Control Method Research Articles

A tube-based model predictive control method for intelligent vehicles path tracking

A tube-based model predictive control method for intelligent vehicles path tracking

Experimental study of emission characteristics for fully premixed combustion of dimethyl ether and hydrogen based on flame ions

The detection of flame ion current is a promising approach in fully premixed combustion. However, how to control the excess air coefficient (α) by ion current still needs an effective method. For this reason, the application of natural gas (12T), hydrogen-doped 12T (12T-H) and dimethyl ether (DME) in fully premixed combustion is experimentally studied in this paper. The emission characteristics of NOx, CO and CO2 from fully premixed condensing gas boilers (FPCGB) were analyzed. The detection voltage generated by the flame ion current is used to monitor the NOx emissions of different gas types. A novel combustion control method based on flame ion current is proposed. The results show that hydrogen addition can reduce NOx, CO and CO2 emissions of 12T. The highest NOx emission is observed at a hydrogen volume ratio (HVR) of 15 % in the experiment. The detection voltage gradually decreases with increasing HVR. The CO2 of DME is always lower than 12T. When α exceeds 1.21, 1.31 and 1.12, respectively, DME exhibits higher values in NOx, CO and detection voltage compared to that of 12T. In addition, two combustion characteristic values are identified and proposed for combustion feedback control. The peak voltage (Up) can be used as a parameter to identify the type of gas. The emission voltage (Ue) can be used to determine whether the NOx emission concentration exceeds the 40 ppm threshold.

Dynamic characteristics and control method of the solar-coal energy complementary system

The solar-coal energy complementary technology is an effective way to use solar energy for power generation. In this work, a 330 MW coal-fired power generation unit, with an integrated solar field system, was studied to improve solar energy utilization efficiency and control performance. The dynamic mathematical model of the system was established using a lumped parameter method; the maximum relative error was found to be less than 5 %. Different coupling methods for the solar thermal system and coal-fired power unit were studied, and the dynamic characteristics of the unit under different solar radiation intensities were obtained. Finally, a coordinated control system was proposed based on the multiscale decomposition of load commands. The results show that the new coordinated control system showed more flexibility and less fluctuation of power during regulation. It has the best control effect, with the integral of the absolute value of the error criterion reaching 890.8 and 795 in step and slope experiments, respectively, and the integral of time multiplied by the squared error criterion reaching 4.172 × 105 and 3.043 × 105, respectively. This study successfully demonstrates the effective use of solar-coal energy complementary technology and provides an important reference for future work.

Observer-based adaptive neutral network inverse optimal containment control for nonlinear multiagent systems with input quantization

This article focuses on the issue of addressing an adaptive neural network (NN) inverse optimal containment control for nonlinear multiagent systems (MASs), which are subject to immeasurable states and quantized input signals simultaneously. To tackle this problem, we utilize a NN to model unknown agents and design a NN observer to estimate the immeasurable states. Additionally, we decompose the hysteretic quantized input into two bounded nonlinear functions. By employing the adaptive backstepping approach and inverse optimal principle, we formulate an adaptive NN inverse optimal containment control method. The developed inverse optimal containment control scheme guarantees that the controlled system is input-to-state stabilizable (ISS). Finally, we validate the effectiveness of our proposed control scheme through simulation results.

