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This paper presents a quasi-static model-based control algorithm for controlling the motion of a soft robotic exo-digit with three independent actuation joints physically interacting with the human finger. A quasi-static analytical model of physical interaction between the soft exo-digit and a human finger model was developed. Then, the model was presented as a nonlinear discrete-time multiple-input multiple-output (MIMO) state-space representation for the control system design. Input–output feedback linearization was utilized and a control input was designed to linearize the input–output, where the input is the actuation pressure of an individual soft actuator, and the output is the pose of the human fingertip. The asymptotic stability of the nonlinear discrete-time system for trajectory tracking control is discussed. A soft robotic exoskeleton digit (exo-digit) and a 3D-printed human-finger model integrated with IMU sensors were used for the experimental test setup. An Arduino-based electro-pneumatic control hardware was developed to control the actuation pressure of the soft exo-digit. The effectiveness of the controller was examined through simulation studies and experimental testing for following different pose trajectories corresponding to the human finger pose during the activities of daily living. The model-based controller was able to follow the desired trajectories with a very low average root-mean-square error of 2.27 mm in the x-direction, 2.75 mm in the y-direction, and 3.90 degrees in the orientation of the human finger distal link about the z-axis.

In this study, we investigate a stochastic predator-prey model with anti-predator behavior. We first analyze the noise-induced transition from a coexistence state to the prey-only equilibrium by using the stochastic sensitive function technique. The critical noise intensity for the occurrence of state switching is estimated by constructing confidence ellipses and confidence bands, respectively, for the coexistence the equilibrium and limit cycle. We then study how to suppress the noise-induced transition by using two different feedback control methods to stabilize the biomass at the attraction region of the coexistence equilibrium and the coexistence limit cycle, respectively. Our research indicates that compared with the prey population, the predators appear more vulnerable and prone to extinction in the presence of environmental noise, but it can be prevented by taking some appropriate feedback control strategies.

In this paper, a stochastic turbidostat model with controllable output is established by using piecewise constant delayed measurements of the substrate concentration. We commence by proving the existence and uniqueness of the global positive solution of the stochastic delayed model. Then, sufficient conditions of extinction and stochastic strong permanence of the biomass are acquired. In quick succession, we investigate the stochastic asymptotical stability of the washout equilibrium as well as the asymptotic behavior of the random paths approaching the interior equilibrium of its corresponding deterministic model by employing the method of Lyapunov functionals. Numerical and theoretical findings show that the influence of environmental random fluctuations on the dynamics of the model may be more pronounced than that of time delay.

Virtual Power Plants (VPPs) have emerged as a modern real-time energy management architecture that seeks to synergistically coordinate an aggregation of renewable and non-renewable generation systems to overcome some of the fundamental limitations of traditional power grids dominated by synchronous machines. In this survey paper, we review the different existing and emerging feedback control mechanisms and architectures used for the real-time operation of VPPs. In contrast to other works that have mostly focused on the optimal dispatch and economical aspects of VPPs in the hourly and daily time scales, in this paper we focus on the dynamic nature of the system during the faster sub-hourly time scales. The virtual (i.e., software-based) component of a VPP, combined with the power plant (i.e., physics-based) components of the power grid, make VPPs prominent examples of cyber-physical systems, where both continuous-time and discrete-time dynamics play critical roles in the stability and transient properties of the system. We elaborate on this interpretation of VPPs as hybrid dynamical systems, and we further discuss open research problems and potential research directions in feedback control systems that could contribute to the safe development and deployment of autonomous VPPs.

This paper is concerned with the energy-to-peak (E2P) feedback control for discrete-time switched Takagi-Sugeno fuzzy (STSF) singular systems with input quantized. The main challenge of this work comes from the difficulty of solving the semi-positive problem on the main diagonal in the E2P feedback control design. In order to further study the E2P control problem in the presence of dynamic quantization strategy and fuzzy switching mechanism, a new method is proposed to ensure the E2P performance index and stochastic stability of closed-loop singular (CLS) systems. Then, the design conditions of model-dependent proportional plus derivative (MDPPD) state feedback controllers and mode-dependent (MD) dynamic quantizers for STSF singular systems are obtained in the form of linear matrix inequalities. Finally, a simulation example is given to illustrate the effectiveness of the proposed control design method.

