Feedback Control Signal Research Articles

Research on Space Maglev Vibration Isolation Control System Modeling and Simulation

The working accuracy of space optical payloads and sensitive components carried on space aircraft greatly depends on the pointing accuracy and stability of the platform. Based on Disturbance Free Payload (DFP) technology, non-contact maglev technology is proposed in this paper, achieving dynamic and static isolation of the platform module and payload module, so that the vibration and interference of the platform module with movable and flexible components will not be transmitted to the payload module, thereby achieving the effect of vibration isolation. High-precision active control of the payload module is adopted at the same time; the platform module follows the master–slave collaborative control strategy of the payload module, meeting the requirements of high-performance payloads. A primary and backup redundant controller is designed, using a one-to-four architecture. The control board achieves high-speed and high-precision driving current control, voltage output, and outputs current feedback signal sampling. Based on uniform magnetic field design, high-precision force control performance is ensured by adjusting current accuracy. Interdisciplinary joint simulation of electric, magnetic, and structural aspects was conducted on the magnetic levitation isolation system. By conducting physical testing and calibration and designing a testing and calibration system, it has been proven that the system meets the design requirements, achieving high-precision current control technology of 0.15 mA and driving force control technology of 0.5 mN.

Sustained Clinical Benefit of Adaptive Deep Brain Stimulation in Parkinson's Disease Using Gamma Oscillations: A Case Report.

Adaptive deep brain stimulation (aDBS) dynamically adjusts stimulation parameters according to patient needs. We recently showed that chronic aDBS utilizing invasive neural signals for feedback control is superior to conventional DBS (cDBS) during normal daily life in a 2-month trial. The stability of aDBS over longer periods remains unclear. To assess the effects of aDBS on motor symptoms and quality of life (QoL) in one individual with Parkinson's disease over 8 months. We used stimulation-entrained cortical gamma oscillations as a control signal for aDBS in the subthalamic nucleus and quantified benefits using motor diary ratings, QoL scales, and wearable metrics. We found that aDBS delivered superior and consistent benefits compared with baseline cDBS in measures of bradykinesia and QoL. aDBS can achieve prolonged, stable improvement over clinically optimized cDBS. The neural signal remains stable, and aDBS parameters remain appropriate over extended periods. © 2024 International Parkinson and Movement Disorder Society.

Rotationary feedback control of the cylinder wake flow using a linear dynamic model

This study presents an active feedback control of the Kármán vortex shedding flow past a circular cylinder at low Reynolds numbers. The cylinder's rotational motion functions as the control actuator, while the transverse velocities of points along the wake axis serve as the feedback signals. First, using the autoregressive with exogenous input method, a linear reduced-order model (ROM) for the unstable flow is developed to capture the input–output behavior between the cylinder's rotational displacement and the feedback signals. This model is then utilized for controller design using the proportional and linear quadratic regulator (LQR) control methods, respectively, with their effectiveness analyzed and validated through high-fidelity numerical simulations. The results show that both methods can effectively suppress the unstable vortex shedding flow, while proportional control exhibits strong sensitivity to monitoring point locations and time delays. The ROM-based model can accurately predict the stability characteristics of the control system, providing valuable guidance for selecting optimal feedback signals. Moreover, we show that by appropriately adjusting the phase angle between the control input and feedback signals via time delays, the performance of proportional control can be significantly enhanced. Lastly, based on the ROM, an output-feedback suboptimal control law is designed using the LQR method. This suboptimal feedback control transforms unstable fluid modes into stable ones, resulting in complete suppression of the unsteady vortex shedding. It is further revealed that the inherent mechanism of suboptimal flow control is to construct an optimal phase shift through the linear superposition of multiple feedback signals. Overall, model-based analysis results agree well with those obtained from direct numerical simulations, confirming the validity of the proposed ROM-based feedback control procedure.

