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This review paper provides an overview of the approaches to model neuromuscular control, focusing on methods to identify nonoptimal control strategies typical of populations with neuromuscular disorders or children. Where possible, the authors tightened the description of the methods to the mechanisms behind the underlying biomechanical and physiological rationale. They start by describing the first and most simplified approach, the reductionist approach, which splits the role of the nervous and musculoskeletal systems. Static optimization and dynamic optimization methods and electromyography-based approaches are summarized to highlight their limitations and understand (the need for) their developments over time. Then, the authors look at the more recent stochastic approach, introduced to explore the space of plausible neural solutions, thus implementing the uncontrolled manifold theory, according to which the central nervous system only controls specific motions and tasks to limit energy consumption while allowing for some degree of adaptability to perturbations. Finally, they explore the literature covering the explicit modeling of the coupling between the nervous system (acting as controller) and the musculoskeletal system (the actuator), which may be employed to overcome the split characterizing the reductionist approach. [ABSTRACT FROM AUTHOR]

We use control-based continuation (CBC) to perform an experimental bifurcation study of a periodically forced dual-beam. The nonlinearity is of geometric nature, provided by a thin, clamped beam. The overall system exhibits hysteresis and bistability in its open-loop frequency response due to a hardening, Duffing-like nonlinear stiffness, which can be designed or adjusted by choosing the properties of the thin beam. We employ local stabilising feedback control to implement CBC and track stable periodic solutions past the fold points. Thus obtained continuous solution branches are used to generate the solution surface over the plane of excitation amplitude and frequency. This surface features two curves of fold bifurcations that meet at a cusp point, and they delimit the experimentally observed bistability range of this nonlinear beam. [ABSTRACT FROM AUTHOR]

Serviceability issues associated with structural lateral vibration are observed frequently, where human–structure interaction (HSI) plays a crucial role. Inverted pendulum (IP) models have attracted much attention from researchers due to their ability to naturally present human mechanical behaviors and significant advantages in studying the mechanisms of HSI. This paper investigates the behaviors of a 3D bipedal spring-loaded inverted pendulum (BSLIP) model while walking on laterally vibrating surfaces. First, we adopt feedback control strategies for the 3D BSLIP human walking model to achieve stable walking. Then, we investigate phase drift, phase pulling, and synchronization behaviors of the human body using the 3D BSLIP. The results indicate that the equivalent damping and mass coefficients of the 3D BSLIP basically agree with the available experimental results from the literature. Compared to the 3D IP model with rigid legs, the equivalent coefficients obtained from the 3D BSLIP exhibit similar ranges and trends as the control parameter increases. Furthermore, the 3D BSLIP and 3D IP models successfully replicate phase pulling and synchronization phenomena. The synchronization phase angles fall within the range of 102.9∘–228.9∘ for the 3D IP and 209∘–349∘ for the 3,D BSLIP model, consistent with the experimental results reported in the existing literature. [ABSTRACT FROM AUTHOR]

ABSTRACT This article proposes a novel method to accelerate the boundary feedback control design of cascaded parabolic difference equations (PDEs) through DeepONet. The backstepping method has been widely used in boundary control problems of PDE systems, but solving the backstepping kernel function can be time‐consuming. To address this, a neural operator (NO) learning scheme is leveraged for accelerating the control design of cascaded parabolic PDEs. DeepONet, a class of deep neural networks designed for approximating nonlinear operators, has shown potential for approximating PDE backstepping designs in recent studies. Specifically, we focus on approximating gain kernel PDEs for two cascaded parabolic PDEs. We utilize neural operators to map only two kernel functions, while the other two are computed using the analytical solution, thus simplifying the training process. We establish the continuity and boundedness of the kernels, and demonstrate the existence of arbitrarily close DeepONet approximations to the kernel PDEs. Furthermore, we demonstrate that the DeepONet approximation gain kernels ensure stability when replacing the exact backstepping gain kernels. Notably, DeepONet operator exhibits computation speeds two orders of magnitude faster than PDE solvers for such gain functions, and their theoretically proven stabilizing capability is validated through simulations. [ABSTRACT FROM AUTHOR]

