Research on Mixed Logic Dynamic Modeling and Finite Control Set Model Predictive Control of Multi-Inverter Parallel System

Parallel connection of multiple inverters is an important means to solve the expansion,reserve and protection of distributed power generation,such as photovoltaics.In view of the shortcomings of traditional droop control methods such as weak anti-interference ability,low tracking accuracy of inverter output voltage and serious circulation phenomenon,a finite control set model predictive control(FCS-MPC)strategy of microgrid multiinverter parallel system based on Mixed Logical Dynamical(MLD)modeling is proposed.Firstly,the MLD modeling method is introduced logical variables,combining discrete events and continuous events to form an overall differential equation,which makes the modeling more accurate.Then a predictive controller is designed based on the model,and constraints are added to the objective function,which can not only solve the real-time changes of the control system by online optimization,but also effectively obtain a higher tracking accuracy of the inverter output voltage and lower total harmonic distortion rate(Total Harmonics Distortion,THD);and suppress the circulating current between the inverters,to obtain a good dynamic response.Finally,the simulation is carried out onMATLAB/Simulink to verify the correctness of the model and the rationality of the proposed strategy.This paper aims to provide guidance for the design and optimal control of multi-inverter parallel systems.

Research on AC Electronic Load with Energy Recovery Based on Finite Control Set Model Predictive Control

Nowadays,AC electronic loads with energy recovery are widely used in the testing of uninterruptible power supplies and power supply equipment.To tackle the problems of control difficulty,strategy complexity,and poor dynamic performance of AC electronic load with energy recovery of the conventional control strategy,a control strategy of AC electronic load with energy recovery based on Finite Control Set Model Predictive Control(FCSMPC)is developed.To further reduce the computation burden of the FCS-MPC,a simplified FCS-MPC with transforming the predicted variables and using sector to select expected state is proposed.Through simplified model and equivalent approximation analysis,the transfer function of the system is obtained,and the stability and robustness of the system are analyzed.The performance of the simplified FCS-MPC is compared with space vector control(SVPWM)and conventional FCS-MPC.The results show that the FCS-MPC method performs better dynamic response and this advantage is more obvious when simulating high power loads.The simplified FCS-MPC shows similar control performance to conventional FCS-MPC at less computation burden.The control performance of the system also shows better simulation results.

Finite Control Set Model Predictive Current Control of a Five-Phase PMSM with Virtual Voltage Vectors and Adaptive Control Set

This paper presents an improved finite control set model predictive current control(FCS-MPCC)of a five-phase permanent magnet synchronous motor(PMSM).First,to avoid including all the 32 voltage vectors provided by a two-level five-phase inverter into the control set,virtual voltage vectors are adopted.As the third current harmonics can be much reduced by virtual voltage vectors automatically,the harmonic items in the cost function of conventional FCS-MPCC are not considered.Furthermore,an adaptive control set is proposed based on voltage prediction.Best control set with proper voltage vector amplitude corresponding to different rotor speed can be achieved by this method.Consequently,current ripples can be largely reduced and the system performs much better.At last,simulations are established to verify the steady and transient performance of the proposed FCS-MPCC,and experiments based on a 2 kW five-phase motor are carried out.The results have validated the performance improvement of the proposed control strategy.

Finite control set model predictive control of three-port converter for interfacing a PV-battery energy storage system to a three-phase stand-alone AC system

This paper proposes a multiport bidirectional non-isolated converter topology that provides advantages in terms of simultaneous multiple operations,single-stage conversion,high power density and reduced power losses due to the lower number of switches.The proposed multiport converter uses a centralized non-linear controller known as a finite control set model predictive controller to manage the flow of power between different ports.It deals with the parallel operation of photovoltaic and battery energy storage systems for stand-alone alternating current(AC)systems.The converter connects the lower voltage battery to the photovoltaic port using a bidirectional buck/boost converter and the photovoltaic port is linked to the stand-alone AC load through a three-phase full-bridge inverter.Each leg of the three-phase converter will act as a bidirectional direct current(DC)/DC converter as well as an inverter simultaneously.Only six switches manage the power transfer between all the connected ports of photovoltaic-battery energy storage system linked to the stand-alone AC load.The proposed multiport converter is mathematically modelled and controlled by a finite control set model predictive controller.The system is validated in simulation(1-kW rating)and experimental environment(200-W rating).The hardware prototype is developed in the laboratory and the controller is implemented on the field-programmable gate array board.Two independent case studies are carried out to validate the efficacy of the system.The first scenario is for a change in solar irradiance,while the second scenario is for a change in the output load.

