Analyzing the Combination Effects of Repetitive Transcranial Magnetic Stimulation and Motor Control Training on Balance Function and Gait in Patients with Stroke-Induced Hemiplegia

Objective:To analyze the effects of repetitive transcranial magnetic stimulation combined with motor control training on the treatment of stroke-induced hemiplegia,specifically focusing on the impact on patients’balance function and gait.Methods:Fifty-two cases of hemiplegic stroke patients were randomly divided into two groups,26 in the control group and 26 in the observation group,using computer-generated random grouping.All participants underwent conventional treatment and rehabilitation training.In addition to these,the control group received repetitive transcranial magnetic pseudo-stimulation therapy+motor control training,while the observation group received repetitive transcranial magnetic stimulation therapy+motor control training.The balance function and gait parameters of both groups were compared before and after the interventions and assessed the satisfaction of the interventions in both groups.Results:Before the invention,there were no significant differences in balance function scores and each gait parameter between the two groups(P>0.05).However,after the intervention,the observation group showed higher balance function scores compared to the control group(P<0.05).The observation group also exhibited higher step speed and step frequency,longer step length,and a higher overall satisfaction level with the intervention compared to the control group(P<0.05).Conclusion:The combination of repetitive transcranial magnetic stimulation and motor control training in the treatment of stroke-induced hemiplegia has demonstrated positive effects.It not only improves the patient’s balance function and gait but also contributes to overall physical rehabilitation.

A Fractional Order Fast Repetitive Control Paradigm of Vienna Rectifier for Power Quality Improvement

Due to attractive features,including high efficiency,low device stress,and ability to boost voltage,a Vienna rectifier is commonly employed as a battery charger in an electric vehicle(EV).However,the 6k±1 harmonics in the acside current of the Vienna rectifier deteriorate theTHDof the ac current,thus lowering the power factor.Therefore,the current closed-loop for suppressing 6k±1 harmonics is essential tomeet the desired total harmonic distortion(THD).Fast repetitive control(FRC)is generally adopted;however,the deviation of power grid frequency causes delay link in the six frequency fast repetitive control to become non-integer and the tracking performance to deteriorate.This paper presents the detailed parameter design and calculation of fractional order fast repetitive controller(FOFRC)for the non-integer delay link.The finite polynomial approximates the non-integer delay link through the Lagrange interpolation method.By comparing the frequency characteristics of traditional repetitive control,the effectiveness of the FOFRC strategy is verified.Finally,simulation and experiment validate the steadystate performance and harmonics suppression ability of FOFRC.

Self-adaptive PID controller of microwave drying rotary device tuning on-line by genetic algorithms

The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and with multivariable nonlinear interaction of microwave and materials. The conventional PID control strategy incorporated with optimization GA was put forward to maintain the optimum drying temperature in order to keep the moisture content below 1%, whose adaptation ability included the cost function of optimization GA according to the output change. Simulations on five different industrial process models and practical temperature process control system for selenium-enriched slag drying intensively by using IMDRDWM were carried out systematically, indicating the reliability and effectiveness of control design. The parameters of proposed control design are all on-line implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed, which makes the developed scheme simpler in its synthesis and application, providing the practical guidelines for the control implementation and the parameter design.

Continuous rotary motor electro-hydraulic servo system based on the improved repetitive controller

In order to suppress the periodic interference of the continuous rotary electro-hydraulic servo motor,this paper makes the motor tracking the periodic signals with high accuracy,and improves the influence of friction interference to the performance of continuous rotary electro-hydraulic servo motor.The mathematic model of the electro-hydraulic position servo system of the continuous rotary motor was established,and the compound control method was adopted based on the repetitive control,feed forward and PID to suppress the friction interference.Through the simulation,the result confirms that the compound control method decreases the tracking error of the system,increases the robust performance of the system and improves the performance of the continuous rotary electro-hydraulic servo motor.

Self-adaptive fuzzy controller with formulary rule for servo control of discharge gap in Micro EDM

In micro electrical discharge machining （micro EDM）, it is difficult for servo controlling the narrow discharge gap with the characters of non-linear and quick change. In this paper, aiming at solving the problems above, a self-adaptive fuzzy controller with formulary rule （SAFCFR） is presented based on the dual feedbacks composed by gap electric signal and discharge-ratio statistics. To ensure the properties of self-optimizing and fast stabilization, the formulary rule was designed with a tuning factor. In addition, the fast-convergence algorithms were introduced to adjust control target center and output scale factor. In this way, the normal discharge ratio can tend to the highest value during micro-EDM process. Experimental results show that the proposed algorithms are effective in improving the servo-control performance. According to the drilling-micro-EDM experiments, the machining efficiency is improved by 20% through applying SAFCFR. Moreover, SAFCFR is a prompt way to optimize parameters of discharge-gap servo control.

A repetitive-PI Current Controller for Boost Single Phase PFC Converters

In this paper, the Author presents the theory and application of repetitive proportional integral current con-troller for boost single phase ac-dc converter with power factor correction (PFC). A repetitive controller which is inserted in series with the proportional integral ( PI) controller shows very low crossover distortion of input current, low total harmonic distortion and very low tracking error when is compared with the con-ventional proportional integral controller. Full analysis of proposed controller is given and Matlab/Simulink is used for simulation. The simulation results show the validity of the proposed control method.

