Discrete Feedback Research Articles

On the effectiveness of Bayesian discrete feedback for quantum information reclaiming

On the effectiveness of Bayesian discrete feedback for quantum information reclaiming

Dissipative imitation learning for discrete dynamic output feedback control with sparse data sets

AbstractImitation learning enables synthesis of controllers for systems with complex objectives and uncertain plant models. However, ensuring an imitation learned controller is stable requires copious amounts of data and/or a known plant model. In this paper, we explore an input–output (IO) stability approach to imitation learning, which achieves stability with sparse data sets while only requiring coarse knowledge of the energy characteristics of the plant. A constrained optimization problem is developed, in which the controller learns to mimic expert data while maintaining stabilizing energy characteristics induced by the plant. While the learning objective is nonconvex, iterative convex overbounding (ICO) and projected gradient descent (PGD) are explored as methods to learn the controller. In numerical examples, it is shown that with little knowledge of the plant model and a small data set, the dissipativity constrained learned controller achieves closed loop stability and successfully mimics the behavior of the expert controller, while other methods often fail to maintain stability and achieve good performance.

Discrete recoil control system modelling and optimal recoil damping of deepwater drilling riser systems

This study addresses discrete dynamic modelling and the optimal recoil control problem of deepwater drilling riser systems subject to the friction resistance of fluid discharge and platform heave motion. First, a discrete-time exosystem model for friction resistance of drilling fluid discharge is developed; then, a discrete-time dynamic model of platform heave motion is established. Third, a finite-time discrete optimal recoil controller with feedforward compensation mechanisms was designed. The optimal recoil controller gains can be obtained by iteratively solving discrete equations. The simulation results show that under the discrete feedforward and feedback optimal recoil controller and pure feedback optimal recoil controller, the recoil movements of the riser system are significantly restrained. Moreover, the former is more effective than the latter at resisting the riser recoil response.

A note on the general stabilization of discrete feedback control for non-autonomous hybrid neutral stochastic systems with a delay

A note on the general stabilization of discrete feedback control for non-autonomous hybrid neutral stochastic systems with a delay

Transfer learning promotes acquisition of individual BCI skills.

Subject training is crucial for acquiring brain-computer interface (BCI) control. Typically, this requires collecting user-specific calibration data due to high inter-subject neural variability that limits the usability of generic decoders. However, calibration is cumbersome and may produce inadequate data for building decoders, especially with naïve subjects. Here, we show that a decoder trained on the data of a single expert is readily transferrable to inexperienced users via domain adaptation techniques allowing calibration-free BCI training. We introduce two real-time frameworks, (i) Generic Recentering (GR) through unsupervised adaptation and (ii) Personally Assisted Recentering (PAR) that extends GR by employing supervised recalibration of the decoder parameters. We evaluated our frameworks on 18 healthy naïve subjects over five online sessions, who operated a customary synchronous bar task with continuous feedback and a more challenging car racing game with asynchronous control and discrete feedback. We show that along with improved task-oriented BCI performance in both tasks, our frameworks promoted subjects' ability to acquire individual BCI skills, as the initial neurophysiological control features of an expert subject evolved and became subject specific. Furthermore, those features were task-specific and were learned in parallel as participants practiced the two tasks in every session. Contrary to previous findings implying that supervised methods lead to improved online BCI control, we observed that longitudinal training coupled with unsupervised domain matching (GR) achieved similar performance to supervised recalibration (PAR). Therefore, our presented frameworks facilitate calibration-free BCIs and have immediate implications for broader populations-such as patients with neurological pathologies-who might struggle to provide suitable initial calibration data.