Minimizing phase delay of feedback control signals for electromagnetic vibrators to reduce measurement uncertainty in ultra-low frequency vibration calibration

Suppressing the distortion of ultra-low frequency (ULF) vibration waveforms produced by an electromagnetic vibrator (EMV) presents significant challenges, leading to considerable measurement uncertainty during vibration calibration. A phase delay minimization method for feedback control signals is proposed to reduce the harmonic distortion of ULF vibration waveforms. The method quantitatively describes the influence law of the active viscoelastic approach on system frequency response characteristics. By selecting appropriate active stiffness and damping coefficients, the method minimizes the phase delay of the feedback control signal, thereby enhancing the disturbance suppression capability of the displacement and velocity composite negative feedback control. Experimental results indicate that harmonic distortion of the vibration waveform is reduced to less than 1.70 % within the ULF frequency range of 0.001 Hz ≤ f ≤ 1 Hz. Furthermore, the amplitude sensitivity calibration uncertainty, which arises from vibration waveform distortion, has been reduced to 0.038 % (k = 1.8), thereby improving the accuracy of vibration calibration.

Droplet Impedance Feedback-Enabled Microsampling Microfluidic Device for Precise Chemical Information Monitoring.

Microelectrodes have transformed our understanding of spatiotemporal responses to electrical stimulation. However, biological signals are often molecular, complicating the capture of intricate chemical signals. The microfluidic chip developed in this paper accurately measures droplet volume by using impedance analysis. The utilization of droplet volume as a feedback signal for precise microsampling pressure control ensures that microsampling remains unaffected by droplet volume influence. Once the microsampling is complete, chemiluminescence detection enables high temporal resolution and continuous and sensitive monitoring of chemical information within the droplets. Experimental verification shows that the chip can avoid volume influence through impedance feedback, achieving consistent and stable microampling at the nanoliter level (0-3 nL). In just 0.3 s, it can perform sensitive chemiluminescence detection of H2O2 and glucose within droplets. The linear detection ranges for these analytes are 10-50,000 and 20-600 μM, respectively, with the limit of detection being 0.648 and 0.334 μM. The significance of this chip lies in its ability to reveal changes in both electrical and chemical signals during transient biological processes. Its potential applications are numerous, encompassing a wide range of emerging areas such as single-cell analysis, cell communication, and cellular immunity.

A Coordinated Emergency Frequency Control Strategy Based on Output Regulation Approach for an Isolated Industrial Microgrid

Constructing isolated industrial microgrids with wind power is beneficial for improving the economic benefits of high-energy-consuming production, such as the electrolytic aluminum industry. Due to the specialized structure of industrial microgrids and the unique characteristics of the electrolytic aluminum load (EAL), the common emergency frequency control methods do not apply to the specific operational requirements of isolated industrial microgrids. Since EALs have huge regulating capacities and fast responses, this paper proposes a coordinated emergency frequency control scheme to deal with power disturbances in isolated industrial microgrids. The coordinated frequency control model of an industrial microgrid considering demand-side participation is derived. With the help of output regulation theory, a practical, feasible coordinated frequency controller is designed by introducing frequency deviation and power disturbance as feedback control signals. The proposed control scheme achieves reserve power distribution between the generation and demand sides. The microgrid frequency can be maintained within a permitted range in the presence of large power imbalances. The simulation results conducted in an actual isolated industrial microgrid validate the effectiveness and dynamic performance of the proposed control scheme.

Iterative control decoupling tuning for precision MIMO motion systems: A matrix update method

