replacing online optimization to reduce computational time. The experimental results show that the accelerated MPC has an excellent performance in numerical simulation and actual industrial processes. Notably, the algorithm achieves a smaller FLOPs and tracking time than other state-of-the-art models, well within the requirement for industrial process control.

Feedback-linearization decoupling based coordinated control of air supply and thermal management for vehicular fuel cell system

The coordinated control of the coupled system of air supply and thermal management is one of the important ways to improve the efficiency of proton exchange membrane fuel cell (PEMFC) system. The novelty of this paper is to design a feedback-linearization decoupling based coordinated controller combining with active disturbance rejection control (ADRC) method. Aiming at strong coupling of air supply and thermal management system, we have proposed a three-dimensional particle swarm coordinated optimization algorithm to calculate the steady-state reference, then, a feedback-linearization decoupling controller is designed to achieve independent control of air flow, cathode pressure and temperature. Besides, aiming at time-varying, nonlinear interference, we have designed an ADRC based on the decoupled method to achieve better dynamic response and stability. The current step load test reveals that the overshoot and the average tracking error are much smaller under the air flow-pressure-temperature decoupling coordinated controller compared that under the proportional-integral-derivative (PID) controller and the air flow-pressure decoupling controller. The test results under different ambient temperatures indicate that the proposed coordinated strategy reduces the tracking error by over 10 %, the efficiency of fuel cell system is increased by 3.5%–4.5 %.

An improved Bi-RRT*-based path planning algorithm with adaptive search strategy assignment mechanism for ultra-low-altitude penetration of fixed-wing aircraft

The ultra-low-altitude penetration with complex three-dimensional terrain environment usually requires the path planning algorithm to be computationally efficient to generate feasible optimized flight paths in a limited period of time. This paper investigates an improved bidirectional rapidly-exploring random tree* (RRT*) path planning algorithm with adaptive search strategy assignment mechanism to accelerate the exploration speed. The greedy optimization method and the three-dimensional Dubins method are utilized to reduce redundant nodes and optimize the flight path to satisfy the kinematic constraints of the fixed-wing aircraft. The model predictive control method is then employed to design the path-following controller, in which multiple types of constraints can be addressed. To demonstrate the superior planning efficiency of the proposed adaptive-alternating-exploration RRT* method, the existing widely-used path planning algorithms are used as benchmarks in the simulation studies. Simulation results show that the path planning time can be significantly reduced by up to 90% with the proposed adaptive-alternating-exploration RRT* method, simultaneously with optimized path length. Moreover, the designed model predictive path-following controller succeeds to track the planned collision-free path with maximum tracking errors of less than 2 meters. The effectiveness of the path planning algorithm and the designed controller is thoroughly demonstrated.