Distributed model predictive control based on adaptive sampling mechanism

In this work,an adaptive sampling control strategy for distributed predictive control is proposed.According to the proposed method,the sampling rate of each subsystem of the accused object is determined based on the periodic detection of its dynamic behavior and calculations made using a correlation function.Then,the optimal sampling interval within the period is obtained and sent to the corresponding sub-prediction controller,and the sampling interval of the controller is changed accordingly before the next sampling period begins.In the next control period,the adaptive sampling mechanism recalculates the sampling rate of each subsystem’s measurable output variable according to both the abovementioned method and the change in the dynamic behavior of the entire system,and this process is repeated.Such an adaptive sampling interval selection based on an autocorrelation function that measures dynamic behavior can dynamically optimize the selection of sampling rate according to the real-time change in the dynamic behavior of the controlled object.It can also accurately capture dynamic changes,meaning that each sub-prediction controller can more accurately calculate the optimal control quantity at the next moment,significantly improving the performance of distributed model predictive control(DMPC).A comparison demonstrates that the proposed adaptive sampling DMPC algorithm has better tracking performance than the traditional DMPC algorithm.

Multi-Objective Optimal Dispatch Considering Wind Power and Interactive Load for Power System

With the rapid and large-scale development of renewable energy, the lack of new energy power transportation or consumption, and the shortage of grid peak-shifting ability have become increasingly serious. Aiming to the severe wind power curtailment issue, the characteristics of interactive load are studied upon the traditional day-ahead dispatch model to mitigate the influence of wind power fluctuation. A multi-objective optimal dispatch model with the minimum operating cost and power losses is built. Optimal power flow distribution is available when both generation and demand side participate in the resource allocation. The quantum particle swarm optimization (QPSO) algorithm is applied to convert multi-objective optimization problem into single objective optimization problem. The simulation results of IEEE 30-bus system verify that the proposed method can effectively reduce the operating cost and grid loss simultaneously enhancing the consumption of wind power.

Application of the hybrid genetic particle swarm algorithm to design the linear quadratic regulator controller for the accelerator power supply

Purpose The purpose of this paper is to study a new method to improve the performance of the magnet power supply in the experimental ring of HIRFL-CSR.Methods A hybrid genetic particle swarm optimization algorithm is introduced,and the algorithm is applied to the optimal design of the LQR controller of pulse width modulated power supply.The fitness function of hybrid genetic particle swarm optimization is a multi-objective function,which combined the current and voltage,so that the dynamic performance of the closed-loop system can be better.The hybrid genetic particle swarm algorithm is applied to determine LQR controlling matrices Q and R.Results The simulation results show that adoption of this method leads to good transient responses,and the computational time is shorter than in the traditional trial and error methods.Conclusions The results presented in this paper show that the proposed method is robust,efficient and feasible,and the dynamic and static performance of the accelerator PWM power supply has been considerably improved.

基于前馈模糊逻辑PID策略的MFC电压控制和非线性性能分析（英文）

微生物燃料电池(MFC)是一种具有应用前景的清洁供电能源设备,但在其运行过程中存在着严重的非线性和干扰,保证其输出电压快速、平稳地达到设定值是一个重要课题。调节进料流量是实现这一目标的有效途径,特别是通过调节阳极进料流量可以达到满意的效果。本文使用了一种前馈模糊逻辑PID算法,引入模糊逻辑系统来处理MFC的非线性动态特性,并根据解模糊化原理计算出相应的PID参数。电流密度的大小值用于模拟外部负载的大小。仿真结果表明,前馈模糊逻辑PID方法能够快速、平稳地跟踪设定值。与其他三种控制方法相比,该方法跟踪精度较佳,抗负载突发干扰的能力较强。

Batch process monitoring based on WGNPE–GSVDD related and independent variables

In many batch processes, there are related or independence relationships among process variables. The traditional monitoring method usually carries out a single statistical model according to the related or independent method, and in the feature extraction there is not fully taken into account the characterization of fault information, it will make the process monitoring ineffective, so a fault monitoring method based on WGNPE(weighted global neighborhood preserving embedding)–GSVDD(greedy support vector data description) related and independent variables is proposed. First, mutual information method is used to separate the related variables and independent variables. Secondly, WGNPE method is used to extract the local and global structures of the related variables in batch process and highlight the fault information, GSVDD method is used to extract the process information of the independent variables quickly and effectively. Finally, the statistical monitoring model is established to achieve process monitoring based on WGNPE and GSVDD. The effectiveness of the proposed method was verified by the penicillin fermentation process.

