General Control Scheme Research Articles

Hybrid Position and Force Control for Upper Limb Rehabilitation of Stroke Patient

This paper presents hybrid position and force control (HPFC) methodology for position and as well as force tracking for upper limb rehabilitation of stroke patients. Stroke is a leading cause of disability in humans. Traditional rehabilitative therapies help regain motor function and ameliorate impairment, but they depend on the therapist’s experience and require many therapists, which is cost-prohibitive. Most robotic tasks with high severity such as rehabilitation, demands an effective force as well as position control scheme to ensure the safe physical contact between the robot and its environment in this case the patient. Some rehabilitation robots have been developed to help stroke survivors recover motor function. In past robots used for upper-limb rehabilitation employed general control schemes such as proportional integral derivative. To overcome the problem of force tracking in rehabilitation, robots require modern control techniques. To mimic the human upper-limb, and universality of application, an end-effector based robot is used for this study. To ensure the convergence of position and force errors to zero extensive simulations are performed. Two of assistant modes passive and active assistive rehabilitation are considered. Most common rehabilitation trajectories horizontal reaching, and vertical reaching are selected as robot’s motion for both passive and active assistive activities. The mathematical model of robots’ kinematics, dynamics, alongside the proposed control scheme has been discussed in detail.

A high-performance general computer cursor control scheme based on a hybrid BCI combining motor imagery and eye-tracking

This study introduces a novel virtual cursor control system designed to empower individuals with neuromuscular disabilities in the digital world. By combining eye-tracking with motor imagery (MI) in a hybrid brain-computer interface (BCI), the system enhances cursor control accuracy and simplicity. Real-time classification accuracy reaches 87.92% (peak of 93.33%), with cursor stability in the gazing state at 96.1%. Integrated into common operating systems, it enables tasks like text entry, online chatting, email, web surfing, and picture dragging, with an average text input rate of 53.2 characters per minute (CPM). This technology facilitates fundamental computing tasks for patients, fostering their integration into the online community and paving the way for future developments in BCI systems.

Extension of Operating Range in Hybrid Cascaded H-Bridge Inverters with Capacitor Voltage Balancing Capability

In this article, a generalized control scheme is proposed to extend the operating range of three-phase hybrid cascaded H-bridge (HCHB) inverters into various voltage levels without necessitating alterations to the core structure or the integration of additional H-bridge submodules. This study addresses a critical challenge related to capacitor voltage drift at various modulation indices and power factors, which is a serious impediment to various applications. To overcome this challenge, a novel balancing control scheme has been developed based on the injection of two independent offset voltages to simultaneously control the DC-link and flying capacitors. A distinctive aspect of the proposed technique involves adjusting the common reference voltage to attain the nearest level in the same cluster, thereby mitigating the insufficiency of redundant switching states. The effectiveness of the proposed technique to regulate the capacitor voltages at various operating conditions has been verified through simulation and experimental results.

Inverse-Reinforcement-Learning-Based Robotic Ultrasound Active Compliance Control in Uncertain Environments

Robotic ultrasound systems (RUS) have gained increasing attention because they can automate repetitive procedures and relieve operators' workloads. However, the complexity and uncertainty of the human surface pose a challenge for stable scanning control. This paper proposes a general active compliance control strategy based on inverse reinforcement learning (IRL) to perform adaptable scanning for uncertain and unstructured environments. We analyze the manual scanning process pattern and propose a velocity-and-force-related control strategy to achieve variable force control and handle unpredictable deformation. Then, a hybrid policy optimization framework is proposed to improve transferability. In this framework, a reinforcement learning (RL) policy with a predefined reward is built to establish the relationship between contact force and posture. Furthermore, the policy is re-optimized using IRL and generated demonstrations for IRL training. The policy is trained on simple standard phantoms and further evaluated for stability and transferability in unseen and complex environments. Quantitative results show that the difference between the proposed method and the 3-D reconstructed model in terms of posture is (2.3±1.3°, 1.9±1.2°) in continuous scans. Overall, our method provides a solution for improving the usability of robotic ultrasound systems in real-world environments.