Finite-Control-Set Model Predictive Control of Permanent Magnet Synchronous Motor Drive Systems——An Overview

Permanent magnet synchronous motors(PMSMs)have been widely employed in the industry. Finite-control-set model predictive control(FCS-MPC), as an advanced control scheme, has been developed and applied to improve the performance and efficiency of the holistic PMSM drive systems. Based on the three elements of model predictive control, this paper provides an overview of the superiority of the FCS-MPC control scheme and its shortcomings in current applications. The problems of parameter mismatch, computational burden, and unfixed switching frequency are summarized. Moreover, other performance improvement schemes, such as the multi-vector application strategy, delay compensation scheme, and weight factor adjustment, are reviewed. Finally, future trends in this field is discussed, and several promising research topics are highlighted.

Gradient control and synchronization of Julia sets

This paper firstly introduces the control methods to fractals and give the definition of synchronization of Julia sets between two different systems. Especially, the gradient control method is taken on the classic Julia sets of complex quadratic polynomial Zn＋1 = zn^2＋ c, which realizes its Julia sets control and synchronization. The simulations illustrate the effectiveness of the method.

Research and Development of Engine-Generator Set Control System for Tracked Vehicle Electric Transmission System

As an energy generating equipment, the engine-generator set supplies power to the electric transmission. Therefore, its control is one of the key technologies of electric vehicles. Based on the discussion about the demands to the engine-generator set in tracked vehicles, the detailed function of engine-generator and the control strategy are determined. The hardware and software of the control system are also developed and tested in a prototype vehicle. The experiment results show that the control system has good reliability and can satisfy the power requirements of vehicles under all operating conditions.

Control and synchronization of second Julia sets

A visualization of Julia sets of the complex Henon map system with two complex variables is introduced in this paper. With this method, the optimal control function method is introduced to this system and the control and synchronization of its Julia sets are achieved. Control and synchronization of generalized Julia sets are also achieved with this optimal control method. The simulations illustrate the efficacy of this method.

Deducing Complete Selection Rule Set for Driver Nodes to Guarantee Network’s Structural Controllability

Structural controllability is critical for operating and controlling large-scale complex networks. In real applications, for a given network, it is always desirable to have more selections for driver nodes which make the network structurally controllable. Different from the works in complex network field where structural controllability is often used to explore the emergence properties of complex networks at a macro level,in this paper, we investigate it for control design purpose at the application level and focus on describing and obtaining the solution space for all selections of driver nodes to guarantee structural controllability. In accord with practical applications,we define the complete selection rule set as the solution space which is composed of a series of selection rules expressed by intuitive algebraic forms. It explicitly indicates which nodes must be controlled and how many nodes need to be controlled in a node set and thus is particularly helpful for freely selecting driver nodes. Based on two algebraic criteria of structural controllability, we separately develop an input-connectivity algorithm and a relevancy algorithm to deduce selection rules for driver nodes. In order to reduce the computational complexity,we propose a pretreatment algorithm to reduce the scale of network's structural matrix efficiently, and a rearrangement algorithm to partition the matrix into several smaller ones. A general procedure is proposed to get the complete selection rule set for driver nodes which guarantee network's structural controllability. Simulation tests with efficiency analysis of the proposed algorithms are given and the result of applying the proposed procedure to some real networks is also shown, and these all indicate the validity of the proposed procedure.

Input Structure Design for Structural Controllability of Complex Networks

This paper addresses the problem of the input design of large-scale complex networks.Two types of network components,redundant inaccessible strongly connected component(RISCC)and intermittent inaccessible strongly connected component(IISCC)are defined,and a subnetwork called a driver network is developed.Based on these,an efficient method is proposed to find the minimum number of controlled nodes to achieve structural complete controllability of a network,in the case that each input can act on multiple state nodes.The range of the number of input nodes to achieve minimal control,and the configuration method(the connection between the input nodes and the controlled nodes)are presented.All possible input solutions can be obtained by this method.Moreover,we give an example and some experiments on real-world networks to illustrate the effectiveness of the method.