Model predictive control of grid-connected PV power generation system considering optimal MPPT control of PV modules

Because of system constraints caused by the external environment and grid faults,the conventional maximum power point tracking(MPPT)and inverter control methods of a PV power generation system cannot achieve optimal power output.They can also lead to misjudgments and poor dynamic performance.To address these issues,this paper proposes a new MPPT method of PV modules based on model predictive control(MPC)and a finite control set model predictive current control(FCS-MPCC)of an inverter.Using the identification model of PV arrays,the module-based MPC controller is designed,and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature.An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors,the optimal voltage vector is selected according to the optimal value function,and the corresponding optimal switching state is applied to power semiconductor devices of the inverter.The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified,and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink.The results show that MPC has better tracking performance under constraints,and the system has faster and more accurate dynamic response and flexibility than conventional PI control.

Multiscale prediction of wind speed and output power for the wind farm

This paper focuses on establishing the multiscale prediction models for wind speed and power in wind farm by the average wind speed collected from the history records. Each type of the models is built with different time scales and by different approaches. There are three types of them that a short-term model for a day ahead is based on the least squares support vector machine （LSSVM）, a medium-term model for a month ahead is on the combination of LSSVM and wavelet transform （WT）, and a long-term model for a year ahead is on the empirical mode decomposition （EMD） and recursive least square （RLS） approaches. The simulation studies show that the average value of the mean absolute percentage error （MAPE） is 4.91%, 6.57% and 16.25% for the short-term, the medium-term and the long-term prediction, respectively. The predicted data also can be used to calculate the predictive values of output power for the wind farm in different time scales, combined with the generator＇s power characteristic, meteorologic factors and unit efficiency under various operating conditions.

Control of upper limb rehabilitation robot based on active disturbance rejection control

The upper limb rehabilitation robot technology integrates rehabilitation medicine,human anatomy,mechanics,computer science,robotics,and many other disciplines.Its main function is to drive the affected limb to carry out rehabilitation training to restore the condition of patients with upper limb dyskinesia,which plays a great role in improving the quality of life.In this study,to resolve the problems of slow convergence speed and poor tracking accuracy due to the interference of patient spasms with the trajectory-tracking control of the upper limb rehabilitation robot,a novel algorithm based on active disturbance rejection control(ADRC)is adopted,and the convergence of its main structure is proved by the time-domain analysis method.First,this ADRC algorithm can obtain better trajectory-tracking performance due to its non-linear extended observer and non-linear feedback mechanism,even if the model suffers a strong disturbance or receives inaccurate information.Second,the non-linear tracking differentiator can guarantee to gain quick convergence speed.To validate this algorithm,a model of three degrees of freedom upper limb rehabilitation robot is established using MATLAB R2019b and three situations including strong spasm and weak spasm are carried out to prove the effectiveness and reliability of the control algorithm designed.

Detecting the breakup of spiral waves in small-world networks of neurons due to channel block

The breakup of a spiral wave by blockade of sodium and potassium channels in a small-world network of Hodgkin-Huxley neurons is investigated in detail.The influence of ion channel block in poisoned excitable membrane patches of a certain size is measured,by varying channel noise and channel densities resulting from the change in conductance,For example,tetraethylammonium is known to cause a block(poisoning) of potassium channels,while tetrodotoxin blocks sodium channels.We observed the occurrence of spiral waves,which are ordered waves believed to play an important role in facilitating the propagation of electric signals across quiescent regions of the brain.In this paper,the effect of channel block was measured by the factors xK and xNa,which represent the ratios of unblocked,or active,ion channels,to the overall number of potassium or sodium ion channels,respectively.To quantify these observations,we use a simple but robust synchronization measure,which succinctly captures the transition from spiral waves to other collective states,such as broken segments resulting from the breakup of the spiral wave.The critical thresholds of channel block can be inferred from the abrupt changes occurring in plots of the synchronization measure against different values of xK and xNa.Notably,small synchronization factors can be tightly associated with states where the formation of spiral waves is robust to mild channel block.

Dynamic parameter identification of upperlimb rehabilitation robot system based on variable parameter particle swarm optimisation

To solve the problem of uncertain parameters in dynamic modelling of upper-limb rehabilitation robots,a dynamic parameter identification method based on variable parameters particle swarm optimisation(PSO)is developed.Based on the dynamic model of the system,the algorithm changes the inertia parameter and learning law of the basic PSO algorithm from the fixed-parameter to the function that changes with the number of iterations.It solves the problems of small search space in the early stage and slow convergence speed in the later stage of the basic PSO algorithm,which greatly improves its identification accuracy.Finally,through the comparison and analysis of the simulation results,compared with those of the least square(LS)and unmodified PSO identification algorithms,a great improvement in the identification accuracy of the algorithm is achieved.The control effect in the actual control system is also much better than those of the LS and PSO algorithms.