Research on Yaw Stability Control Strategy Based on Direct Slip Rate Allocation

This paper deals with the integrated control of trajectory tracking and yaw stability for autonomous vehicles. Firstly, a nonlinear vehicle dynamics model is established. The MPC algorithm was used to determine the best front wheel angle. The PID control algorithm is used to ensure the accuracy of longitudinal speed tracking. The sliding mode control algorithm is used to generate additional yaw moment and optimize the distribution of longitudinal tire force. In order to ensure the most effective distribution of the driving torque of the four wheels of the vehicle, the PID algorithm is used to track and manage the longitudinal slip rate of each tire on the bottom layer. Simulation tools such as MATLAB/Simulink and Carsim are used to verify the effectiveness of the multi-closed-loop integrated control technology. Compared with the general control strategy (no slip rate control), the trajectory error of this control algorithm is reduced by 55.6%, which indicates that it has advantages in obtaining a high tracking accuracy and ensuring the stability of autonomous vehicles.

Nanometrology based control: taming radical grafting reactions with attoliter precision.

Precisely controlled micropatterning with organic moieties is a promising route for designing smart surfaces, enabling the development of microsensors and actuators with optimal usage of reactants. Such applications require fine control over the surface modification process, which in turn demands detailed knowledge about the surface modification process. As complex surface kinetics often emerge as a result of even slight modifications of the grafting entity, non-invasive, sensitive and precise closed loop control strategies are highly desirable. In this paper we demonstrate that a nanometrology approach based on quantitative phase imaging (QPI) fulfill all these requirements. We first use the technique to monitor surface photografting kinetics of aryl radicals, comprehensively analyzing the effect of substituents on surface addition reactions. We demonstrate that several aspects of the grafting process are affected in complex ways, rendering open-loop strategies impossible to implement precisely. Then, we show that the operando optical phase signal can be used as a direct feedback, guiding the grafting reaction process. Using relatively simple instrumentation, we demonstrate that general and precise control strategies can be designed and used to control the volume of the grafting material with attoliter precision, in spite of radically different surface modification kinetics spanning several orders of magnitude.

A general visual-servoing control strategy for active non-cooperative target tracking of space manipulators

Non-cooperative target tracking plays an important role in many space missions. Recent studies on non-cooperative target tracking are mostly concentrated in the field of passive target tracking. Passive non-cooperative target tracking can effectively be used in the initial phases of a close-range rendezvous manoeuvre. Instead, during the terminal phases of such manoeuvres, active object tracking(AOT) techniques are more convenient. This study propose a general visual servo strategy for the active non-cooperative target tracking of space manipulator. Our proposed method do not need any a priori information about the targets and can be used for space manipulators of any configuration and binocular cameras of any types. Our strategy includes a real-time and accurate position estimation algorithm for non-cooperative targets and a motion planning algorithm combined Position-based visual servo(PBVS) and Image-based visual servo(IBVS). The experiment verified that the proposed strategy well solved the active non-cooperative target tracking task and can be used as a baseline for solving such tracking problems which provides a reference for subsequent studies.

Multilevel Converter Based on Series and Parallel Connections Using Floating Capacitor

The series connections of converter cells are interesting for high voltage applications, while parallel connections are for high current applications. The series/parallel is interesting for medium voltage and current applications because they present semiconductor devices with a lower rating in both voltage and current, thus, reducing the cost and size of the converters. This article investigates the configuration of a single-phase multilevel DC-AC converter based on series and parallel connections of H-bridges using floating capacitors to reduce the number of independent DC sources. The use of the floating capacitor has the limitation of working in certain operating regions. This article investigates which regions can work with floating capacitors and presents the mathematical model, general control strategy, pulse width modulation (PWM) strategies, and loss comparison with conventional series or parallel systems. Simulations and experimental results are presented to validate the control and PWM strategies used.