BIO-INSPIRED SELF-ADAPTIVE MANUFACTURING SYSTEM CONTROL ARCHITECTURE

Future manufacturing systems need to cope with frequent changes and disturbances, therefore their control architectures require constant adaptability, agility, stability, self-organization, intelligence, and robustness. Bio-inspired manufacturing system can well satisfy these requirements. For this purpose, by referencing the biological organization structure and the mechanism, a bio-inspired manufacturing cell is presented from a novel view, and then a bio-inspired self-adaptive manufacturing model is established based on the ultra-short feedback mechanism of the neuro-endocrine system. A hio-inspired self-adaptive manufacturing system coordinated model is also established based on the neuro-endocrine-immunity system （NEIS）. Finally, an example based on pheromone communication mechanism indicates that the robustness of the whole manufacturing system is improved by bio-inspired technologies.

Design and analysis of dual-mode structure repetitive control based hybrid current regulation scheme for active power filters

An all-digital hybrid current regulation scheme for the single-phase shunt active power filter （APF） is presented. The proposed hybrid current control scheme integrates the deadbeat control and the dual-mode structure repetitive control （DMRC） so that it can offer superior steady-state performance and good transient features. Unlike the conventional schemes, the proposed scheme-based APF can compensate both the odd and the even order harmonics in grid. The detailed design criteria and the stability analysis of the proposed hybrid current controller are presented. Moreover, an improved structure which incorporates the proposed hybrid controller and the resonant controller for tracking specific order harmonics is given. The relationships between the resonant controller and different repetitive control schemes are discussed. Experimental results verify the effectiveness and advantages of the proposed hybrid control scheme.

Repetitive PD control strategy with inverse transfer function compensation for CVCF inverter

A novel repetitive control strategy for the output waveform of single-phase CVCF inverters is presented. In this scheme, the inverse transfer function of inverter is used as a compensator to obtain stable and satisfy harmonic rejection. Besides, PD controller is adopted to improve transient performance. Simulation and experimental results, which are gotten from a DSP-based 400Hz, 5.5KW inverter, indicate that the proposed control scheme can achieve not only low THD during steady-state operation but also fast transient response during load step change.

Effect of Combined Low-frequency Repetitive Transcranial Magnetic Stimulation and Virtual Reality Training on Upper Limb Function in Subacute Stroke:a Double-blind Randomized Controlled Trail

The effect of combined low-frequency repetitive transcranial magnetic stimulation（LF r TMS） and virtual reality（VR） training in patients after stroke was assessed. In a double-blind randomized controlled trial, 112 patients with hemiplegia after stroke were randomly divided into two groups： experimental and control. In experimental group, the patients received LF r TMS and VR training treatment, and those in control group received sham r TMS and VR training treatment. Participants in both groups received therapy of 6 days per week for 4 weeks. The primary endpoint including the upper limb motor function test of Fugl-meyer assessment（U-FMA） and wolf motor function test（WMFT）, and the secondary endpoint including modified Barthel index（MBI） and 36-item Short Form Health Survey Questionnaire（SF-36） were assessed before and 4 weeks after treatment. Totally, 108 subjects completed the study（55 in experimental group and 53 in control group respectively）. After 4-week treatment, the U-FMA scores [mean difference of 13.2, 95% confidence interval（CI） 3.6 to 22.7, P〈0.01], WMFT scores（mean difference of 2.9, 95% CI 2.7 to 12.3, P〈0.01）, and MBI scores（mean difference 16.1, 95% CI 3.8 to 9.4, P〈0.05） were significantly increased in the experimental group as compared with the control group. The results suggested the combined use of LF r TMS with VR training could effectively improve the upper limb function, the living activity, and the quality of life in patients with hemiplegia following subacute stroke, which may provide a better rehabilitation treatment for subacute stroke.

Observer-based Iterative and Repetitive Learning Control for a Class of Nonlinear Systems

In this paper, both output-feedback iterative learning control(ILC) and repetitive learning control(RLC) schemes are proposed for trajectory tracking of nonlinear systems with state-dependent time-varying uncertainties. An iterative learning controller, together with a state observer and a fully-saturated learning mechanism, through Lyapunov-like synthesis, is designed to deal with time-varying parametric uncertainties. The estimations for outputs, instead of system outputs themselves, are applied to form the error equation, which helps to establish convergence of the system outputs to the desired ones. This method is then extended to repetitive learning controller design. The boundedness of all the signals in the closed-loop is guaranteed and asymptotic convergence of both the state estimation error and the tracking error is established in both cases of ILC and RLC. Numerical results are presented to verify the effectiveness of the proposed methods.