Control decoupling is the basic control step for precision MIMO (Multi-Input-Multi-Output) motion systems. After decoupling, the MIMO logical controlled plant can be diagonal dominant so that the control design for each DOF (Degree of Freedom) can be separately treated. However, due to the manufacturing tolerances and the assembling errors, the actual CoG (Center of Gravity) and actuator positions are inconsistent with the designed values. The nominal static control decoupling matrix is inaccurate, which significantly deteriorates the decoupling performance. To address the problem, this paper develops a matrix update based iterative control decoupling tuning method. Tuning with feedback control signals to achieve accurate tuning is its first distinct feature. By employing rigid-body model information to construct more accurate basis functions, fast tuning can also be achieved, especially for relatively low control bandwidth occasions. Differing from the feedforward compensation based iterative control decoupling tuning method, the matrix update method improves the dynamics of the logical controlled plant, does not increase the control complexity and is more robust to the inaccuracy of the model information. Experimental results on the short-stroke module of a precision motion stage which is the key subsystem of the lithographic projection lens testing system present significant control decoupling performance improvement (for example the peak value of the Rx-DOF servo error caused by the Z-DOF movement is reduced from 1.29 ×10−5 m to 3.19 ×10−6 m) after just two trials.

Bifurcation and phase transitions in heterogeneous non-lane-discipline-based car-following model integrating cooperative feedback control under automated and human-driven vehicles environment

Automated vehicles (AVs) equipped with adaptive cruise control (ACC) possess different driving performances from human-driven vehicles (HDVs). AVs will have an unusual impact on the traffic flow, especially in non-lane-discipline-based (NLD) environment. Therefore, we create a heterogeneous NLD car-following model (HNLD-CFM) integrating cooperative feedback control under AVs and HDVs environment. The velocity differences between two preceding vehicles and the current vehicle are adopted for the cooperative feedback control signal. The stability condition of the HNLD-CFM is inferred through control theory, which is closely related to the proportion of AVs, lateral separation of vehicles and control signal. Moreover, bifurcation phenomena are been investigated via theory analysis and simulation. Furthermore, numerical simulation clarifies that traffic congestion can be effectively suppressed and energy consumption is reduced by increasing the proportion of AVs and lateral separation of vehicles, besides a control signal.

Application of Artificial Intelligence in the Management of Coagulation Treatment Engineering System

In this paper, the application of artificial intelligence, especially neural networks, in the field of water treatment is comprehensively reviewed, with emphasis on water quality prediction and chemical dosage optimization. It begins with an overview of machine learning and deep learning concepts relevant to water treatment. Key advances and challenges in using neural networks for coagulation processes are thoroughly analyzed, including the automation of coagulant dosing, dosage level optimization, and efficiency comparisons of modeling approaches. Applications of neural networks in predicting pollutant levels and supporting water quality monitoring are explored. The review identifies avenues for improving coagulation-based modeling with neural networks, such as enhancing data quality, employing feature engineering, refining model selection criteria, and improving cross-validation methods. The necessity of continuous monitoring and adaptive optimization strategies is emphasized. Challenges such as the complexity of coagulation processes, feedback control signal acquisition, and model adaptability from simulations to real-world settings are discussed. Cost control and resource management in water treatment are also highlighted, emphasizing the optimized chemical dosage to reduce expenses while maintaining water quality compliance. In summary, this review provides valuable insights into the current state of neural network applications in water treatment and highlights key areas for further research and development. Integrating AI into coagulation processes has the potential to enhance the efficiency and sustainability of drinking water treatment.

Event-triggered exponential stabilization of delayed systems on time scales

This paper addresses exponential stabilization issue for delayed systems on time scales. A dynamic event-triggered strategy (DETS) is proposed that relies on both the last sampled state and an exponential threshold, which can reduce the transmission frequency of the feedback control signal. Through inequality techniques and comparison method, a criterion of exponential stabilization for the underlying systems is presented. Besides, the measurement errors and estimation of system states are used to prevent Zeno behavior in DETS design. Two simulations are provided to illustrate the theoretical outcomes.