Potential transformers are vital for measuring and protecting the power grid. Their accuracy and reliability directly impact the stability and security of the power system. To address the issues with traditional high-precision potential transformer calibrators, such as cumbersome operation and low efficiency, a high-precision potential transformer calibrator has been developed. The calibrator is based on an embedded system architecture of FPGA and ARM. It uses a high-precision current comparator along with feedback control technology. By monitoring and adjusting the error feedback voltage, it can perform the automated calibration of potential transformers with an accuracy class of 0.0001. The measurement ranges from 0.00001% to 200.0%. This study can be adapted to meet the development needs of modern digital measurement systems. [ABSTRACT FROM AUTHOR]

We consider the stabilization of a class of linear evolution systems z ′ = A z + B v under the observation y = C z by means of a finite dimensional control v. The control is based on the design of a Luenberger observer which can be infinite or finite dimensional (of dimension large enough). In the infinite dimensional case, the operator A is supposed to generate an analytical semigroup with compact resolvent and the operators B and C are unbounded operators whereas in the finite dimensional case, A is assumed to be a self-adjoint operator with compact resolvent, B and C are supposed to be bounded operators. In both cases, we show that if (A, B) and (A, C) verify the Fattorini-Hautus Criterion, then we can construct an observer-based control v of finite dimension (greater or equal than largest geometric multiplicity of the unstable eigenvalues of A) such that the evolution problem is exponentially stable. As an application, we study the stabilization of the diffusion system. [ABSTRACT FROM AUTHOR]

This paper concerns the polynomial-logarithmic stability and stabilization of time-varying control systems. We present sufficient Lyapunov-like conditions guaranteeing this polynomial-logarithmic stability with applications to several linear and nonlinear control systems. [ABSTRACT FROM AUTHOR]

Introduction: Chaotic resonance is similar to stochastic resonance, which emerges from chaos as an internal dynamical fluctuation. In chaotic resonance, chaos-chaos intermittency (CCI), in which the chaotic orbits shift between the separated attractor regions, synchronizes with a weak input signal. Chaotic resonance exhibits higher sensitivity than stochastic resonance. However, engineering applications are difficult because adjusting the internal system parameters, especially of biological systems, to induce chaotic resonance from the outside environment is challenging. Moreover, several studies reported abnormal neural activity caused by CCI. Recently, our study proposed that the double-Gaussian-filtered reduced region of orbit (RRO) method (abbreviated as DG-RRO), using external feedback signals to generate chaotic resonance, could control CCI with a lower perturbation strength than the conventional RRO method. Method: This study applied the DG-RRO method to a model which includes excitatory and inhibitory neuron populations in the frontal cortex as typical neural systems with CCI behavior. Results and discussion: Our results reveal that DG-RRO can be applied to neural systems with extremely low perturbation but still maintain robust effectiveness compared to conventional RRO, even in noisy environments. [ABSTRACT FROM AUTHOR]

Background: Patients with neurological disorders including stroke use rehabilitation to improve cognitive abilities, to regain motor function and to reduce the risk of further complications. Robotics-assisted tilt table technology has been developed to provide early mobilisation and to automate therapy involving the lower limbs. The aim of this study was to evaluate the feasibility of employing a feedback control system for heart rate (HR) during robotics-assisted tilt table exercise in patients after a stroke. Methods: This feasibility study was designed as a case series with 12 patients ( n = 12 ) with no restriction on the time post-stroke or on the degree of post-stroke impairment severity. A robotics-assisted tilt table was augmented with force sensors, a work rate estimation algorithm, and a biofeedback screen that facilitated volitional control of a target work rate. Dynamic models of HR response to changes in target work rate were estimated in system identification tests; nominal models were used to calculate the parameters of feedback controllers designed to give a specified closed-loop bandwidth; and the accuracy of HR control was assessed quantitatively in feedback control tests. Results: Feedback control tests were successfully conducted in all 12 patients. Dynamic models of heart rate response to imposed work rate were estimated with a mean root-mean-square (RMS) model error of 2.16 beats per minute (bpm), while highly accurate feedback control of heart rate was achieved with a mean RMS tracking error (RMSE) of 2.00 bpm. Control accuracy, i.e. RMSE, was found to be strongly correlated with the magnitude of heart rate variability (HRV): patients with a low magnitude of HRV had low RMSE, i.e. more accurate HR control performance, and vice versa. Conclusions: Feedback control of heart rate during robotics-assisted tilt table exercise was found to be feasible. Future work should investigate robustness aspects of the feedback control system. Modifications to the exercise modality, or alternative modalities, should be explored that allow higher levels of work rate and heart rate intensity to be achieved. [ABSTRACT FROM AUTHOR]