Multi-objective optimization based optimal setting control for industrial double-stream alumina digestion process

The operation variables,including feed rate of ore slurry,caustic solution and live steams in the double-stream alumina digestion process,determine the product quality,process costs and the environment pollution.Previously,they were set by the technical workers according to the offline analysis results and an empirical formula,which leads to unstable process indices and high consumption frequently.So,a multi-objective optimization model is built to maintain the balance between resource consumptions and process indices by taking technical indices and energy efficiency as objectives,where the key technical indices are predicted based on the digestion kinetics of diaspore.A multi-objective state transition algorithm(MOSTA)is improved to solve the problem,in which a self-adaptive strategy is applied to dynamically adjust the operator factors of the MOSTA and dynamic infeasible threshold is used to handle constraints to enhance searching efficiency and ability of the algorithm.Then a rule based strategy is designed to make the final decision from the Pareto frontiers.The method is integrated into an optimal control system for the industrial digestion process and tested in the actual production.Results show that the proposed method can achieve the technical target while reducing the energy consumption.

Set-point Filter Design for a Two-degree-of-freedom Fractional Control System

This paper focuses on a new approach to design(possibly fractional) set-point filters for fractional control systems.After designing a smooth and monotonic desired output signal,the necessary command signal is obtained via fractional input-output inversion.Then,a set-point filter is determined based on the synthesized command signal.The filter is computed by minimizing the 2-norm of the difference between the command signal and the filter step response.The proposed methodology allows the designer to synthesize both integer and fractional setpoint filters.The pros and cons of both solutions are discussed in details.This approach is suitable for the design of two degreeof-freedom controllers capable to make the set-point tracking performance almost independent from the feedback part of the controller.Simulation results show the effectiveness of the proposed methodology.

A Dual-mode Nonlinear Model Predictive Control with the Enlarged Terminal Constraint Sets

Aiming at a class of nonlinear systems with multiple equilibrium points, we present a dual-mode model predictive control algorithm with extended terminal constraint set combined with control invariant set and gain schedule. Local LQR control laws and the corresponding maximum control invariant sets can be designed for finite equilibrium points. It is guaranteed that control invariant sets are overlapped each other. The union of the control invariant sets is treated as the terminal constraint set of predictive control. The feasibility and stability of the novel dual-mode model predictive control are investigated with both variable and fixed horizon. Because of the introduction of extended terminal constrained set, the feasibility of optimization can be guaranteed with short prediction horizon. In this way, the size of the optimization problem is reduced so it is computationally efficient. Finally, a simulation example illustrating the algorithm is presented.

Attractor-Based Simultaneous Design of the Minimum Set of Control Nodes and Controllers in Boolean Networks

Design of control strategies for gene regulatory networks is a challenging and important topic in systems biology. In this paper, the problem of finding both a minimum set of control nodes (control inputs) and a controller is studied. A control node corresponds to a gene that expression can be controlled. Here, a Boolean network is used as a model of gene regulatory networks, and control specifications on attractors, which represent cell types or states of cells, are imposed. It is important to design a gene regulatory network that has desired attractors and has no undesired attractors. Using a matrix-based representation of BNs, this problem can be rewritten as an integer linear programming problem. Finally, the proposed method is demonstrated by a numerical example on a WNT5A network, which is related to melanoma.

Feedback control and synchronization of Mandelbrot sets

The movement of a particle could be depicted by the Mandelbrot set from the fractal viewpoint. According to the requirement, the movement of the particle needs to show different behaviors. In this paper, the feedback control method is taken on the classical Mandelbrot set. By amending the feedback item in the controller, the control method is applied to the generalized Mandelbrot set and by taking the reference item to be the trajectory of another system, the synchronization of Mandelbrot sets is achieved.