Self-Triggered and State-Triggered Sampling Adaptive Fuzzy Design for Full State Constrained Nonlinear Systems

In this paper, the problems of state-triggered sampling and self-triggered tracking control for nonlinear systems with constraints are studied. Firstly, the fuzzy observer is designed for the unknown state. In the presence of state-triggered sampling error, the cooperative design of constraint controller is a key problem to be solved. The median value theorem provides help to solve this problem and an asymmetric state-triggering strategy is presented. In addition, it is proved that the closed-loop signals are input-to-state stable, and the sampling error and tracking error are bounded. Then, a general self-triggered tracking control scheme is presented. In order to compare the control performance of different triggering mechanisms, fuzzy observer and logarithmic barrier Lyapunov function are selected also, and the scheme is designed under the framework of backstepping method. Co-designing controllers and scheduling functions is a key issue, so that the physical implementation benefits from not requiring constant monitoring of the state. Finally, the effectiveness of the proposed method is verified by case study, and the control performance of different triggering mechanisms is compared.

Stark control of electrons across the molecule-semiconductor interface.

Controlling matter at the level of electrons using ultrafast laser sources represents an important challenge for science and technology. Recently, we introduced a general laser control scheme (the Stark control of electrons at interfaces or SCELI) based on the Stark effect that uses the subcycle structure of light to manipulate electron dynamics at semiconductor interfaces [A. Garzón-Ramírez and I. Franco, Phys. Rev. B 98, 121305 (2018)]. Here, we demonstrate that SCELI is also of general applicability in molecule-semiconductor interfaces. We do so by following the quantum dynamics induced by non-resonant few-cycle laser pulses of intermediate intensity (non-perturbative but non-ionizing) across model molecule-semiconductor interfaces of varying level alignments. We show that SCELI induces interfacial charge transfer regardless of the energy level alignment of the interface and even in situations where charge exchange is forbidden via resonant photoexcitation. We further show that the SCELI rate of charge transfer is faster than those offered by resonant photoexcitation routes as it is controlled by the subcycle structure of light. The results underscore the general applicability of SCELI to manipulate electron dynamics at interfaces on ultrafast timescales.

Passivity Enhancement-Based General Design of Capacitor Current Active Damping for LCL-Type Grid-Tied Inverter

Grid-side current control based on capacitor-current-feedback (CCF) active damping is a common control method for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> -type grid-tied inverters. The control system should not only ensure inverter stability under complex grid conditions, but also be simple in structure. Therefore, the multi-objective design for the CCF control is proposed to enhance the passivity, while improving the internal stability. Firstly, the passive constraint of CCF control is derived, from which two general control schemes, namely, negative CCF-based phase-lead (N-Lead) control scheme and positive CCF-based phase-lag (P-Lag) control scheme, are proposed. Then, the two schemes are implemented by the design of a first-order phase-lead CCF controller and a first-order phase-lag compensator-based positive CCF controller. Besides, the parameters design is also given for the convenience of engineering application. Finally, the experimental results verify the theoretical analysis and the effectiveness of the proposed design methods.

Stochastic probes in delay viral infection model with general incidence rate and control strategies

An analytical expedition to spotlight vital role of incidence rates in epidemic models is exemplified via delayed virus infection model perturbed with Gaussian white noise in this article. A stochastic model structure with time delay in general incidence rate accomplished our assertion on the role of infection transmission rates in the course of viral dynamics. Stability properties exhibited by model system around equilibrium solutions are rigorously analyzed using Lyapunov technique. We have also strengthened our theoretical achievements with empirical substantiations and visualized them as graphical representations. Our exemplifications showcased intricate relationship between model dynamics and incidence rates in unison.