Vibration suppression of smart composite beam using model predictive controller

Abstract This work presents an adaptive model predictive control (MPC) strategy to suppress the vibration in a laminated composite beam. The control method incorporates a system identification algorithm to estimate the system parameters online, which provides a precise simulation of system dynamics. A fixed-free cantilever composite beam equipped with piezoelectric actuators was used to evaluate the efficacy of the control method. The sensors and actuators are securely bonded to the upper and lower surfaces at arbitrary locations along the beam’s length. A unified mechanical displacement field is applied to all layers, while displacements are considered independently for each layer. The beam is composed of eight layers of material, each with a thickness of 0.2 mm and orientations specified as (90°/0°/90°/0°). To achieve the best performance, the parameters of the MPC were adjusted numerically. The numerical analysis revealed that placing the actuator near the clamped end at the fixed end resulted in superior control outcomes, with a settling time of approximately 1.8 s. Conversely, the longest settling time occurred when the actuator was positioned at the free end, taking around 4 s. This model could potentially be expanded to address vibration in more intricate beams exhibiting nonlinear characteristics. The deflection readings measured at the end of the beam have been utilized as feedback control signals for predicting future behavior over a predetermined control horizon. The subsequent cost function is minimized through a quadratic equation to determine the sequence of optimal yet constrained control inputs. The suggested active vibration control system is then implemented and assessed numerically to examine the effectiveness of the control method.

State Observers/Kalman Filters are Generally Unsuitable for Feedback Control While a Far More Rational and Superior Solution is Available

-An observer that estimates system state vector x(t) is a state observer. Although a state observer can generate state feedback control signal Kx(t) where the constant gain K can bese parately designed before its realizing observer, the actual observer feedback system cannot have its loop transfer function equal K(sI – A)-1B, the loop transfer function of the direct state feedback control, for a great majority of plant systems. Because loop transfer function determines the sensitivity function and robust properties of the corresponding feedback system, this implies that the robust properties of the Kx(t)-control are failed to be realized by state observers for a great majority of plant systems.Because robustness against system model uncertainty and terminal disturbance is foremost of feedback control even above performance, state observers are unsuitable for feedback control. Although this problem has initiated a vibrant robust control research in the past 40+ years, the result of that research has been unsatisfactory if parameter K is designed separately and prior to observer design. As a result, control theory remains essentially stagnant and a large amount of works still applied Kalman filters and state observers to feedback control applications. Fortunately, this vital problem has found a fundamentally novel yet decisively satisfactory solution, that is to design parameter K based on the key observer parameters! These new observers are not restricted to be state observers, and can have freely designed and reduced observer order for the first time, and thus can guarantee the full realization of loop transfer function and robust properties of their corresponding Kx(t)-control. Such an observer exits for a great majority of plant systems!In addition, this new design principle is very simple to be learned, and adjusted very easily. Thus, a design methodology that can achieve high performance and robustness for general L-T-I systems is finally developed

Lattice Boltzmann method based feedback control approach for pinned spiral waves

Spiral waves are common in nature and have received a lot of attention. Spiral wave is the source of ventricular tachycardia and fibrillation, and pinned spiral wave is less likely to be eliminated than free spiral wave. Therefore, it is important to find an effective method to control the pinned spiral wave. In this work, we investigate the feedback control approach to eliminating pinned spiral wave based on the lattice Boltzmann method, by using the FitzHugh-Nagumo model as an object. The numerical results show that the feedback control method has a good control effect on the pinned spiral wave no matter whether it is pinned on a circular or rectangular obstacle. In addition, the excitability coefficient, amplitude of feedback control, recording feedback signal time and obstacle size are systematically investigated by numerical simulation. The study shows that there are three cases of pinned spiral wave cancellation. Firstly, the amplitude of feedback control and excitability coefficient are related to the time required to eliminate the pinned spiral wave, and the larger the amplitude of feedback control signal or the smaller the excitability coefficient, the faster the cancellation of the pinned spiral waveis. Secondly, the size of the obstacle and the excitability coefficient affect the time interval between the time of recording the feedback signal and the time of adding the feedback control that can successfully control the pinned spiral wave. Finally, the recorded feedback signal time affects the minimum amplitude of feedback control required to successfully eliminate the pinned spiral wave, while the added feedback control time is constant. According to the discussion in this paper, it can be seen that the feedback control method has a good control effect on the pinned spiral wave.