Intravenous (IV) therapy provides a rapid therapeutic effect. However, errors in infusion rate can lead to over-dosing and under-dosing, potentially causing severe side effects for patients. To prevent incidents, medical staff visually monitors medication injection information (MIF) such as current flow rates (CFR) and injection volume. In this study, we propose an internet of things (IoT) system that automatically controls the CFR and remotely monitors the MIF. First, a peristaltic pump is designed to infuse parenteral fluids based on the “drop-by-drop” phenomenon. Second, a computer vision-based on deep learning algorithm provides real-time video and counts the fluid dropping to derive the MIF. Finally, the CFR is automatically applied as a feedback signal to regulate the infusion cycle of the peristaltic pump. After embedding all systems in our prototype, we evaluate the system performance according to IV therapy protocols used in clinical practice. In our experiments, the mean accuracy of the fluid injection using the peristaltic pump was 99.23% and the dropping count was the highest at 98.25%. Furthermore, the average accuracy of the CFR using the feedback system was 99.3%. Based on our results, we confirmed that automated control of infusion rate is possible in IV therapy and computer vision-based on deep learning can be utilized for feedback sensors and monitoring system. Therefore, we believe that our method is the practical solution that can increase patient safety and work efficiency of clinical staffs.

This paper considers design of controllers for highly resonant systems. Resonant systems are generally modeled as lightly damped linear systems. The purpose of control design is to damp the resonances. The plant and controller are written in a second order form, and their states are concatenated to form a closed loop. The controller parameters are determined by matching the eigen-values and eigen-vectors of the closed loop with that of a desired plant. This matching, referred to as Quadratic Eigen structure Assignment, leads to a set of linear equations. These equations may or may not have a solution. A least squares approximation is an alternative when no solution exists. However, least squares could lead to an unstable closed loop. Stability constraints are derived and enforcing the constraints while solving the least squares renders a stable closed loop as well as a stable controller. The constraints derived (Linear Matrix Inequalities) and the cost function are Convex. Hence, have a unique solution and are numerically tractable.

The ball and beam (BnB) system serves as a benchmark in control engineering as it provides a foundational concept applicable to addressing stabilization challenges of various underactuated nonlinear systems. This includes tasks like maintaining the balance of goods carried by mobile robots and controlling the attitude of unmanned aerial vehicles. In this study, the focus is on enhancing control optimization strategies for BnB systems that take into account inherent nonlinearities arising from actuator constraints and state measurements. The work introduces a novel intelligent control approach, termed hybrid PSO-WOA, which combines Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) to automate optimal parameter search for proportional-integral-derivative (PID) and state feedback (SF) controllers. The collaborative technique between PSO and WOA is formulated to strike a balance between exploration and exploitation phases, and to mitigate premature convergence risks due to the system’s complexities. Additionally, three control schemes, namely cascade PID-PID, cascade PID-SF, and cascade PID-observer are introduced, each with tailored cost functions for optimization through the hybrid PSO-WOA algorithm, accommodating both measurable and unmeasurable state scenarios. Simulation results consistently demonstrate the superior performance of the hybrid approach compared to individual PSO and WOA methods, as well as conventional PID and linear quadratic regulator approaches. Notably, the hybrid approach exhibits a significant improvement in error metrics, reducing integral-time absolute error by 18.99%, integral squared error by 35.37%, and steady-state error by 92.86%. This substantial enhancement suggests promising directions for future research in automated control parameter tuning for underactuated nonlinear systems.