On-chip optical power monitors are indispensable for functional implementation and stabilization of large-scale and complex photonic integrated circuits (PICs). Traditional on-chip optical monitoring is implemented by tapping a small portion of optical power from the waveguide, which leads to significant loss. Due to its advantages like non-invasive nature, miniaturization, and complementary metal-oxide-semiconductor (CMOS) process compatibility, a transparent monitor named the contactless integrated photonic probe (CLIPP), has been attracting great attention in recent years. The CLIPP indirectly monitors the optical power in the waveguide by detecting the conductance variation of the local optical waveguide caused by the surface state absorption (SSA) effect. In this review, we first introduce the fundamentals of the CLIPP including the concept, the equivalent electric model and the impedance read-out method, and then summarize some characteristics of the CLIPP. Finally, the functional applications of the CLIPP on the identification and feedback control of optical signal are discussed, followed by a brief outlook on the prospects of the CLIPP. [ABSTRACT FROM AUTHOR]

In this paper, we study the long time behavior of solutions for an optimal control problem associated with the magnetic Bénard problem in a two dimensional bounded domain, achieved through the adjustment of distributed controls. We first construct a quasi-optimal solution for the magnetic Bénard problem characterized by exponential decay over time. We then derive preliminary estimates concerning the extended temporal behavior of all admissible solutions to the magnetic Bénard problem. Next we establish the existence of a solution for the optimal control problem over both finite and infinite time intervals. Additionally, we present the first-order necessary optimality conditions for the finite time interval case. Finally, we establish the long-time decay characteristics of the solutions for the optimal control problem. [ABSTRACT FROM AUTHOR]

We introduce a new inequality similar to the fractional Poincar$ \acute{e} $ inequality and obtain the continuous data assimilation and feedback control of fractional reaction-diffusion equations. The feedback control scheme has finite number of determining parameters. The continuous data assimilation is obtained based on finite-dimensional feedback controls. [ABSTRACT FROM AUTHOR]

China’s birth rate has declined, but its mortality rate has risen year after year due to the new coronavirus pandemic. Using the new coronavirus pandemic as a feedback control variable, we proposed a new non-autonomous singlepopulation feedback control model in which the feedback control variable reduces the population’s birth rate while increasing the population’s mortality rate. We determined sufficient conditions for the persistence, extinction, and global stability. The analytical results are then compared numerically with relevant examples. [ABSTRACT FROM AUTHOR]

The estimation and control of the substrate temperature in the Metal Organic Chemical Vapor Deposition (MOCVD) of superconductor tape manufacturing is investigated. In the advanced MOCVD (AMOCVD) for High-Temperature Superconductor (HTS) tape manufacturing developed at the University of Houston, Ohmic resistance heating is implemented to heat the rolling tape at a reference temperature. Control of the electric current through the tape is proposed to ensure substrate temperature uniformity that is essential for reducing nanoscale defect growth. The substrate temperature measurement is corrupted by the continuous deposition of precursor gas material on the crystal rods of the pyrometers in the reaction chamber challenging the real-time control process. The proposed approach for model-based substrate temperature control is based on the real-time estimation of the states of the substrate temperature model dynamics, and the sensor drift and drift rate, using an extended Kalman filter (EKF) combined with particle swarm optimization (PSO) to optimally tune the process and measurement noise covariance matrices. Experimental data from the AMOCVD facility are used to validate the model parameter and the sensor drift estimation process. The estimated temperature is used in a simulation study of a PSO-tuned LQR controller with adjustable gains designed to ensure uniform heating along the tape by controlling the electric current, and the results are compared with those of a PI controller. Precise temperature regulation in Ohmic heating is critical within a multitude of manufacturing and material processes, and the proposed methods constitute a systematic framework for model-based temperature estimation and control. [ABSTRACT FROM AUTHOR]