Real-world application of a discrete feedback control system for flexible biogas production

Using renewable energy is increasingly prevalent as part of a global effort to safeguard the environment, with a reduction in {mathrm{CO}}_{2} being one of the primary objectives. A biogas plant provides an opportunity to produce green energy, but its profitability prevents it from being utilized more frequently. A suitable response to this economic issue would be flexible biogas production to exploit fluctuating energy prices. Nevertheless, the complex nature of the anaerobic digestion process that proceeds within the biogas plant and the wide range of substrates that may be utilized as the plant’s feeds make it challenging to achieve flexible biogas production truly. Most plant operators will rely on their experience and intuition to run the plant without knowing exactly how much biogas they will produce with the feed substrate. This work combines a system model estimation and feedback controller to provide an intuitive yet precise feedback control system. The system model estimation represents the biogas plant mathematically, and a total of six distinct approaches have been compared and evaluated. A PT1 model most accurately approximated the step-down and the step-up by the time percentage method, with the Akaike Information Criterion as the primary evaluation criterion for selecting the best model. The downward model was controlled by a discrete PI controller modified with the Root Locus Method and an Anti-Windup scheme, and the upward model was controlled by a state space controller. The quality of the controller was evaluated in both simulation and at the actual biogas plant in Grub, and the controller was able to reduce the biogas production rate approaching the setpoint in the expected period. Furthermore, the developed feedback control system is effortless enough to be installed in many biogas plants.

A New Cardiovascular Mock Loop Driven by Novel Active Capacitance in Normal and Abnormal Conditions.

The hybrid mock circulatory loop (hMCL) serves as a crucial hemodynamic simulation tool, offering exceptional flexibility, controllability, and reproducibility for investigating the mechanisms underlying cardiovascular diseases (CVD) in a controlled environment, circumventing the limitations of live organism studies. This paper introduces a novel design and control strategy for hMCL, introducing a novel left ventricle volume-elastance (LVVE) equation that unifies the autoregulation of the Frank-Starling mechanism (FSM) with left ventricle contractility (LVC). LVVE establishes a dynamic link between left ventricular volume (LVV) and LVC, inherently satisfying the regulatory relationship between left ventricular pressure (LVP) and LVV through a mathematical equation. For the first time, LVVE integration significantly enhances the physiological relevance of hMCL by faithfully replicating FSM responses across diverse conditions, including aortic stenosis (AS), variations in systemic vascular resistance (SVR), and heart rate (HR) variations. Furthermore, this study introduces the stability proofs for the discrete closed-loop hMCL, enabling real-time proportional valve control through discrete feedback linearization-an innovative departure from conventional methods. Notably, FSM emulation is achieved by tracking reference maximum and minimum LVV values, eliminating the reliance on predefined functions or existing data, such as the maximum LV elastance value. Rigorous experimental validation, encompassing numerical simulations and comparative analyses with prior research, attests to the precision and efficacy of the proposed hMCL in faithfully replicating both normal and abnormal CV conditions. Significantly, the hMCL demonstrates that increasing HR enhances LVC while maintaining physiological pressures; however, this increase in LVC corresponds with a decrease in LVV, in alignment with human data and FSM principles. Crucially, the coupling mechanism between the FSM and LVC yields results of enhanced physiological fidelity, significantly advancing the hMCL's utility in physiological research. Moreover, the hMCL's capacity to simulate critical cardiovascular scenarios, including AS, SVR fluctuations, and HR variations, underscores its versatility and substantial potential for investigating complex CV dynamics.

New Criteria for Analyzing the Permanence, Periodic Solution, and Global Attractiveness of the Competition and Cooperation Model of Two Enterprises with Feedback Controls and Delays

This paper studies a class of the non-autonomous competition and cooperation model of two enterprises involving discrete time delays and feedback controls. The paper proposes new criteria for analyzing the permanence, periodic solution, and global attractiveness of the model. The common mathematical techniques of the Lyapunov method, the continuation theorem, and the comparison principle are used in this paper. By means of the comparison principle and inequality techniques, the concept of permanence is investigated, which refers to the long-term survival of the enterprises within the competitive and cooperative framework. Meanwhile, using the continuation theorem, we establish conditions under which the system exhibits periodic behavior. Additionally, the global attractiveness of the system is derived by constructing multiple Lyapunov functionals. Finally, an example is presented to illustrate the applicability and validity of the proposed criteria in this paper. This example serves as a demonstration that showcases the main results derived from the analysis.