Control techniques for operation of roof-top solar photovoltaics based microgrid in islanded mode

This paper presents the control techniques for the operation of roof-top solar photovoltaics based single-phase ac microgrid in islanded mode. The proposed microgrid consists of four roof-top photovoltaics on various buildings, battery energy storage system in each building, and loads. In each building, the roof-top photovoltaic system is connected to the single-phase ac loads and proposed single-phase ac microgrid through the two-stage converter, which consists of dc-dc boost converter and bidirectional single-phase voltage source converter. A bidirectional dc-dc converter is used to connect the battery energy storage system to the photovoltaic system. The control technique of the bidirectional single-phase voltage source converter, using the feed-forward and feed-back control signals, has been suggested into a rotating d-q synchronous reference frame, with the dual controllers, for controlling the power mismatch and rated sinusoidal ac voltage of the microgrid. The control technique of the bidirectional dc-dc converter, used for the battery energy storage system, is implemented with two voltage controllers and one current controller for managing of the power mismatch in the proposed microgrid system. In this work, the power control algorithms have also been developed, which are aimed to manage the power balance in the single roof-top PV based building as well as in the proposed microgrid, under islanded mode, for various operating scenarios including fault condition.

Event-Triggered Control for a Second Order ODE-Heat System Coupling at Intermediate Point

Motivated by the thermoelastic coupling effect arising in microelectromechanical systems (MEMS), event-triggered control of second-order ODE-heat systems coupled at intermediate point is investigated. The event-triggered control includes two parts, one is the feedback control signal, and the other is the event-triggered mechanism that decides when to update the control input. First, we design an event-triggered controller by using Zero-Order Hold to a continuous-time controller. Then, a dynamic triggering condition is established. Next, we derive that the existence of a minimal dwell-time, which avoids the occurrence of Zeno behavior. By utilizing Lyapunov-Krasovskii functional method, the global exponential stability of the closed-loop system is demonstrated. Finally, the simulation data based on the thermoelastic coupling system is displayed to demonstrate the availability of the theoretical results.

Real-Time Localization for Closed-Loop Control of Assistive Furniture

For people with limited mobility, navigating in cluttered indoor environment is challenging. In this work, we propose a mobile assistive furniture suite that is designed to ease the life of people with special needs in indoor movement. To enable intelligent coordination of this system, a key component is the localization of each mobile furniture. The challenge is to assess the state of an arbitrary living scenario so that the estimation can be used as a real-time feedback signal for autonomous closed-loop control of mobile furniture. We propose a perception pipeline that addresses these challenges. A machine learning model is designed and trained to jointly achieve multi-object semantic keypoint detection and classification in camera images. The synthetic data generation is employed to augment the training set and boost the model performance. A robust point cloud registration uses the detected semantic keypoints and depth information to estimate poses of the furniture. Tracking is applied to achieve smooth estimation. A high-performance accelerator that optimizes the efficiency of using heterogeneous devices is applied to achieve real-time performance. This visual perception pipeline is used in closed-loop control to steer the mobile furniture from initial to a desired location demonstrated in experiments on real hardware.

A Kernel Reinforcement Learning Decoding Framework Integrating Neural and Feedback Signals for Brain Control.