This article addresses the robust fixed‐time synchronization of fuzzy shunting‐inhibitory cellular neural networks (FSICNNs) with delays by utilizing two different types of control strategies. First, a feedback controller is proposed to achieve fixed‐time synchronization of FSICNNs. Secondly, a novel adaptive controller is designed to guarantee fixed‐time synchronization of FSICNNs, automatically adjusting all control gains without the need for advanced settings. The use of differential inequality techniques and the Lyapunov function method yields several sufficient conditions to ensure fixed‐time synchronization for the considered FSICNNs. Finally, an example, along with its numerical simulation, is presented to demonstrate the validity of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

In this study, we investigated autonomous vessel obstacle avoidance using advanced techniques within the Guidance, Navigation, and Control (GNC) framework. We propose a Mixed Integer Linear Programming (MILP) based Guidance system for robust path planning avoiding static and dynamic obstacles. For Navigation, we suggest a multi-modal neural network for perception, demonstrating the identification of obstacle type, position, and orientation using imaging sensors. Additionally, the paper compares an error-based PID control strategy and a Model Predictive Control (MPC) scheme as well. This evaluation aids in better evaluating their performance and determining their applicability within the GNC scheme. We detail the implementation of these systems, present simulation results, and offer a performance evaluation using an experimental dataset. Our findings, analysed through qualitative discussion and quantitative performance indicators, contribute to advancements in autonomous navigation and the control strategies to achieve it. [ABSTRACT FROM AUTHOR]

A single nitrogen-vacancy (NV) center in a diamond can be used as a nanoscale sensor for magnetic field, electric field or nuclear spins. Due to its low photon detection efficiency, such sensing processes often take a long time, suffering from an electron spin resonance (ESR) frequency fluctuation induced by the time-varying thermal perturbations noise. Thus, suppressing the thermal noise is the fundamental way to enhance single-sensor performance, which is typically achieved by utilizing a thermal control protocol with a complicated and highly costly apparatus if a millikelvin-level stabilization is required. Here, we analyze the real-time thermal drift and utilize an active way to alternately track the single-spin ESR frequency drift in the experiment. Using this method, we achieve a temperature stabilization effect equivalent to sub-millikelvin (0.8 mK) level with no extra environmental thermal control, and the spin-state readout contrast is significantly improved in long-lasting experiments. This method holds broad applicability for NV-based single-spin experiments and harbors the potential for prospective expansion into diverse nanoscale quantum sensing domains. [ABSTRACT FROM AUTHOR]

The accurate installation of long-span arch bridges' arch ribs remains a challenge due to the complex calculations required for cable forces and arch rib displacements, as well as the significant influence of environmental and construction loads. In this study, we propose a practical approach to alignment and error feedback control for long-span arch bridges. Cable forces were optimized using multiple control objectives based on influence matrix principles. The impact of temperature on the next segment to be installed was analyzed using the metastatic GM(1, 1) model and fitting results. Several tunable parameters were employed to account for parameter errors and environmental interference. These parameters were adjusted based on the deviations between practical and theoretical alignments for different arch rib segments, achieving a model output of an offset-free-tracking arch rib structure. This technology was applied to monitor the construction of the Tian'e Longtan Grand Bridge. Compared to conventional alignment control approaches, the proposed method achieved excellent arch ring alignment after the closure of the high-accuracy arch rib and cable release, as well as effective control of cable force uniformity and tower deviation. Field measurement data indicate that the closing deviation of the arch ring is only 3 mm. This study provides a valuable reference for the construction control of long-span arch bridges. [ABSTRACT FROM AUTHOR]