PI control based on fuzzy set-point weighting tracking for hydraulic crane boom system

A PI control strategy based on fuzzy set-point weighting following was proposed for the active damping control of a hydraulic crane boom system （HCBS）. Two valve-controlled PI controllers, which include a proportional feedforward controller based on fuzzy set-point weighting following and a limited semi-integrator（LSI）, are designed respectively. LSI is used to limit output signal and to prevent wind up at the low frequency of the spectrum. By using a range camera and an electronic feedback control, the tip damping on the HCBS can be adjusted artificially. A collaborative control simulation technique of HOPSAN and MATLAB/SIMULINK is applied to the controller design. Simulation results show that the proposed PI control system has less overshoot as well as faster response. The tip damping on the HCBS during operation is improved.

Stabilizing model predictive control scheme for piecewise affine systems with maximal positively invariant terminal set

An efficient algorithm is proposed for computing the solution to the constrained finite time optimal control （CFTOC） problem for discrete-time piecewise affine （PWA） systems with a quadratic performance index. The maximal positively invariant terminal set, which is feasible and invariant with respect to a feedback control law, is computed as terminal target set and an associated Lyapunov function is chosen as terminal cost. The combination of these two components guarantees constraint satisfaction and closed-loop stability for all time. The proposed algorithm combines a dynamic programming strategy with a multi-parametric quadratic programming solver and basic polyhedral manipulation. A numerical example shows that a larger stabilizable set of states can be obtained by the proposed algorithm than precious work.

纳布啡不同镇痛泵参数用于剖宫产术后镇痛的比较

目的比较三种不同参数纳布啡患者自控静脉镇痛(PCIA)用于剖宫产术后镇痛的效果。方法选择剖宫产术后的产妇179例,年龄18~45岁,BMI 20~35 kg/m 2,ASAⅠ或Ⅱ级,术后均给予PCIA(纳布啡2 mg/kg+托烷司琼8 mg/100 ml,间隔时间15 min,极限量10 ml/h)。将产妇随机分成三组:对照组(C组,背景量2 ml/h,单次按压2 ml,n=60)、低背景量组(L组,背景量1 ml/h,单次按压3 ml,n=62)和无背景量组(N组,背景量0 ml/h,单次按压4 ml,n=57)。记录术后2、6、12、24、48 h静息和活动时VAS疼痛评分、纳布啡消耗量。记录术后48 h内PCIA按压情况、术后首次肛门排气时间、术后住院时间、Ramsay镇静评分和术后恶心呕吐、头晕、感染等不良反应的发生情况。结果与C组比较,N组术后6、12 h静息时VAS疼痛评分明显升高(P<0.05),L组和N组术后24、48 h纳布啡消耗量明显减少,术后首次肛门排气时间明显缩短,头晕发生率明显降低(P<0.05),N组术后住院时间明显缩短(P<0.05)。与L组比较,N组术后12 h静息和活动时VAS疼痛评分明显升高,术后48 h纳布啡消耗量明显减少,总按压次数和有效按压次数明显增加,术后首次肛门排气时间明显缩短(P<0.05)。三组Ramsay镇静评分和术后恶心呕吐、感染发生率差异无统计学意义。结论低背景量(1 ml/h)纳布啡PCIA可在充分镇痛的同时,减少阿片类药物用量,降低相关不良反应发生率,缩短术后首次肛门排气时间,可作为纳布啡PCIA用于剖宫产术后镇痛参数设置的优先选择。

All Set and Intelligent Bargaining Control Model

As the rapid development of electronic commerce in our country in recent years it is becoming focus how to use electronic commerce to realize non-distant communication of E2C and E2E which is called the second generation of electronic commerce. This paper combines the All Set theory with Matter Element transform into bargaining theory to build All Set bargaining model. In this model, the bargaining conditions of both sides are viewed as Matter Element which is the basic cell of extension theory. By using All Set theory, shell theory, fuzzy control and extension theory, the process of Matter Element transform from original Matter Element to final Matter Element which can be satisfied by both sides in bargaining is discussed. There are four axioms are distracted based on the All Set bargaining model. At last we extend our bi-persons bargaining model to multi-persons bargaining model by using soft matching system, and make it possible for intelligent bargaining on the supporting of electronic commerce.