41 Is there anything we can do to lessen the severity of PRRSv?

Abstract Veterinarians and the swine industry continue to battle PRRS virus (v), as demonstrated by both the MSHMP (Morrison Swine Health Monitoring Project) and the SDRS (Swine Disease Reporting System). Both systems depict a continued increase in PRRSv prevalence and percentage of positive cases. The PRRSv variants that are circulating today seem to be more severe, especially in the last 2 to 3 yr, with more significant health and production losses in the nursery and finishing phases for prolonged periods of time. The best thing that can be done is to avoid having the virus with a good bioexclusion plan and if sites become infected, a strong biocontainment plan to avoid contaminating more sites should be considered. Realizing that bioexclusion is not always possible and sites will become infected, there are methods to help to reduce the impact of the virus. With the concerns of recombination pushing the virus evolution along faster, more producers are working toward eliminating field viruses from their systems. The use of filtration both on sow farms and nurseries has become more common as well as an additional method of bioexclusion. In the face of an outbreak, obtaining an accurate diagnosis is still one of the most important things that we do. In farrow to wean farms, serum samples are generally used when clinical signs (e.g., abortions, off feed) are observed to target testing to specific animals that are more likely to allow for early detection. In nursery and finishing sites, the use of oral fluid has worked very well as a screening tool when there are clinical signs or problems within a group or flow. Fortunately, there are differential tests that can be used to identify field infection and vaccines strains (since most wean to finish pigs are vaccinated in pig dense areas). With severe strains, depopulation and repopulation strategies have become more popular as a control method, but the majority of herd would use a herd closure. However, the amount of time required for these closures is extending, which lengthens the impacts on sow farms. Among wean to finish systems, the use of depopulating and cleaning up the site is used most, with only routine down times. Anti-inflammatories can also be administered in the acute phase of the outbreak in addition to the use of antibiotics to control secondary bacterial infections, as well as providing a good environment to allow the pigs to better deal with the disease. It’s best to keep the virus out of the herd with a good bioexclusion program. However, when we do have outbreaks, anti-inflammatory and antibiotics to help the pigs through it are important to utilize as we work to eliminate the field virus from the herd.

Predictive torque control of induction motor for rotor bar faults diagnosis

Unlike DC motors and synchronous motors, they are maintenance-free motors due to the absence of brushes, commutators and slip rings. Induction motors can be operated in polluted and explosive environments as they do not have brushes which can cause sparks. In this paper, the performance of two control techniques, namely direct torque control (DTC) and predictive torque control (PTC), are compared in transient and static states when applied to a faulty induction machine (IM). The current and torque ripples is evaluated in a healthy machine, as well as in the presence of faults, at various speed and load values. During the transient state, the objective is to assess the method that provides the optimal dynamic response, which is achieving the desired speed without any overshooting while during the static state, the objective is to minimize torque ripple and harmonics in the stator current. The Discrete Wavelet Transform (DWT) is used to analyze stator phase current. In addition, the energy eigen value (EEV) analysis has been used to determine the fault severity. The healthy and faulty systems are simulated using Matlab/Simulink for the two control methods. The results show the superiority of the PTC method compared to the DTC. A comparison of the proposed control method with other works reported in the literature is performed to verify the superiority of the proposed strategy.

A homotopy-based reinforcement learning scheme to optimal control for Markov switched interconnected systems

ABSTRACT In this paper, a homotopy-based reinforcement learning optimal control method is developed for Markov switched interconnected systems with unknown system dynamics. By utilising the subsystem decomposition method and parallel learning control method, the solution of the game coupled algebraic Riccati equations with jumping parameters is approximated. To dispense with the requirement of initial stability, a homotopy-based policy iteration is introduced, which can place unstable poles into a stable plane. In this regard, a model-free reinforcement learning method is presented to design the optimal controller for Markov switched interconnected systems. Finally, the effectiveness of the proposed method is verified by a numerical example and a practical example.