A Finite-Time Sliding Mode Control Approach for Constrained Euler–Lagrange System

This paper investigates a general control strategy to track the reference trajectory for the constrained Euler–Lagrange system with model uncertainties and unknown external disturbances. Unlike the disturbances assumed to be upper-bounded by a constant in other papers, we consider the disturbances to be bounded by a function of the system states, which are more realistic. First, the nominal controller was designed based on the nonsingular fast terminal sliding mode control, and global fast finite-time convergence to the sliding surface was guaranteed. As the system is state-constrained in this paper, we introduce the control barrier function approach to formulate the constraints and ensure the system does not break the restrictions. The proposed control strategy was numerically assessed on a two-link robot manipulator system, and the simulation results illustrate the effectiveness of the proposed control strategy.

Observer-Based Non-PDC Control Design for PMSG-Based Wind Energy Conversion Systems

In this study, the nonparallel distribution compensation (PDC) control is proposed for permanent magnet synchronous generator (PMSG)-based wind energy conversion systems (WECS). To do this, the nonlinear PMSG-based WECS is expressed by Takagi-Sugeno (T-S) fuzzy systems. Based on the imperfect premise matching, the T-S fuzzy observer is delineated for estimating the unmeasurable states of the proposed WECS. Unlike the traditional PDC control, a general non-PDC control strategy is designed via the T-S fuzzy observer. The designed control scheme indicates that it is not required to share the same membership functions as the observer and system. By constructing the Lyapunov functional with available information on the fuzzy membership function and introducing the slack variables, sufficient conditions are obtained to assure the asymptotic stability of error dynamic for the PMSG-based T-S fuzzy system and its observer. Finally, the feasibility and applicability of the derived theoretical results are validated through the numerical example.

Stitching Dynamic Movement Primitives and Image-Based Visual Servo Control

Utilizing perception for feedback control in combination with dynamic movement primitive (DMP)-based motion generation for a robot’s end-effector control is a useful solution for many robotic manufacturing tasks. For instance, while performing an insertion task when the hole or the recipient part is not visible in the eye-in-hand camera, a learning-based movement primitive method can be used to generate the end-effector path. Once the recipient part is in the field of view (FOV), image-based visual servo (IBVS) can be used to control the motion of the robot. Inspired by such applications, this article presents a generalized control scheme that switches between motion generation using DMP and IBVS control. To facilitate the design, a common state-space representation for the DMP and the IBVS systems is first established. The stability analysis of the switched system using multiple Lyapunov functions shows that the state trajectories converge to a bound asymptotically. The developed method is validated by three real-world experiments using the eye-in-hand configuration of a Baxter research robot.

Synergy-Space Recurrent Neural Network for Transferable Forearm Motion Prediction from Residual Limb Motion.

Transhumeral amputees experience considerable difficulties with controlling a multifunctional prosthesis (powered hand, wrist, and elbow) due to the lack of available muscles to provide electromyographic (EMG) signals. The residual limb motion strategy has become a popular alternative for transhumeral prosthesis control. It provides an intuitive way to estimate the motion of the prosthesis based on the residual shoulder motion, especially for target reaching tasks. Conventionally, a predictive model, typically an artificial neural network (ANN), is directly trained and relied upon to map the shoulder-elbow kinematics using the data from able-bodied subjects without extracting any prior synergistic information. However, it is essential to explicitly identify effective synergies and make them transferable across amputee users for higher accuracy and robustness. To overcome this limitation of the conventional ANN learning approach, this study explicitly combines the kinematic synergies with a recurrent neural network (RNN) to propose a synergy-space neural network for estimating forearm motions (i.e., elbow joint flexion-extension and pronation-supination angles) based on residual shoulder motions. We tested 36 training strategies for each of the 14 subjects, comparing the proposed synergy-space and conventional neural network learning approaches, and we statistically evaluated the results using Pearson's correlation method and the analysis of variance (ANOVA) test. The offline cross-subject analysis indicates that the synergy-space neural network exhibits superior robustness to inter-individual variability, demonstrating the potential of this approach as a transferable and generalized control strategy for transhumeral prosthesis control.