Europe needs to save energy, and lowered indoor temperature is frequently promoted as part of the solution. To facilitate this, heating control systems with feedback from indoor temperature sensors are often required to avoid thermal discomfort and achieve long-term temperature reductions. This article describes a measurement- and interview-based study on feedback control where 107 Swedish multifamily buildings were analysed. The obtained results show that buildings with lowered indoor temperatures had reduced annual heating demand by 4 kWh/m2 and a reduced indoor temperature of 0.4 °C. There were, however, significant individual differences and even buildings with increased indoor temperatures, which harmed the energy savings. Temperature fluctuation was most often significantly reduced, but the impact on heating power demand during cold weather was, on average, only 2%. An interview with different actors indicated higher energy savings, possibly due to their stock's original room temperature levels. Several interviewees also mentioned other advantages of temperature mapping. Most of the results obtained in this study were in line with several previous investigations. The study's novelty lies in the large number of investigated buildings with mature commercial heat control technology, including PI-control for adjusting supply temperature, indoor temperature sensors in almost every apartment and a parallel analysis of additional affected parameters. [ABSTRACT FROM AUTHOR]

This paper introduces robust nonlinear controller strategies for multiphase induction machines, aiming to enhance operational reliability under healthy and faulty conditions, including stator phase and converter leg openings. Due to the induction machine's inherent nonlinearities and parameter variations, a robust control is required. The study evaluates the effectiveness of the sliding mode control with linear feedback and switched gains, the fuzzy proportional integral control, and their combined application in both healthy and faulty modes. The experimental assessment involves a symmetrical six-phase induction machine in generation mode, with comparisons with a classic proportional integral controller for inner current loop regulations. Experimental results show that the fuzzy proportional integral controller presents the best performance by minimizing torque ripples during both healthy and faulty operations. [ABSTRACT FROM AUTHOR]

The feedback control system was designed to control the pipeline blockage and leakage fault. Based on the open-loop engine system, different degrees of faults were simulated, and the changes in system parameters when faults occur were analyzed. Then, the faults were injected into the engine system with feedback control, and the effects of the controller to different degrees of faults and the changes of the parameters of the electric pump with the controller were studied. The simulation results showed that under the action of the feedback control system, the deviation of the engine system parameters caused by these faults can recover to the set value within a few seconds. When the fault disappears, the system parameters can be still within the normal operating range. [ABSTRACT FROM AUTHOR]

A fuzzy PI controller was utilized to realize the modal matching between a driving and detecting model. A simulation model was built to study electrostatic decoupling controlling technology. The simulation results show that the modal matching can be gained by the fuzzy PI controller. The frequency difference between the driving mode and the detection mode is less than 1 Hz, and the offset of the input DC is smaller than 0.6 V. The optimal proportionality factor and integral coefficient are 1.5 and 20, respectively. The fuzzy PI controlling technology provides a good way for the parameter optimization to gain modal matching of micro gyro, via which the detecting accuracy and stability can be improved greatly. [ABSTRACT FROM AUTHOR]

This paper investigates a discrete-time predator–prey model with predator harvesting. The stability analysis for different fixed points of the discretized model is shown briefly. In this study, different types of bifurcation and their normal forms are determined. As the prey harvesting and conversion rate of prey into predator are ecologically significant, the impact of theirs have been studied by choosing the bifurcation parameter. The system exhibits a sequence of bifurcations of codim-1 viz. Neimark–Sacker bifurcation and flip bifurcation (period-doubling) and codim-2 resonance bifurcation (1:2, 1:3 and 1:4) at a positive fixed point. For each bifurcation, by using the critical normal form coefficient method, various critical states are calculated under non-degeneracy conditions. Further, a detailed numerical simulation is presented for supporting the analytical findings. The bifurcation curves, phase plots and Maximum Lyapunov exponent (MLE) are drawn. The system exhibits a wide range of bifurcation, including periodic orbits, quasi-periodicity, resonance bifurcation and chaos. Moreover, it is shown that predator harvesting has a stabilizing effect on the dynamics of the model. The chaos that occurred in the system is reduced beyond the critical value of harvesting. The predator population goes extinct after crossing the threshold value of harvesting. This work reflects that the feasible upper bound of the harvesting rate for the species coexistence can be guaranteed. Further, a state feedback controller is employed to suppress the dense chaos in the discrete system. The control technique applied for codim-1 Neimark–Sacker bifurcation and codim-2 1:4 resonance bifurcation is instrumental to reduce the complexity in the system. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhejiang University: Science A is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article investigates the finite-time decentralized event-triggered feedback control problem for generalized neural networks (GNNs) with mixed interval time-varying delays and cyberattacks. A decentralized event-triggered method reduces the network transmission load and decides whether sensor measurements should be sent out. The cyber-attacks that occur at random are described employing Bernoulli distributed variables. By the Lyapunov-Krasovskii stability theory, we apply an integral inequality with an exponential function to estimate the derivative of the Lyapunov-Krasovskii functionals (LKFs). We present new sufficient conditions in the form of linear matrix inequalities. The main objective of this research is to investigate the stochastic finite-time boundedness of GNNs with mixed interval time-varying delays and cyber-attacks by providing a decentralized event-triggered method and feedback controller. Finally, a numerical example is constructed to demonstrate the effectiveness and advantages of the provided control scheme. [ABSTRACT FROM AUTHOR]