The New Four-Dimensional Fractional Chaotic Map with Constant and Variable-Order: Chaos, Control and Synchronization

Using fractional difference equations to describe fractional and variable-order maps, this manuscript discusses the dynamics of the discrete 4D sinusoidal feedback sine iterative chaotic map with infinite collapse (ICMIC) modulation map (SF-SIMM) with fractional-order. Also, it presents a novel variable-order version of SF-SIMM and discusses their chaotic dynamic behavior by employing a distinct function for the variable fractional-order. To establish the existence of chaos in the suggested discrete SF-SIMM, some numerical methods such as phase plots, bifurcation and largest Lyapunov exponent diagrams, C0 complexity and 0–1 test are utilized. After that, two different control schemes are used for the conceived discrete system. The states are stabilized and asymptotically forced towards zero by the first controller. The second controller is used to synchronize a pair of maps with non–identical parameters. Finally, MATLAB simulations will be executed to confirm the results provided.

Exponential synchronization of 2D cellular neural networks with boundary feedback

In this work we propose a new mathematical model of 2D cellular neural networks (CNN) in terms of the lattice FitzHugh-Nagumo equations with boundary feedback. The model features discrete Laplacian operators and periodic boundary feedback instead of the interior-clamped or mean-field feedback. We first prove the globally dissipative dynamics of the solutions through a priori uniform estimates. In the main result we show that the 2D cellular neural networks are exponentially synchronized if the computable threshold condition is satisfied by the synaptic gap signals of pairwise boundary cells of the grid and the system parameters with a linear feedback coupling coefficient tunable in applications.

Effect of motion state variability on error-related potentials during continuous feedback paradigms and their consequences for classification

BackgroundAn erroneous motion would elicit the error-related potential (ErrP) when humans monitor the behavior of the external devices. This EEG modality has been largely applied to brain-computer interface in an active or passive manner with discrete visual feedback. However, the effect of variable motion state on ErrP morphology and classification performance raises concerns when the interaction is conducted with continuous visual feedback. New MethodIn the present study, we designed a cursor control experiment. Participants monitored a continuously moving cursor to reach the target on one side of the screen. Motion state varied multiple times with two factors: (1) motion direction and (2) motion speed. The effects of these two factors on the morphological characteristics and classification performance of ErrP were analyzed. Furthermore, an offline simulation was performed to evaluate the effectiveness of the proposed extended ErrP-decoder in resolving the interference by motion direction changes. ResultsThe statistical analyses revealed that motion direction and motion speed significantly influenced the amplitude of feedback-ERN and frontal-Pe components, while only motion direction significantly affected the classification performance. Comparison with existing methodsSignificant deviation was found in ErrP detection utilizing classical correct-versus-erroneous event training. However, this bias can be alleviated by 16% by the extended ErrP-decoder. ConclusionThe morphology and classification performance of ErrP signal can be affected by motion state variability during continuous feedback paradigms. The results enhance the comprehension of ErrP morphological components and shed light on the detection of BCI’s error behavior in practical continuous control.

Synchronously discrete-time feedback control of large-scale systems

This paper studies the synchronously discrete-time feedback control of large-scale systems. Given an unstable complex networks (the ith subsystem is ẋi(t)=Aixi(t)+∑j=1NaijΓxj(t) ), we will design a discrete time feedback control Biei(tττ) to stabilize it. These discrete times are 0,τ,2τ,…, where τ>0 is the duration between two consecutive observations. When τ is sufficiently small, these discrete times are all over 0,∞. Thus we design an auxiliary system which is exponentially stable (the ith subsystem is eˆ̇i(t)=Aieˆi(t)+Bieˆi(t)+∑j=1NaijΓeˆj(t)), and hope to find a range of τ by the error of two feedback control systems. By constructing Lyapunov functions and using a graph-theoretic technique, we obtain a synchronization criterion.