A new control method for leader–follower consensus problem of uncertain constrained nonlinear multi-agent systems

The design of Adaptive Fuzzy Sliding-Mode Control (AFSMC) is discussed in this study for the distributed leader–follower consensus problem of nonlinear uncertain multi-agent systems with input and state constraints. Avoiding the critical circumstances related to the violation of input and state constraints is an important issue in the design of consensus control systems. The limitations of existing adaptive consensus controllers developed for full-state constrained systems motivated the present novel method without restrictive structural assumptions. To convert the original problem to an unconstrained consensus control problem, the proposed control system uses a barrier function-based state transformation method and the input-state linearization methodology. As a result, each agent’s adaptive fuzzy sliding-mode control input, which consists of a fuzzy system and a robust term, is constructed based on the derived representation of the system dynamics. The fuzzy system is used to approximate the dynamic equations of the agent, its neighbors, and maybe the leader in each local feedback control law structure, and the robust term is designed to compensate for the fuzzy approximation error. The magnitudes of the robust terms and the output vectors of the fuzzy systems are also determined using the proposed adaptation laws. The second Lyapunov theorem and Barbalat’s lemma are used to verify the closed-loop system’s asymptotic stability. The effectuality of the proposed methodology is confirmed by numerical simulations for several Autonomous Underwater Vehicles (AUVs). Simulation results show that by means of the proposed AFSMC, the leader–follower consensus is achieved without state constraint violation and performance degradation.

Adaptive Fuzzy Fixed-Time Control for Uncertain Nonlinear Systems with Mismatched Disturbances

This paper focuses on addressing the adaptive fuzzy fixed-time issue for a class of nonlinear systems with uncertainty functions and mismatched disturbances. Fuzzy logical systems are utilized for identifying unknown functions. Additionally, to tackle challenges posed by mismatched disturbances, disturbance observers are constructed based on the backstepping method. Utilizing the adding one power integrator approach and the fixed-time control method, this paper introduces a fixed-time adaptive fuzzy control algorithm. Notably, this algorithm accommodates the presence of unknown mismatched disturbances and nonlinear functions. The paper establishes, through the application of the Lyapunov stability theory, that the designed adaptive fixed-time fuzzy control algorithm ensures practical fixed-time stability for the resulting closed-loop systems. Finally, the effectiveness of the derived strategy is demonstrated through an illustrative example involving two cases.

Optimal declutching control of hinged multiple floating bodies

In deep seas, there is an increasing utilisation of multiple floating structures as devices for harnessing renewable energy, such as hybrid wind-wave energy systems and multi-body wave energy converters. The control strategies have a great influence on the motion characteristics and energy conversion efficiency of these systems. The control technology brings more benefits to the system's performance, yet also introduces more complex challenges. This paper aims to fill the research gap in the control of multiple floating bodies and to propose a novel optimal declutching control method. Declutching control enables discontinuous loading and releasing of the force from power take-off system between bodies. It was found that the feasibility of the proposed method is highly dependent on the choice of objective functions, iteration numbers, and wave frequencies. With appropriate parameters, the declutching control can achieve a variety of objectives for a hinged floating multibody system, such as reducing pitch motion, improving extracted wave power, increasing relative rotational speed, and enhancing power extraction. The control effects on the velocity and the force-velocity phase relationship and how they benefit the objectives are discussed. All objective functions show a better control effect (at least 7.76%) on their respective performance indices than others.

Quantum-inspired deep reinforcement learning for adaptive frequency control of low carbon park island microgrid considering renewable energy sources

The low carbon park islanded microgrid faces operational challenges due to the high variability and uncertainty of distributed renewable energy sources. These sources cause severe random disturbances that impair the frequency control performance and increase the regulation cost of the islanded microgrid, jeopardizing its safety and stability. This paper presents a data-driven intelligent load frequency control (DDI-LFC) method to address this problem. The method replaces the conventional LFC controller with an intelligent agent based on a deep reinforcement learning algorithm. To adapt to the complex islanded microgrid environment and achieve adaptive multi-objective optimal frequency control, this paper proposes the quantum-inspired maximum entropy actor-critic (QIS-MEAC) algorithm, which incorporates the quantum-inspired principle and the maximum entropy exploration strategy into the actor-critic algorithm. The algorithm transforms the experience into a quantum state and leverages the quantum features to improve the deep reinforcement learning’s experience replay mechanism, enhancing the data efficiency and robustness of the algorithm and thus the quality of DDI-LFC. The validation on the Yongxing Island isolated microgrid model of China Southern Grid (CSG) demonstrates that the proposed method utilizes the frequency regulation potential of distributed generation, and reduces the frequency deviation and generation cost.