In manufacturing systems, many quality measurements are in the form of images, including overlay measurements in the semiconductor manufacturing and dimensional deformation profiles of fuselages in an aircraft assembly process. To reduce the process variability and ensure on-target quality, process control strategies should be deployed, in which the high-dimensional image output is controlled by one or more input variables. To design an effective control strategy, the process model should be first estimated via relationship exploration between the image output and inputs, off-line. Next, the control law is formulated by minimizing the control objective function online. The main challenges of achieving such a control strategy include (i) the high dimensional output of a regression model, (ii) the integrated analysis of both the spatial structure of image outputs and the temporal structure of the image sequence, and (iii) non-iid noises. To address these challenges, we propose a novel tensor-based process control approach by incorporating the tensor time series and regression techniques. Based on the process model, we can then obtain the control law by minimizing a control objective function. Although our proposed approach is motivated by a 2D image case, it can be extended to higher-order tensors such as point clouds. Simulation and case studies show that our proposed method is more effective than benchmarks in terms of relative mean square error. [ABSTRACT FROM AUTHOR]

The fixed-time synchronization problems are solved for fractional-order memristive neural networks. According to the voltage and current characteristics of memristors, the model of fractional-order memristive neural networks with time-varying delays is established. Different from the traditional calculation method of memristive synaptic weights based on the maximum absolute value, by introducing some transformations, using differential inclusion theory and set-valued maps, the fractional-order memristive neural networks are transformed into a type of fractional-order systems with uncertain parameters in the framework of Filippov solution. Based on the fixed-time stability theory and the theory of measurable selection, the sufficient conditions of fixed-time synchronization are given by constructing Lyapunov function and using inequality techniques, and the calculation formula of the upper bound for the synchronization time is given. By designing an appropriate state feedback controller, the master-slave systems can reach fixed-time synchronization, and the upper bound for the synchronization time is independent of the initial state for the systems. The simulation example shows that the designed controller makes the systems achieve synchronization quickly. [ABSTRACT FROM AUTHOR]

This article proposes the design of a sliding mode controller with a time-varying sliding surface for the plate heat exchanger. A time-varying sliding mode controller (TVSMC) combines the benefit of the control system's robustness and convergence rate. Using Lyapunov stability theory, the stability of the designed controller is proved. In addition, the controller parameters of the designed controller are specified optimally via the dragonfly algorithm (DA). The input constraint's effect is considered in the controller design process by applying the concept of the auxiliary system. The bounded disturbances are applied to investigate the robustness of the proposed techniques. Moreover, the quasi-sliding mode controller (QSMC) is developed as a benchmark to evaluate the convergence behavior of the proposed TVSMC technique. The simulation results demonstrate the proposed TVSMC with the optimal parameters provided by the DA algorithm (TVSMC+DA) can regulate the temperature to the desired level under bounded disturbances. When compared to the QSMC method, the TVSMC+DA performs significantly faster convergence speed and greater reduction in chattering occurrence. The results clearly indicate that the proposed controller can enhance convergence properties while being robust to disturbances. [ABSTRACT FROM AUTHOR]