Cloud Control of Connected Vehicle Under Both Communication-Induced Sensing and Control Delay: A Prediction Method

In this paper, we focus on the problem of cloud control of the connected vehicle with time-varying communication-induced sensing delay and control delay. First, the connected vehicle cloud control system containing communication-induced delay is modeled as the discrete linear output feedback system with bounded input and output delay. Second, corresponding to the linear output feedback system, the “predictor-observer” structured controller is introduced and redesigned to compensate for the delay in both output and input channels actively, without measurement of actual input delay as its original version. The stability of the close-looped prediction-based control system under a certain delay boundary is analyzed, and the sufficient stability condition is proposed. Third, with the help of Cone Complementary Linearization (CCL), an iterative algorithm is proposed to design stabilized controller and observer gains. Finally, the proposed method is verified through vehicle lateral and longitudinal control simulations, and the effectiveness and the performance of the proposed delay compensation strategy are innovatively evaluated under real vehicle control scenarios.

Convergence rates of a discrete feedback control arising in mean-field linear quadraticoptimal control problems

Convergence rates of a discrete feedback control arising in mean-field linear quadraticoptimal control problems

Preview the Products on Display at Display Week 2023

Preview the Products on Display at Display Week 2023

Getting off track: Cortical feedback processing network modulated by continuous error signal during target-feedback mismatch

Performance monitoring and feedback processing – especially in the wake of erroneous outcomes – represent a crucial aspect of everyday life, allowing us to deal with imminent threats in the short term but also promoting necessary behavioral adjustments in the long term to avoid future conflicts. Over the last thirty years, research extensively analyzed the neural correlates of processing discrete error stimuli, unveiling the error-related negativity (ERN) and error positivity (Pe) as two main components of the cognitive response. However, the connection between the ERN/Pe and distinct stages of error processing, ranging from action monitoring to subsequent corrective behavior, remains ambiguous. Furthermore, mundane actions such as steering a vehicle already transgress the scope of discrete erroneous events and demand fine-tuned feedback control, and thus, the processing of continuous error signals – a topic scarcely researched at present. We analyzed two electroencephalography datasets to investigate the processing of continuous erroneous signals during a target tracking task, employing feedback in various levels and modalities. We observed significant differences between correct (slightly delayed) and erroneous feedback conditions in the larger one of the two datasets that we analyzed, both in sensor and source space. Furthermore, we found strong error-induced modulations that appeared consistent across datasets and error conditions, indicating a clear order of engagement of specific brain regions that correspond to individual components of error processing.

Continuous versus discrete robotic feedback for brain-computer interfaces aimed for neurorehabilitation.

Brain-Computer Interfaces (BCI) can allow control of external devices using motor imagery (MI) decoded from electroencephalography (EEG). Although BCI have a wide range of applications including neurorehabilitation, the low spatial resolution of EEG, coupled to the variability of cortical activations during MI, make control of BCI based on EEG a challenging task. An assessment of BCI control with different feedback timing strategies was performed. Two different feedback timing strategies were compared, comprised by passive hand movement provided by a robotic hand orthosis. One of the timing strategies, the continuous, involved the partial movement of the robot immediately after the recognition of each time segment in which hand MI was performed. The other feedback, the discrete, was comprised by the entire movement of the robot after the processing of the complete MI period. Eighteen healthy participants performed two sessions of BCI training and testing, one with each feedback. Significantly higher BCI performance (65.4 ± 17.9% with the continuous and 62.1 ± 18.6% with the discrete feedback) and pronounced bilateral alpha and ipsilateral beta cortical activations were observed with the continuous feedback. It was hypothesized that these effects, although heterogenous across participants, were caused by the enhancement of attentional and closed-loop somatosensory processes. This is important, since a continuous feedback timing could increase the number of BCI users that can control a MI-based system or enhance cortical activations associated with neuroplasticity, important for neurorehabilitation applications.