Design of Discrete-time Repetitive Control System Based on Two-dimensional Model

This paper presents a novel design method for discrete-time repetitive control systems （RCS） based on two-dimensional （2D） discrete-time model. Firstly, the 2D model of an RCS is established by considering both the control action and the learning action in RCS. Then, through constructing a 2D state feedback controller, the design problem of the RCS is converted to the design problem of a 2D system. Then, using 2D system theory and linear matrix inequality （LMI） method, stability criterion is derived for the system without and with uncertainties, respectively. Parameters of the system can be determined by solving the LMI of the stability criterion. Finally, numerical simulations validate the effectiveness of the proposed method.

Variable Parameter Self-Adaptive Control Strategy Based on Driving Condition Identification for Plug-In Hybrid Electric Bus

A variable parameter self-adaptive control strategy based on driving condition identification is proposed to take full advantage of the fuel saving potential of the plug-in hybrid electric bus(PHEB).Firstly,the principal component analysis(PCA)and the fuzzy c-means clustering(FCM)algorithm is used to construct the comprehensive driving cycle,congestion driving cycle,urban driving cycle and suburban driving cycle of Chinese urban buses.Secondly,an improved particle swarm optimization(IPSO)algorithm is proposed,and is used to optimize the control parameters of PHEB under different driving cycles,respectively.Then,the variable parameter self-adaptive control strategy based on driving condition identification is given.Finally,for an actual running vehicle,the driving condition is identified by relevance vector machine(RVM),and the corresponding control parameters are selected to control the vehicle.The simulation results show that the fuel consumption of using the variable parameter self-adaptive control strategy is reduced by 4.2% compared with that of the fixed parameter control strategy,and the feasibility of the variable parameter self-adaptive control strategy is verified.

Repetitive Learning Control for Time-varying Robotic Systems： A Hybrid Learning Scheme

重复学习控制为不明确的变化时间的机器的系统追踪的 finite-time-trajectory 被介绍。在时间函数以一个反复的学习方法被学习的地方，一个混合学习计划被给在系统动力学应付经常、变化时间的 unknowns，没有泰勒表示的帮助，当常规微分学习方法为估计经常的被建议时。介绍重复学习控制为在每个周期的开始的起始的重新定位避免要求，是不同的，并且变化时间的 unknowns 不是必要的周期。随混合学习的采纳，靠近环的系统的州的变量的固定被保证，追踪的错误被保证作为重复增加收敛到零，这被显示出。建议计划的有效性通过数字模拟被表明。

Neural Dynamics for Cooperative Motion Control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

REDUCTION APPROACHES FOR VIBRATION CONTROL OF REPETITIVE STRUCTURES

The reduction approaches are presented for vibration control of symmetric, cyclic periodic and linking structures. The condensation of generalized coordinates, the locations of sensors and actuators, and the relation between system inputs and control forces are assumed to be set in a symmetric way so that the control system posses the same repetition as the structure considered. By employing proper transformations of condensed generalized coordinates and the system inputs, the vibration control of an entire system can be implemented by carrying out the control of a number of sub-structures, and thus the dimension of the control problem can be significantly reduced.

FBFN-based adaptive repetitive control of nonlinearly parameterized systems

An adaptive repetitive control scheme is presented for a class of nonlinearly parameterized systems based on the fuzzy basis function network （FBFN）. The parameters of the fuzzy rules are tuned with adaptive schemes. To attenuate chattering effectively, the discontinuous control term is approximated by an adaptive PI control structure. The bound of the discontinuous control term is assumed to be unknown and estimated by an adaptive mechanism. Based on the Lyapunov stability theory, an adaptive repetitive control law is proposed to guarantee the closed-loop stability and the tracking performance. By means of FBFNs, which avoid the nonlinear parameterization from entering into the adaptive repetitive control, the controller singularity problem is solved. The proposed approach does not require an exact structure of the system dynamics, and the proposed controller is utilized to control a model of permanent-magnet linear synchronous motor subject to significant disturbances and parameter uncertainties. The simulation results demonstrate the effectiveness of the proposed method.

Robust H-infinity control of uncertain stochastic time-delay linear repetitive processes

Repetitive processes are a distinct class of 2D systems of both theoretic and practical interest.The robust H-infinity control problem for uncertain stochastic time-delay linear continuous repetitive processes is investigated in this paper.First,sufficient conditions are proposed in terms of stochastic Lyapunov stability theory,It o differential rule and linear matrix inequality technology.The corresponding controller design is then cast into a convex optimization problem.Attention is focused on constructing an admissible controller,which guarantees that the closed-loop repetitive processes are mean-square asymptotically stable and have a prespecified H-infinity performance γ with respect to all energy-bounded input signals.A numerical example illustrates the effectiveness of the proposed design scheme.

An Improved Repetitive Control Strategy for LCL Grid-Connected Inverter

This paper proposes a cascade repetitive control strategy based on odd internal mode,and combines it with proportional-integral(PI)control to establish a compound repetitive control system for improving the quality of grid connected current of LCL grid connected inverter.More specifically,the proposed method could effectively improve the control effect of grid-connected current of LCL inverter,restrain current harmonics and reduce the distortion rate of grid-connected current.Simulation experiment is conducted to verify the proposed repetitive control strategy,and the verification results show that,compared with traditional PI control,the proposed improved compound repetitive control strategy has a higher response speed,and the steady-state and dynamic performance have also been significantly improved.