Different from an ordinary project, a large group mission like the unmanned aerial vehicle (UAV) swarm cooperative strike mission is performed by multiple executors and needs to be strictly carried out according to the plan. Because of the complex cooperative relationships between the sub-missions that make up a large mission, a small disturbance may cause a delay in the entire plan. Therefore, the mission process must be precisely controlled in real time to resist disturbances and ensure that the mission proceeds as planned. To address the real-time process control problem of large group missions, we propose a novel dual-code group network plan diagram model that enables plan description and process tracking for complex group missions. Additionally, a mission process closed-loop feedback control system is designed that models the mission process control problem as a mapping from the mission state observation to plan adjustment. Furthermore, an analytic-based mission process control strategy is proposed and rigorously proven to converge and be effective, as well as demonstrate the maximum anti-disturbance capability. Finally, the control strategy is tested on a UAV swarm cooperative strike mission containing 56 sub-missions. The simulation results demonstrate that the proposed control strategy is capable of achieving high, fast, and accurate control for the mission process and enhancing the anti-disturbance capability of the plan by adjusting the mission plan in real time. This will provide a valuable reference for the management of large group missions. [ABSTRACT FROM AUTHOR]

Abstract Background Patients with neurological disorders including stroke use rehabilitation to improve cognitive abilities, to regain motor function and to reduce the risk of further complications. Robotics-assisted tilt table technology has been developed to provide early mobilisation and to automate therapy involving the lower limbs. The aim of this study was to evaluate the feasibility of employing a feedback control system for heart rate (HR) during robotics-assisted tilt table exercise in patients after a stroke. Methods This feasibility study was designed as a case series with 12 patients ( $$n = 12$$ n = 12 ) with no restriction on the time post-stroke or on the degree of post-stroke impairment severity. A robotics-assisted tilt table was augmented with force sensors, a work rate estimation algorithm, and a biofeedback screen that facilitated volitional control of a target work rate. Dynamic models of HR response to changes in target work rate were estimated in system identification tests; nominal models were used to calculate the parameters of feedback controllers designed to give a specified closed-loop bandwidth; and the accuracy of HR control was assessed quantitatively in feedback control tests. Results Feedback control tests were successfully conducted in all 12 patients. Dynamic models of heart rate response to imposed work rate were estimated with a mean root-mean-square (RMS) model error of 2.16 beats per minute (bpm), while highly accurate feedback control of heart rate was achieved with a mean RMS tracking error (RMSE) of 2.00 bpm. Control accuracy, i.e. RMSE, was found to be strongly correlated with the magnitude of heart rate variability (HRV): patients with a low magnitude of HRV had low RMSE, i.e. more accurate HR control performance, and vice versa. Conclusions Feedback control of heart rate during robotics-assisted tilt table exercise was found to be feasible. Future work should investigate robustness aspects of the feedback control system. Modifications to the exercise modality, or alternative modalities, should be explored that allow higher levels of work rate and heart rate intensity to be achieved.

Abstract On-chip optical power monitors are indispensable for functional implementation and stabilization of large-scale and complex photonic integrated circuits (PICs). Traditional on-chip optical monitoring is implemented by tapping a small portion of optical power from the waveguide, which leads to significant loss. Due to its advantages like non-invasive nature, miniaturization, and complementary metal-oxide-semiconductor (CMOS) process compatibility, a transparent monitor named the contactless integrated photonic probe (CLIPP), has been attracting great attention in recent years. The CLIPP indirectly monitors the optical power in the waveguide by detecting the conductance variation of the local optical waveguide caused by the surface state absorption (SSA) effect. In this review, we first introduce the fundamentals of the CLIPP including the concept, the equivalent electric model and the impedance read-out method, and then summarize some characteristics of the CLIPP. Finally, the functional applications of the CLIPP on the identification and feedback control of optical signal are discussed, followed by a brief outlook on the prospects of the CLIPP. Graphical Abstract