Safe Autonomous Docking of Spacecraft: A RTT-conbined Output-constrained Recursive Control Method

Safe Autonomous Docking of Spacecraft: A RTT-conbined Output-constrained Recursive Control Method

Artificial neural network‐based adaptive control for nonlinear dynamical systems

SummaryThis research article presents an artificial neural network (ANN)‐based indirect adaptive control method for nonlinear dynamical systems. In this article, a modified Elman recurrent neural network (MERNN) is proposed as an identifier and controller for controlling nonlinear systems. The architecture of the proposed controller is a modified form of the existing Elman recurrent neural network. The parameter training of ANN‐based controllers is obtained by using the most popular optimization algorithm which is known as the back‐propagation algorithm. A comparative study includes Elman, Diagonal, Jordan, feed‐forward neural network (FFNN), and radial basis function network (RBFN)‐based controllers to compare with the proposed MERNN controller. To determine the controller's robustness, parameter variations, and disturbance signals have been considered. The performance analysis of the proposed controller is illustrated by two simulation examples. The simulation results reveal that MERNN can not only identify the unknown dynamics of the plant but also adaptively control it compared to the others.

A novel adaptive fast sliding mode control method based on fuzzy algorithm for the air management system of fuel cell stack

Proton exchange membrane fuel cells (PEMFCs) offer advantages, such as, cleanliness, high efficiency, and environmental friendliness. To achieve optimal control of the PEMFC stack's air supply and management system under complex operating conditions, a novel non-singular terminal sliding mode controller based on fuzzy algorithm is proposed. Compared with the conventional sliding mode control, the terminal sliding mode control exhibits further performance improvements. However, it still has limitations. The proposed innovative sliding mode structure would ensure finite-time convergence and avoid the issue of singularity associated with terminal sliding mode control. Moreover, fuzzy logic makes sliding mode gain adapt to the system and further enhances the performance and stability, which is benefited from a stronger sliding surface. Results indicate that, compared with the other controllers, the designed controller could reduce delay further and provide faster response to sudden changes. The overshoot of sliding surface has decreased by 70% compared to its previous level. This novel control approach significantly enhances the efficiency and reliability of the PEMFC system.

Aerodynamic performance and wake development of NACA 0018 airfoil with serrated gurney flaps

Improving the aerodynamic performance of the airfoil is important for optimising the rotor efficiency of the vertical axis wind turbines. As a simple passive control method, the Gurney flap is widely used to improve the aerodynamic performance of airfoils. In this paper, we study the impact of applying a novel serrated gurney flap with different heights on the NACA 0018 airfoil. An improved delayed detached eddy simulation method is adopted to investigate the lift-enhancing mechanism of the serrated gurney flap and the evolution of the downstream vortex system. The results show that the serrated gurney flap can effectively increase the airfoil lift coefficient and the lift-to-drag ratio. The improvement of the serrated gurney flap on the aerodynamic performance of the airfoil is more pronounced at moderate angles of attack. Further analysis of the downstream wake shows that a pair of vortices wraps over both sides of the airfoil and rotates perpendicular to the wake flow, which is produced by the columnar vortex upstream of the flap. These vortices mixed with the wake and accelerated the dissipation of the separated vortex on the suction surface of the airfoil.

FDR control for linear log-contrast models with high-dimensional compositional covariates

Linear log-contrast models have been widely used to describe the relationship between the response of interest and the compositional covariates, in which one central task is to identify the significant compositional covariates while controlling the false discovery rate (FDR) at a nominal level. To achieve this goal, a new FDR control method is proposed for linear log-contrast models with high-dimensional compositional covariates. An appealing feature of the proposed method is that it completely bypasses the traditional p-values and utilizes only the symmetry property of the test statistic for the unimportant compositional covariates to give an upper bound of the FDR. Under some regularity conditions, the FDR can be asymptotically controlled at the nominal level for the proposed method in theory, and the theoretical power is also proven to approach one as the sample size tends to infinity. The finite-sample performance of the proposed method is evaluated through extensive simulation studies, and applications to microbiome compositional datasets are also provided.