Simulation of the Joint Movement of a Robotic-Towing Vehicle and an Aircraft Using Signals from an Optocoupler Matrix

Actuality. The heavy workload of airports with transport and cargo aviation creates a problem the land logistics, that seriously complicates the work of the airfield towing system and personnel working processes. That entails expences associated with delayed deliveries or equipment downtime. The usage of the automatic aircraft movement systems, which are capable of towing aircraft round the clock and quickly along the airfield, following the optimal trajectory allows to avoid such situations.Purpose of research. Creation of the mathematical model and the control algorithm for the autonomous joint movement of a wheeled aircraft towing vehicle and an aircraft, where the control is based on the signals of an optocoupler array.Methods. Achieving this goal required solving the following problems: describing of a dynamic model of wheeled bodies interconnected by an elastic element; creation of a feedback model based on a group of optocouplers detecting a contrast line; description of the logical conditions that allow determining the position of the object relative to the contrast line. To describe the dynamics of the wheeled platform, the equations of Lagrangian dynamics, as well as the numerical methods of mathematical modeling, were used. An optocoupler array is considered as a tool for estimating the position of the system relative to the contrast line.Results. A mathematical model, which explores the system movement and reveals the interaction of the system elements has been developed. An adaptive algorithm based on the discrete type feedback for the system managing is developed. An example of the control system when moving in a straight line with the passage of turns at an angle of 90 degrees is considered. A combined braking system in which the distributed brake efforts both on a aircraft and the tower are formed is proposed.Conclusion. As a result of the mathematical modeling, it was found that the system of wheeled bodies, bound by the force of elasticity, is able to perform controlled movements along a straight line and when passing sharp turns, based on the readings of the optocoupler matrix; corresponding graphs are shown.

Multisensory Pseudo‐Haptics for Rendering Manual Interactions with Virtual Objects

Recent advances in extended reality (XR) technologies make seeing and hearing virtual objects commonplace, yet strategies for synthesizing haptic interactions with virtual objects continue to be limited. Two design principles govern the rendering of believable and intuitive haptic feedback: movement through open space must feel “free” while contact with virtual objects must feel stiff. Herein, a novel multisensory approach that conveys proprioception and effort through illusory visual feedback and refers to the wrist, via a bracelet interface, discrete and continuous interaction forces that would otherwise occur at the hands and fingertips, is presented. Results demonstrate that users reliably discriminate the stiffness of virtual buttons when provided with multisensory pseudohaptic feedback, comprising tactile pseudohaptic feedback (discrete vibrotactile feedback and continuous squeeze cues in a bracelet interface) and visual pseudohaptic illusions of touch interactions. Compared to the use of tactile or visual pseudohaptic feedback alone, multisensory pseudohaptic feedback expands the range of physical stiffnesses that are intuitively associated with the rendered virtual interactions and reduces individual differences in physical‐to‐virtual stiffness mappings. This multisensory approach, which leaves users' hands unencumbered, provides a flexible framework for synthesizing a wide array of touch‐enabled interactions in XR, with great potential for enhancing user experiences.

Real-time vibrotactile pattern generation and identification using discrete event-driven feedback

This study assesses human identification of vibrotactile patterns by using real-time discrete event-driven feedback. Previously acquired force and bend sensor data from a robotic hand were used to predict movement-type (stationary, flexion, contact, extension, release) and object-type (no object, hard object, soft object) states by using decision tree (DT) algorithms implemented in a field-programmable gate array (FPGA). Six able-bodied humans performed a 2- and 3-step sequential pattern recognition task in which state transitions were signaled as vibrotactile feedback. The stimuli were generated according to predicted classes represented by two frequencies (F1: 80 Hz, F2: 180 Hz) and two magnitudes (M1: low, M2: high) calibrated psychophysically for each participant; and they were applied by two actuators (Haptuators) placed on upper arms. A soft/hard object was mapped to F1/F2; and manipulating it with low/high force was assigned to M1/M2 in the left actuator. On the other hand, flexion/extension movement was mapped to F1/F2 in the right actuator, with movement in air as M1 and during object manipulation as M2. DT algorithm performed better for the object-type (97%) than the movement-type (88%) classification in real time. Participants could recognize feedback associated with 14 discrete-event sequences with low-to-medium accuracy. The performance was higher (76 ± 9% recall, 76 ± 17% precision, 78 ± 4% accuracy) for recognizing any one event in the sequences. The results show that FPGA implementation of classification for discrete event-driven vibrotactile feedback can be feasible in haptic devices with additional cues in the physical context.