Unlike ballistic movements such as reaching, little is known about how aging affects tracking performance. The current study initially aimed at confirming the differences in tracking performance between younger and older people. The experiment included 14 young and 10 older subjects who were instructed to perform a circular tracking task on the fronto-parallel and sagittal planes with a visible target in the first half interval (called the feedback (FB) interval) and an invisible target disappearing and reappearing in the remaining interval (called the feedforward (FF) interval). The results demonstrated that the aging effect was sufficient to deteriorate tracking performance, regardless of the environment. However, the older group’s performance was not significantly influenced by the depth movement, whereas the younger group’s performance significantly deteriorated during the FB interval. Finally, the differences in errors between the FF and FB intervals for each movement plane were investigated, demonstrating the vulnerability of the internal model used for each plane to the visibility of the target. The younger group demonstrated a significant difference between the fronto-parallel and sagittal planes; however, the older group exhibited no significant difference, indicating that the younger group acquired an internal model sensitive to visual information. We confirmed aging-induced tracking performance degradation and highlighted the impact of aging on tracking performance, resulting in an increased understanding of cerebellar motor control rather than conventional ballistic movements.

The goal of this article considers a class of semilinear fractional evolution hemivariational inequalities control systems described by the Riemann-Liouville fractional derivative in separable reflexive Banach spaces. At first, the existence of Riemann-Liouville fractional semilinear evolution hemivariational inequalities with nonconvex feedback control constraint is discussed by applying fractional operator theory and Schauder's fixed point theorem. Then, we show that the set of the original system's trajectories can be approximated by the set of the upper semicontinuous convex valued regularization of the original feedback system's trajectories under certain hypotheses. Also, an example is given to illustrate our main results at the end of the paper. [ABSTRACT FROM AUTHOR]

Essential tremor (ET), amovement disorder characterized by involuntary oscillations of the limbs during movement, remains to date not well understood. It has been recently suggested that the tremor originates from impaired delay compensation, affecting movement representation and online control. Here we tested this hypothesis directly with 24 ET patients (14 female; 10 male) and 28 neurologically intact (NI) human volunteers (17 female; 11 male) in an upper limb postural perturbation task. After maintaining their hand in a visual target, participants experienced perturbations of unpredictable direction and magnitude and were instructed to counter the perturbation and steer their hand back to the starting position. In comparison with NI volunteers, ET patients' early muscular responses (short and long-latency responses, 20-50 and 50-100 ms, respectively) were preserved or even slightly increased. However, they exhibited perturbation-dependent deficits when stopping and stabilizing their hand in the final target supporting the hypothesis that the tremor was generated by the feedback controller. We show in a computational model that errors in delay compensation accumulating over time produced the same small increase in initial feedback response followed by oscillations that scaled with the perturbation magnitude as observed in ET population. Our experimental results therefore validate the computational hypothesis that inaccurate delay compensation in long-latency pathways could be the origin of the tremor. [ABSTRACT FROM AUTHOR]

Real‐world traffic flow is influenced by various random factors. Gaussian white noise is employed to describe the inherent randomness of traffic flow. A stochastic optimization velocity model is established for dynamic analysis. To enhance the traffic flow stability, the velocity difference of two successive vehicles ahead is considered. The velocity difference feedback control strategy is proposed, utilizing the velocity differences between the vehicle and the two targeted vehicles as control signals. Stability conditions for both the stochastic optimization velocity model and the feedback control model are derived by using the moment stability theory. The study demonstrates that the random factors can destabilize traffic flow, while the proposed feedback control strategy effectively stabilizes traffic flow. Additionally, the influences of random factors on the stability of traffic flow are discussed extensively, aiding in understanding actual traffic congestion mechanisms and offering insights for traffic control strategies. [ABSTRACT FROM AUTHOR]