Optical-pulse-based reconfigurable phase control for independent beamforming and band fusion in a dual-band phased array receiver.

This Letter proposes an optical-pulse-based reconfigurable phase control method, enabling a dual-band phased array receiver to operate in two modes: dual-band-independent operation and dual-band fusion. The method utilizes optical pulses and optical delay to compensate for phase differences across frequency bands. An electrical phase shifter is employed to compensate for phase residual in both bands. All phase operations to both bands are processed concurrently in one link, thereby maintaining inter-band phase coherence. Experimental results verify the ability of dual-band-independent beamforming and inter-band phase coherence maintaining. A four-channel dual-band (X- and Ku-band) phased array antenna (PAA) receiver is constructed to measure PAA patterns and demonstrate band fusion. The pulse compression results in all directions reveal a doubled improvement in range resolution, which shows the potential for enhancement of radar performance.

Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators

Purpose To provide high torques needed to move a robot’s links, electric actuators are followed by a transmission system with a high transmission rate. For instance, gear ratios of 100:1 are often used in the joints of a robotic manipulator. This results into an actuator with large mechanical impedance (also known as nonback-drivable actuator). This in turn generates high contact forces when collision of the robotic mechanism occur and can cause humans’ injury. Another disadvantage of electric actuators is that they can exhibit overheating when constant torques have to be provided. Comparing to electric actuators, pneumatic actuators have promising properties for robotic applications, due to their low weight, simple mechanical design, low cost and good power-to-weight ratio. Electropneumatically actuated robots usually have better friction properties. Moreover, because of low mechanical impedance, pneumatic robots can provide moderate interaction forces which is important for robotic surgery and rehabilitation tasks. Pneumatic actuators are also well suited for exoskeleton robots. Actuation in exoskeletons should have a fast and accurate response. While electric motors come against high mechanical impedance and the risk of causing injuries, pneumatic actuators exhibit forces and torques which stay within moderate variation ranges. Besides, unlike direct current electric motors, pneumatic actuators have an improved weight-to-power ratio and avoid overheating problems. Design/methodology/approach The aim of this paper is to analyze a nonlinear optimal control method for electropneumatically actuated robots. A two-link robotic exoskeleton with electropneumatic actuators is considered as a case study. The associated nonlinear and multivariable state-space model is formulated and its differential flatness properties are proven. The dynamic model of the electropneumatic robot is linearized at each sampling instance with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. Within each sampling period, the time-varying linearization point is defined by the present value of the robot’s state vector and by the last sampled value of the control inputs vector. An H-infinity controller is designed for the linearized model of the robot aiming at solving the related optimal control problem under model uncertainties and external perturbations. An algebraic Riccati equation is solved at each time-step of the control method to obtain the stabilizing feedback gains of the H-infinity controller. Through Lyapunov stability analysis, it is proven that the robot’s control scheme satisfies the H-infinity tracking performance conditions which indicate the robustness properties of the control method. Moreover, global asymptotic stability is proven for the control loop. The method achieves fast convergence of the robot’s state variables to the associated reference trajectories, and despite strong nonlinearities in the robot’s dynamics, it keeps moderate the variations of the control inputs. Findings In this paper, a novel solution has been proposed for the nonlinear optimal control problem of robotic exoskeletons with electropneumatic actuators. As a case study, the dynamic model of a two-link lower-limb robotic exoskeleton with electropneumatic actuators has been considered. The dynamic model of this robotic system undergoes first approximate linearization at each iteration of the control algorithm around a temporary operating point. Within each sampling period, this linearization point is defined by the present value of the robot’s state vector and by the last sampled value of the control inputs vector. The linearization process relies on first-order Taylor series expansion and on the computation of the associated Jacobian matrices. The modeling error which is due to the truncation of higher-order terms from the Taylor series is considered to be a perturbation which is asymptotically compensated by the robustness of the control algorithm. To stabilize the dynamics of the electropneumatically actuated robot and to achieve precise tracking of reference setpoints, an H-infinity (optimal) feedback controller is designed. Actually, the proposed H-infinity controller for the model of the two-link electropneumatically actuated exoskeleton achieves the solution of the associated optimal control problem under model uncertainty and external disturbances. This controller implements a min-max differential game taking place between: (i) the control inputs which try to minimize a cost function which comprises a quadratic term of the state vector’s tracking error and (ii) the model uncertainty and perturbation inputs which try to maximize this cost function. To select the stabilizing feedback gains of this H-infinity