Brain-Machine Interfaces (BMIs) have the potential to allow subjects to brain control (BC) external devices, where their brain signals could be translated to the action of the neuro-prosthesis by reinforcement learning (RL) based decoder. During the BC task, feedback cues are provided to guide subject's learning. Subjects will adapt the neural signals according to the feedback cues. Concurrently, the RL decoding parameters are adjusted when the subject explores the BC task through trial and error, leading to a co-adaptive process between the subject and the decoder. However, when subjects receive the feedback cues and enhance their learning, the decoder does not actively utilize the feedback cues. If the RL decoder could integrate both neural signals and feedback cues, the training efficiency of the BC task would increase. A major challenge is the different temporal scales of neural signals and feedback cues, making it difficult to integrate them into a single decoder. In this paper, we propose a novel kernel RL decoding method as the first attempt to combine two signals with different temporal scales for RL decoding. The neural signals and the feedback cues comprise the decoding input, which is then projected into individual Reproducing Kernel Hilbert Spaces (RKHSs) respectively. These two RKHSs form a joint feature space, where the action of the neuro-prosthesis could be decoded linearly. We evaluate the proposed method on a simulated brain control cursor-reaching task. Our proposed method is compared with the kernel RL that only uses neural signals as the input. The proposed method has a faster learning speed and better decoding accuracy. The results demonstrate that our proposed method has successfully integrated the information of the feedback cue and facilitates the training procedure for the BC task.Clinical Relevance-This paper provides an integrated reinforcement learning decoding framework, which combines the neural signals and the feedback cues to increase the learning speed and the accuracy of the brain control task. Subjects could learn the task more easily with this decoder.

Development of Control Strategies for Operation of Cluster of Interconnected Hybrid Microgrids in Islanded Mode

This article presents the architecture and control techniques for interconnections of islanded hybrid smart microgrids (MGs, both dc and ac), for transferring the bidirectional power between different MGs. Three ring-type MGs, i.e., one ac microgrid (acMG) and two dc microgrids (dcMGs) operating at different voltages are interconnected to form a power park. A bidirectional dc–dc converter has been used to interconnect two dcMGs, and bidirectional three-phase voltage source converter (VSC) is used to interconnect the dcMG and acMG. The control techniques of these bidirectional VSCs are also proposed to control the operation of interconnected MGs system. The feedforward and feedback control signals are suggested in the proposed control techniques. The objectives of the proposed control techniques are to control the bidirectional power flow between the interconnected MGs, and to establish the constant rated voltages of all MGs, under islanded mode, for various operating scenarios. To check the effectiveness and robustness of the developed interconnected MGs system and proposed control techniques, the simulations have been carried out for the different operating conditions, in the real-time digital simulator environment.

Self-Excited Microcantilever with Higher Mode Using Band-Pass Filter.

Microresonators have a variety of scientific and industrial applications. The measurement methods based on the natural frequency shift of a resonator have been studied for a wide range of applications, including the detection of the microscopic mass and measurements of viscosity and stiffness. A higher natural frequency of the resonator realizes an increase in the sensitivity and a higher-frequency response of the sensors. In the present study, by utilizing the resonance of a higher mode, we propose a method to produce the self-excited oscillation with a higher natural frequency without downsizing the resonator. We establish the feedback control signal for the self-excited oscillation using the band-pass filter so that the signal consists of only the frequency corresponding to the desired excitation mode. It results that careful position setting of the sensor for constructing a feedback signal, which is needed in the method based on the mode shape, is not necessary. By the theoretical analysis of the equations governing the dynamics of the resonator coupled with the band-pass filter, it is clarified that the self-excited oscillation is produced with the second mode. Furthermore, the validity of the proposed method is experimentally confirmed by an apparatus using a microcantilever.

Leader-Following Consensus Control for Uncertain Feedforward Stochastic Nonlinear Multiagent Systems.

This article addresses the leader-following consensus problem of feedforward stochastic nonlinear multiagent systems with switching topologies. Output information for all agents, except for state information, can be acquired based on sensor measurement. Moreover, the stochastic disturbances from external unpredictable environments are considered on all agent systems with a feedforward structure. In these conditions, we propose a novel consensus scheme with a simple design procedure. First, for each follower, we construct a dynamic gain-based switched compensator using its output and its neighbor agents' outputs to provide feedback control signals. Then, for each follower, we develop a compensator-based distributed controller that is not directly associated with the topology switching signal such that it has a first derivative and antishake. Thereafter, by means of the Lyapunov stability theory, we verify that the leader-following consensus can be acquired asymptotically in probability under the controllers' action if the topology switching signal fulfills an average dwell time condition. Finally, the feasibility of the control algorithm is checked via numerical simulation.