Tactile servoing is an important technique because it enables robots to manipulate objects with precision and accuracy while adapting to changes in their environments in real-time. One approach for tactile servo control with high-resolution soft tactile sensors is to estimate the contact pose relative to an object surface using a convolutional neural network (CNN) for use as a feedback signal. In this paper, we investigate how the surface pose estimation model can be extended to include shear, and utilise these combined pose-and-shear models to develop a tactile robotic system that can be programmed for diverse non-prehensile manipulation tasks, such as object tracking, surface-following, single-arm object pushing and dual-arm object pushing. In doing this, two technical challenges had to be overcome. Firstly, the use of tactile data that includes shear-induced slippage can lead to error-prone estimates unsuitable for accurate control, and so we modified the CNN into a Gaussian-density neural network and used a discriminative Bayesian filter to improve the predictions with a state dynamics model that utilises the robot kinematics. Secondly, to achieve smooth robot motion in 3D space while interacting with objects, we used SE (3) velocity-based servo control, which required re-deriving the Bayesian filter update equations using Lie group theory, as many standard assumptions do not hold for state variables defined on non-Euclidean manifolds. In future, we believe that pose-and-shear-based tactile servoing will enable many object manipulation tasks and the fully-dexterous utilisation of multi-fingered tactile robot hands. [ABSTRACT FROM AUTHOR]

The self-sensing technology of microactuators utilizes a smart material to concurrently actuate and sense in a closed-loop control system. This work aimed to develop a position feedback-control system of nickel electrothermal microactuators using a resistivity self-sensing technique. The system utilizes the change in heating/sensing elements' resistance, due to the Joule heat, as the control parameter. Using this technique, the heating/sensing elements would concurrently sense and actuate in a closed loop control making the structures of microactuators simple. From a series of experiments, the proposed self-sensing feedback control system was successfully demonstrated. The tip's displacement error was smaller than 3 µm out of the displacement span of 60 µm. In addition, the system was less sensitive to the abrupt temperature change in surroundings as it was able to displace the microactuator's tip back to the desired position within 5 s, which was much faster than a feed-forward control system. [ABSTRACT FROM AUTHOR]

In this paper, the stochastic Takagi–Sugeno fuzzy recurrent neural networks (STSFRNNS) with distributed delay is established based on the Takagi–Sugeno (TS) model and the fixed time synchronization problem is investigated. In order to synchronize the networks, we design two kinds of controllers: a feedback controller and an adaptive controller. Then, we obtain the synchronization criteria in a fixed time by combining the Lyapunov method and the related inequality theory of the stochastic differential equation and calculate the stabilization time for the STSFRNNS. In addition, to verify the authenticity of the theoretical results, we use MATLABR2023A to carry out numerical simulation. [ABSTRACT FROM AUTHOR]

Methods from controlled Lagrangians, double-bracket dissipation and interconnection and damping assignment–passivity-based control (IDA-PBC) are used to construct nonlinear feedback controls which (asymptotically) stabilize previously unstable equilibria of Lie–Poisson Hamiltonian systems. The results are applied to find an asymptotically stabilizing control for the rotor driven satellite, and a stabilizing control for Hall magnetohydrodynamic flow. [ABSTRACT FROM AUTHOR]

Gas lift is one of the most common artificial lift methods used to enhance oil well production. Oscillations in production flow rate, known as casing heading instability, are a common issue in gas lift systems. Designing an automatic control system is a recommended solution to prevent casing heading and improve the performance of a gas lift system. One of the major challenges in designing such a control system is the uncertainty of the system parameters and state variables. In this paper, the analytical gas lift model presented in previous studies was modified to account for the real gas flow in the annulus. Then, a parameter estimator and a state observer were designed using a square root Kalman filter to estimate the uncertain parameter and state variables. The simulation results have shown that the improved model is more reliable. The designed Linear Quadratic Gaussian (LQG) control, along with the estimator and observer, can stabilize the gas lift system under uncertain conditions. The main contribution of this work is the development of an integrated system for controlling and estimating the gas lift system. This system ensures reliable performance even in the presence of changes in wellbore and reservoir conditions. [ABSTRACT FROM AUTHOR]