controller, an algebraic Riccati equation is being repetitively solved at each time-step of the control method. The global stability properties of the H-infinity control scheme are proven through Lyapunov analysis. Research limitations/implications Pneumatic actuators are characterized by high nonlinearities which are due to air compressibility, thermodynamics and valves behavior and thus pneumatic robots require elaborated nonlinear control schemes to ensure their fast and precise positioning. Among the control methods which have been applied to pneumatic robots, one can distinguish differential geometric approaches (Lie algebra-based control, differential flatness theory-based control, nonlinear model predictive control [NMPC], sliding-mode control, backstepping control and multiple models-based fuzzy control). Treating nonlinearities and fault tolerance issues in the control problem of robotic manipulators with electropneumatic actuators has been a nontrivial task. Practical implications The novelty of the proposed control method is outlined as follows: preceding results on the use of H-infinity control to nonlinear dynamical systems were limited to the case of affine-in-the-input systems with drift-only dynamics. These results considered that the control inputs gain matrix is not dependent on the values of the system’s state vector. Moreover, in these approaches the linearization was performed around points of the desirable trajectory, whereas in the present paper’s control method the linearization points are related with the value of the state vector at each sampling instance as well as with the last sampled value of the control inputs vector. The Riccati equation which has been proposed for computing the feedback gains of the controller is novel, so is the presented global stability proof through Lyapunov analysis. This paper’s scientific contribution is summarized as follows: (i) the presented nonlinear optimal control method has improved or equally satisfactory performance when compared against other nonlinear control schemes that one can consider for the dynamic model of robots with electropneumatic actuators (such as Lie algebra-based control, differential flatness theory-based control, nonlinear model-based predictive control, sliding-mode control and backstepping control), (ii) it achieves fast and accurate tracking of all reference setpoints, (iii) despite strong nonlinearities in the dynamic model of the robot, it keeps moderate the variations of the control inputs and (iv) unlike the aforementioned alternative control approaches, this paper’s method is the only one that achieves solution of the optimal control problem for electropneumatic robots. Social implications The use of electropneumatic actuation in robots exhibits certain advantages. These can be the improved weight-to-power ratio, the lower mechanical impedance and the avoidance of overheating. At the same time, precise positioning and accurate execution of tasks by electropneumatic robots requires the application of elaborated nonlinear control methods. In this paper, a new nonlinear optimal control method has been developed for electropneumatically actuated robots and has been specifically applied to the dynamic model of a two-link robotic exoskeleton. The benefit from using this paper’s results in industrial and biomedical applications is apparent. Originality/value A comparison of the proposed nonlinear optimal (H-infinity) control method against other linear and nonlinear control schemes for electropneumatically actuated robots shows the following: (1) Unlike global linearization-based control approaches, such as Lie algebra-based control and differential flatness theory-based control, the optimal control approach does not rely on complicated transformations (diffeomorphisms) of the system’s state variables. Besides, the computed control inputs are applied directly on the initial nonlinear model of the electropneumatic robot and not on its linearized equivalent. The inverse transformations which are met in global linearization-based control are avoided and consequently one does not come against the related singularity problems. (2) Unlike model predictive control (MPC) and NMPC, the proposed control method is of proven global stability. It is known that MPC is a linear control approach that if applied to the nonlinear dynamics of the electropneumatic robot, the stability of the control loop will be lost. Besides, in NMPC the convergence of its iterative search for an optimum depends on initialization and parameter values selection and consequently the global stability of this control method cannot be always assured. (3) Unlike sliding-mode control and backstepping control, the proposed optimal control method does not require the state-space description of the system to be found in a specific form. About sliding-mode control, it is known that when the controlled system is not found in the input-output linearized form the definition of the sliding surface can be an intuitive procedure. About backstepping control, it is known that it cannot be directly applied to a dynamical system if the related state-space model is not found in the triangular (backstepping integral) form. (4) Unlike PID control, the proposed nonlinear optimal control method is of proven global stability, the selection of the controller’s parameters does not rely on a heuristic tuning procedure, and the stability of the control loop is assured in the case of changes of operating points. (5) Unlike multiple local models-based control, the nonlinear optimal control method uses only one linearization point and needs the solution of only one Riccati equation so as to compute the stabilizing feedback gains of the controller. Consequently, in terms of computation load the proposed control method for the electropneumatic actuator’s dynamics is much more efficient.