This paper uses the stationary distribution of Markov chains to obtain new criteria for the almost sure exponential stability of nonlinear hybrid stochastic functional differential equations with Lévy noise (HSFDEs-LN) under weaker conditions and an upper bound τ ∗ of time delay is obtained. That is, if corresponding HSDEs-LN is almost surely exponentially stable, then the HSFDEs-LN is also almost surely exponentially stable as long as time delay τ < τ ∗ . Furthermore, the new theory enabled us to design discrete-time state observations feedback control by using Lévy noise as the control source to stabilize a class of unstable hybrid differential equations. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The continuous development of metal additive manufacturing (AM) promises the flexible and customized production, spurring AM research towards end-use part fabrication rather than prototyping, but inability to well control process defects and variability has precluded the widespread applications of AM. To solve these issues, process monitoring and control is a powerful approach. Recently, a variety of monitoring methods have been proposed and integrated with metal AM machines, which enables a large volume of data to be collected during the process. However, the data analytics faces great challenges due to the complexity of the process, bringing difficulties on developing effective models for defects detection as well as feedback control to improve quality. To overcome these challenges, machine learning methods have been frequently employed in the model development. By using machine learning methods, the models can be built based on the collected dataset, while it is not necessary to fully understand the process. This paper reviews the applications of machine learning methods in metal powder-bed fusion process monitoring and control, illuminates the challenges to be solved, and outlooks possible solutions. [ABSTRACT FROM AUTHOR]

Remotely operated underwater vehicles are usually equipped with four horizontal thrusters that form an X-shaped actuation configuration. Yet, thruster's inherent dead-zone may possibly result in strong chatter of moment inputs and motion tracking of underwater vehicles. This paper proposes a two-layer cascade tracking controller together with a dead-zone compensator, in order to achieve simplified and effective surge-heading control of underwater vehicles equipped with an X-shaped horizontal actuation configuration. For the sake of brevity, the surge and heading dynamics are firstly unified as a second-order dynamic system where the known and unknown parts are separated, respectively. Based on this model, a feedback linearization control law with a combined error measure is designed in the first-layer cascade system for the simplified dynamics tracking. Then, a reduced-order extended state observer without using any priori knowledge of uncertainties is utilized in the second-layer cascade system to estimate the complex uncertainty of the dynamics. It is noted that this two-layer tracking controller has only two gains to be adjusted, ensuring a simple calculation and microprogramming. Subsequently, a dedicated dead-zone compensator is proposed for the X-shaped actuation configuration and the input-to-state stability of the whole tracking system is analyzed. Finally, comparative numerical cases are provided to demonstrate the adaptivity and robustness of the designed surge-heading tracking controller, i.e., up to 56% reduction of the maximum surge tracking error owing to this dead-zone compensator and less than 0.03 ∘ of the heading steady state error against different initial states. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with rapid stabilization of Timoshenko beam system with the internal delay control. The main idea of solving the stabilization problem is transformation. The original time delay system is firstly transformed into the undelayed system, and then the feedback control law which can stabilize the undelayed system is found. Finally, we prove that the feedback control law can also exponentially stabilize the time delay system. [ABSTRACT FROM AUTHOR]

In many studies aimed at improving the quality of service (QoS) in wireless local area networks (WLAN), the performance was improved by applying non-ACK model, physical interference model, analytical models etc. with using optimization algorithms with stable simulations. For efficient QoS in WLANs, performance is generally increased using 802.11n medium access control protocol through request to send threshold value (RSTV), fragmentation threshold value (FTV) and buffer size (BS) parameters. While the RSTV activates the request to send/clear to send (RTS/CTS) mechanism in carrier sense multiple access with collision avoidance (CSMA/CA) protocol, FTV fragments the larger packets during CSMA/CA transmission, reducing the packet loss in WLANs. BS, however, is the memory used in the CSMA/CA model to reduce cost. In previous studies, most of the new model applied in process layers by using Riverbed Modeler simulation tool and ideal input values had been obtained for RSTV, FTV and BS during the simulation stable. In this study, a new model proposed for CSMA/CA protocol on the process layers which used feedback controlled method with fuzzy logic to improve QoS during the simulation. Values obtained with nearly six million different test results, has been revealed that throughput was increased by 26.48%, the channel utilization by 2.30%, the data traffic received by 14.59% and the data traffic sent by 17.06% respectively. While RSTV, FTV and BS input parameters are optimized with the help of the feedback controlled algorithm used in these studies, the effects of external parameters such as number of nodes, inter-arrival time, transmitting power etc. on the performance improvements in the model are demonstrated in graphics. All these test results have shown that the new model provides a high rate of performance improvement for WLANs. [ABSTRACT FROM AUTHOR]