We demonstrate proprioceptive feedback control of a one degree of freedom soft, pneumatically actuated origami robot and an assembly of two robots into a two degree of freedom system. The base unit of the robot is a 41 mm long, 3-D printed Kresling-inspired structure with six sets of sidewall folds and one degree of freedom. Pneumatic actuation, provided by negative fluidic pressure, causes the robot to contract. Capacitive sensors patterned onto the robot provide position estimation and serve as input to a feedback controller. Using a finite element approach, the electrode shapes are optimized for sensitivity at larger (more obtuse) fold angles to improve control across the actuation range. We demonstrate stable position control through discrete-time proportional-integral-derivative (PID) control on a single unit Kresling robot via a series of static set points to 17 mm, dynamic set point stepping, and sinusoidal signal following, with error under 3 mm up to 10 mm contraction. We also demonstrate a two-unit Kresling robot with two degree of freedom extension and rotation control, which has error of 1.7 mm and 6.1°. This work contributes optimized capacitive electrode design and the demonstration of closed-loop feedback position control without visual tracking as an input. This approach to capacitance sensing and modeling constitutes a major step towards proprioceptive state estimation and feedback control in soft origami robotics. [ABSTRACT FROM AUTHOR]

Melt electrowriting (MEW) is a solvent‐free (i.e., no volatile chemicals), a high‐resolution three‐dimensional (3D) printing method that enables the fabrication of semi‐flexible structures with rigid polymers. Despite its advantages, the MEW process is sensitive to changes in printing parameters (e.g., voltage, printing pressure, and temperature), which can cause fluid column breakage, jet lag, and/or fiber pulsing, ultimately deteriorating the resolution and printing quality. In spite of the commonly used error‐and‐trial method to determine the most suitable parameters, here, we present a machine learning (ML)‐enabled image analysis‐based method for determining the optimum MEW printing parameters through an easy‐to‐use graphical user interface (GUI). We trained five different ML algorithms using 168 MEW 3D print samples, among which the Gaussian process regression ML model yielded 93% accuracy of the variability in the dependent variable, 0.12329 on root mean square error for the validation set and 0.015201 mean square error in predicting line thickness. Integration of ML with a control feedback loop and MEW can reduce the error‐and‐trial steps prior to the 3D printing process, decreasing the printing time (i.e., increasing the overall throughput of MEW) and material waste (i.e., improving the cost‐effectiveness of MEW). Moreover, embedding a trained ML model with the feedback control system in a GUI facilitates a more straightforward use of ML‐based optimization techniques in the industrial section (i.e., for users with no ML skills). [ABSTRACT FROM AUTHOR]

This paper presents a single-lever control method based on Power Management System (PMS) and improved Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm for turboprop engines. In this approach, power level angle command, which is the single-lever command, is decoupled into controlled variable commands by PMS, and the controller based on improved TD3 algorithm can ensure that controlled variables track their commands rapidly and accurately. To achieve the optimal conversion relationship between different commands, an offline optimization process is used to design PMS. By optimization, specific fuel consumption and propeller efficiency are both improved after conversion. To deal with strong interactions between different control loops of a turboprop engine, TD3 algorithm which is a deep reinforcement learning algorithm is adopted. Two improvements which are the design method of observation state and prioritized experience replay are made to enhance the tracking accuracy. Simulation results show that improved TD3 algorithm can learn an optimal control policy to guarantee good control effect with fast response and small overshoot. The maximum settling time is less than 0.25s and the maximum overshoot is less than 0.1%. It also has a good robustness performance when the plant exists model uncertainties. The maximum fluctuations are less than 0.05%. [ABSTRACT FROM AUTHOR]

A nonlinear system of ordinary differential equations with control parameters is considered. Pointwise restrictions are imposed on the possible values of these parameters. It is required to solve the problem of transferring the trajectory of the system from an arbitrary initial position to the smallest possible neighborhood of a given target set on a fixed time interval by selecting an appropriate feedback control. To solve this problem, it is proposed to construct a continuous piecewise cubic function of a special kind. The level sets of this function correspond to internal estimates of the solvability sets of the system. Using this function, it is also possible to construct a feedback control function that solves the target control problem on a fixed time interval. The paper proposes formulas for calculating the values of the piecewise cubic function, examines its properties, and considers an algorithm for searching for parameters defining this function. [ABSTRACT FROM AUTHOR]