Long chain unsaturated fatty acids alter growth and reduce biofilm formation of Cronobacter sakazakii

AbstractCronobacter sakazakii is a foodborne pathogen predominately transmitted through contaminated dried foods and affects populations including neonates, infants, and the elderly. Following several recent outbreaks, it is now a notifiable infection in those under 12 months of age. Current control methods include strict manufacturing guidelines, with monitoring of this genus a legal requirement in powdered infant formula production. Fatty acids have long been known as antimicrobials, with long‐chain fatty acids increasingly identified as agents that target virulence factors. This study gives insight into the changes promoted by three long‐chain unsaturated fatty acids (oleic, linoleic, and α‐linolenic) on C. sakazakii growth, morphology, and biofilm formation. Each fatty acid was individually introduced to C. sakazakii 29544 both as a sole carbon source and as an addition to complex media. Following comparison to the untreated control, bacterial cells treated with these fatty acids showed a significant and media‐dependent impact on growth and biofilm inhibition. With further characterization, long‐chain fatty acids, including α‐linolenic acid, could be utilized as a control method with minimal safety constraints regarding their use in the food production environment.

Research on High-Stability Composite Control Methods for Telescope Pointing Systems under Multiple Disturbances.

During the operation of space gravitational wave detectors, the constellation configuration formed by three satellites gradually deviates from the ideal 60° angle due to the periodic variations in orbits. To ensure the stability of inter-satellite laser links, active compensation of the breathing angle variation within the constellation plane is achieved by rotating the optical subassembly through the telescope pointing mechanism. This paper proposes a high-performance robust composite control method designed to enhance the robust stability, disturbance rejection, and tracking performance of the telescope pointing system. Specifically, based on the dynamic model of the telescope pointing mechanism and the disturbance noise model, an H∞ controller has been designed to ensure system stability and disturbance rejection capabilities. Meanwhile, employing the method of an H∞ norm optimized disturbance observer (HODOB) enhances the nonlinear friction rejection ability of the telescope pointing system. The simulation results indicate that, compared to the traditional disturbance observer (DOB) design, utilizing the HODOB method can enhance the tracking accuracy and pointing stability of the telescope pointing system by an order of magnitude. Furthermore, the proposed composite control method improves the overall system performance, ensuring that the stability of the telescope pointing system meets the 10 nrad/Hz1/2 @0.1 mHz~1 Hz requirement specified for the TianQin mission.