Unlike regular linear quadratic regulator (LQR) problems, singular LQR problems, in general, cannot be solved using a static state-feedback controller. This work is primarily focused on the design of feedback controllers which solve the singular LQR problem. We show that such problems can be solved using proportional-derivative (PD) state-feedback controllers. It is well known in the literature that the maximal rank-minimizing solution of the singular LQR linear matrix inequality (LMI) is pivotal in solving the singular LQR problem. In this paper, we first make use of this maximal rank-minimizing solution to compute the optimal trajectories. Then we provide a PD feedback controller that restricts the trajectories of the closed-loop system to these optimal ones and thus solves the singular LQR problem. While numerous solutions to this problem have been proposed over the course of the extensive research efforts in this field, a controller in the form of a PD state feedback has been long sought after. Our approach is based on the notion of weakly unobservable (slow) and strongly reachable (fast) subspaces developed in [M. Hautus and L. Silverman, Linear Algebra Appl., 50 (1983), pp. 369--402]. But, unlike in that reference, we employ these notions to the corresponding Hamiltonian system and not to the plant. This crucial extension of these well-known subspaces to the corresponding Hamiltonian system is key to the optimal PD feedback design that we propose in this paper. It is well known that an optimal state feedback for the singular LQR problem does not exist; the limiting state-feedback controller of the suboptimal ones (high gain controllers) has unbounded coefficients as optimality is approached. We show in this paper that the limiting high gain controller is in fact a PD controller. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with the minimization problem of an integral functional with integrand that is not convex in the control on solutions of a Riemann-Liouville fractional differential control system with mixed nonconvex feedback constraints on the control. At First, the existence results for Riemann-Liouville fractional semilinear differential control systems are discussed by using Schauder's fixed point theorem. Under some reasonable assumptions, we prove that the relaxation problem has an optimal solution, and that for each optimal solution there has a minimizing sequence of the original problem that converges to the optimal solution with respect to the trajectory, the control, and the functional in appropriate topologies simultaneously. Finally, we give an example to illustrate our main results. [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.

There is an unbalanced problem in the traditional laneway excavation process for coal mining because the laneway excavation and support are at the same position in space but they are separated in time, consequently leading to problems of low efficiency in laneway excavation. To overcome these problems, an advanced dual-arm tunneling robotic system for a coal mine is developed that can achieve the synchronous operation of excavation and the permanent support of laneways to efficiently complete excavation tasks for large-sized cross-section laneways. A dual-arm cutting robot (DACR) has an important influence on the forming quality and excavation efficiency of large-sized cross-section laneways. As a result, the relative kinematics, workspace, and control of dual-arm cutting robots are investigated in this research. First, a relative kinematic model of the DACR is established, and a closed-loop control strategy for the robot is proposed based on the relative kinematics. Second, an associated workspace (AW) for the DACR is presented and generated, which can provide a reference for the cutting trajectory planning of a DACR. Finally, the relative kinematics, closed-loop kinematic controller, and associated workspace generation algorithm are verified through simulation results.