Research on Trajectory Tracking Control of Driverless Electric Formula Racing Car Based on Game Theory

A game theory based trajectory tracking control method is studied for the dual-objective optimization problem of trajectory tracking the accuracy and driving stability of driverless electric formula racing cars in high-speed trajectory conditions. The general control strategy and the model predictive controller based on the evolutionary game between the two players are designed to optimize their own decisions to achieve the optimal payoff for themselves, and to obtain the optimal solution to the dual-objective optimization problem, by taking the dual objectives of trajectory tracking accuracy and driving stability as the two players in the game. Considering the influence of the dynamic environment, the time-varying interactive game mechanism between two plays is introduced, the game payoff matrix is established, the weights of each subject are determined, and a dynamic replication system is constructed by weight evolution to find the optimal equilibrium strategy for the model prediction controller. The simulated results show that the designed controller can play a significant role in optimizing the trajectory tracking accuracy and driving stability compared to a single model predictive controller under different speed tracking conditions.

Building Automation Using Wireless Sensor Networks

AbstractThe use of wireless senor networks in building automation is gaining popularity so as to improve the safety, security, and energy efficiency of the buildings. The automation solution will differ as per the type of building. In this paper, authors have proposed three wireless sensor network (WSN) based automation solutions for buildings of different sizes. First strategy has been proposed for a small building using a single network. Second one is for a large building complex using multiple networks and third strategy is for building automation solution for local individual control. For building automation both monitoring and control of the parameters of interest are required. Authors have implemented first strategy in a small room of 501.9 m length and 410.9 m breadth. The humidity temperature and pressure (HTP) and TL sensor nodes of Eigen Technologies Ltd. have been used in this research work. HTP sensor node has been used for monitoring of humidity, temperature, and pressure. However, for the monitoring of luminosity TL sensor node has been chosen. Four sensor nodes of each type have been placed at appropriate locations in the selected room. Finally, a general control strategy for the temperature control has been proposed for the control of the building automation of a small building.

Interacting moving bottlenecks in traffic flow

&lt;abstract&gt;&lt;p&gt;We present a general multi-scale approach for modeling the interaction of controlled autonomous vehicles (AVs) with the surrounding traffic flow. The model consists of a scalar conservation law for the bulk traffic, coupled with ordinary differential equations describing the possibly interacting AV trajectories. The coupling is realized through flux constraints at the moving bottleneck positions, inducing the formation of non-classical jump discontinuities in the traffic density. In turn, AVs are forced to adapt their speed to the downstream traffic average velocity in congested situations. We analyze the model solutions in a Riemann-type setting, and propose an adapted finite volume scheme to compute approximate solutions for general initial data. The work paves the way to the study of general optimal control strategies for AV velocities, aiming at improving the overall traffic flow by reducing congestion phenomena and the associated externalities.&lt;/p&gt;&lt;/abstract&gt;

Optimal nonequilibrium thermometry in Markovian environments

What is the minimum time required to take a temperature? In this paper, we solve this question for a large class of processes where temperature is inferred by measuring a probe (the thermometer) weakly coupled to the sample of interest, so that the probe's evolution is well described by a quantum Markovian master equation. Considering the most general control strategy on the probe (adaptive measurements, arbitrary control on the probe's state and Hamiltonian), we provide bounds on the achievable measurement precision in a finite amount of time, and show that in many scenarios these fundamental limits can be saturated with a relatively simple experiment. We find that for a general class of sample-probe interactions the scaling of the measurement uncertainty is inversely proportional to the time of the process, a shot-noise like behaviour that arises due to the dissipative nature of thermometry. As a side result, we show that the Lamb shift induced by the probe-sample interaction can play a relevant role in thermometry, allowing for finite measurement resolution in the low-temperature regime. More precisely, the measurement uncertainty decays polynomially with the temperature as T&amp;#x2192;0, in contrast to the usual exponential decay with T&amp;#x2212;1. We illustrate these general results for (i) a qubit probe interacting with a bosonic sample, where the role of the Lamb shift is highlighted, and (ii) a collective superradiant coupling between a N-qubit probe and a sample, which enables a quadratic decay with N of the measurement uncertainty.