Traffic during peak hours is getting worse over time and the duration of the peak is increasing in most metropolitan areas as more drivers try to use limited roadway capacity. Bottlenecks caused by traffic incidents or road construction limit roadway capacity even further and can cause traffic “shock waves.” When an incident causes a highway lane to close unexpectedly, vehicles are forced to change lanes close to the incident and at low speeds. These forced lane changes interfere with traffic flow in open lanes and decrease the overall flow of the roadway. Heavy-duty trucks can exacerbate congestion because they are larger and slower than passenger vehicles. Advanced technologies may help to improve traffic flow in these situations. Variable speed limits can change based on road, traffic, and weather conditions. Speed limits can be reduced in real time when congestion is imminent to smooth traffic flow and handle more traffic volume at a slower, but not stop-and-go, speed. Lane change control systems provide lane change recommendations well upstream of blocked lanes, spreading lane changes over a greater distance and minimizing bottlenecks that disrupt traffic flow.This policy brief summarizes findings from researchers at the University of Southern California who simulated traffic patterns along a section of Interstate 710 near the Ports of Long Beach/Los Angeles, a congested area that gets substantial truck traffic. They simulated the use of variable speed limit and lane change control systems to evaluate the potential traffic impacts of these systems.This brief is based on research from two NCST projects: Eco-Friendly Intelligent Transportation System Technology for Freight Vehicles, and Reducing Truck Emissions and Improving Truck Fuel Economy via ITS Technologies.

To investigate the time dependence of the heart rate variability (HRV) during treadmill running, a feedback control loop was implemented to eliminate the potentially confounding influence of cardiovascular drift. Without cardiovascular drift, observed changes in HRV can be directly attributed to time only and not to drift-related increases in heart rate. To quantify the time-dependence of HRV, standard HRV metrics for two consecutive windows of equal duration (12.5 min) were computed and compared. Eight participants were included. The outcome measures showed an overall tendency to decrease over time. Seven of the 10 HRV metrics were significantly lower (p

Abstract Background Restoring or improving seated stability after spinal cord injury (SCI) can improve the ability to perform activities of daily living by providing a dynamic, yet stable, base for upper extremity motion. Seated stability can be obtained with activation of the otherwise paralyzed trunk and hip musculature with neural stimulation, which has been shown to extend upper limb reach and improve seated posture. Methods We implemented a proportional, integral, derivative (PID) controller to maintain upright seated posture by simultaneously modulating both forward flexion and lateral bending with functional neuromuscular stimulation. The controller was tested with a functional reaching task meant to require trunk movements and impart internal perturbations through rapid changes in inertia due to acquiring, moving, and replacing objects with one upper extremity. Five subjects with SCI at various injury levels who had received implanted stimulators targeting their trunk and hip muscles participated in the study. Each subject was asked to move a weighted jar radially from a center home station to one of three target stations. The task was performed with the controller active, inactive, or with a constant low level of neural stimulation. Trunk pitch (flexion) and roll (lateral bending) angles were measured with motion capture and plotted against each other to generate elliptical movement profiles for each task and condition. Postural sway was quantified by calculating the ellipse area. Additionally, the mean effective reach (distance between the shoulder and wrist) and the time required to return to an upright posture was determined during reaching movements. Results Postural sway was reduced by the controller in two of the subjects, and mean effective reach was increased in three subjects and decreased for one. Analysis of the major direction of motion showed return to upright movements were quickened by 0.17 to 0.32 s. A 15 to 25% improvement over low/no stimulation was observed for four subjects. Conclusion These results suggest that feedback control of neural stimulation is a viable way to maintain upright seated posture by facilitating trunk movements necessary to complete reaching tasks in individuals with SCI. Replication of these findings on a larger number of subjects would be necessary for generalization to the various segments of the SCI population.

By taking into account third-order dispersion effects in the presence of feedback, we investigate the stability and nonlinear dynamic movements of waves created in microcavities and fiber cavities from numerical simulations of the extended Lugiato–Lefever equation. The current analysis emphasizes that third-order scattering, feedback strength, and feedback local and global components allow for the numerical observation of dissipative bound states of solitons and chaos with extended and antisymmetric interaction domains. In this regard, we demonstrate how the symmetry of the localized structural interactions is broken by the radiation that results from third-order dispersion.