APPLICATION OF FRF ESTIMATOR BASED ON ERRORS-IN-VARIABLES MODEL IN MULTI-INPUT MULTI-OUTPUT VIBRATION CONTROL SYSTEM

The FRF estimator based on the errors-in-variables （EV） model of multi-input multi-output （MIMO） system is presented to reduce the bias error of FRF HI estimator. The FRF HI estimator is influenced by the noises in the inputs of the system and generates an under-estimation of the true FRF. The FRF estimator based on the EV model takes into account the errors in both the inputs and outputs of the system and would lead to more accurate FRF estimation. The FRF estimator based on the EV model is applied to the waveform replication on the 6-DOF （degree-of-freedom） hydraulic vibration table. The result shows that it is favorable to improve the control precision of the MIMO vibration control system.

Transfer-learning multi-input multi-output equalizer for mode-division multiplexing systems

We propose a transfer-learning multi-input multi-output(TL-MIMO)scheme to significantly reduce the required training complexity for converging the equalizers in mode-division multiplexing(MDM)systems.Based on a built three-mode(LP01,LP11a,and LP11b)multiplexed experimental system,we thoughtfully investigate the TL-MIMO performances on the three-typed data,collecting from different sampling times,launching optical powers,and inputting optical signal-to-noise ratios(OSNRs).A dramatic reduction of approximately 40%–83.33%in the required training complexity is achieved in all three scenarios.Furthermore,the good stability of TL-MIMO in both the launched powers and OSNR test bands has also been proved.

Matrix Power Control Algorithm for Multi-input Multi-output Random Vibration Test

Both auto-power spectrum and cross-power spectrum need to be controlled in multi-input multi-output （MIMO） random vibration test. During the control process with the difference control algorithm （DCA）, a lower triangular matrix is derived from Cholesky decomposition of a reference spectrum matrix. The diagonal elements of the lower triangular matrix （DELTM） may become negative. These negative values have no meaning in physical significance and can cause divergence of auto-power spectrum control. A proportional root mean square control algorithm （PRMSCA） provides another method to avoid the divergence caused by negative values of DELTM, but PRMSCA cannot control the cross-power spectrum. A new control algorithm named matrix power control algorithm （MPCA） is proposed in the paper. MPCA can guarantee that DELTM is always positive in the auto-power spectrum control. MPCA can also control the cross-power spectrum. After these three control algorithms are analyzed, three-input three-output random vibration control tests are implemented on a three-axis vibration shaker. The results show the validity of the proposed MPCA.

Control method for multi-input multi-output non-Gaussian random vibration test with cross spectra consideration

A control method for Multi-Input Multi-Output（MIMO） non-Gaussian random vibration test with cross spectra consideration is proposed in the paper. The aim of the proposed control method is to replicate the specified references composed of auto spectral densities, cross spectral densities and kurtoses on the test article in the laboratory. It is found that the cross spectral densities will bring intractable coupling problems and induce difficulty for the control of the multioutput kurtoses. Hence, a sequential phase modification method is put forward to solve the coupling problems in multi-input multi-output non-Gaussian random vibration test. To achieve the specified responses, an improved zero memory nonlinear transformation is utilized first to modify the Fourier phases of the signals with sequential phase modification method to obtain one frame reference response signals which satisfy the reference spectra and reference kurtoses. Then, an inverse system method is used in frequency domain to obtain the continuous stationary drive signals. At the same time, the matrix power control algorithm is utilized to control the spectra and kurtoses of the response signals further. At the end of the paper, a simulation example with a cantilever beam and a vibration shaker test are implemented and the results support the proposed method very well.

Multi-input multi-output random vibration control using Tikhonov filter

Noises always disturb the control effect of an environment test especially in multi-input multi-output(MIMO) systems. If the frequency response function matrices are ill-conditioned, the noises in the driving forces will be amplified and the response spectral lines may awfully exceed their tolerances. Most of the major biases between the response spectra and the reference spectra are produced by the amplified noises. However, ordinary control algorithms can hardly reduce the level of noises. The influences of the noises on both the auto- and cross-power spectra are analyzed in this paper. As a conventional frequency domain method on the inverse problem, the Tikhonov filter is adopted in the environment test to suppress the exceeding spectral lines. By altering regularization parameters gradually, the auto-power spectra can be improved in a closed control loop. Instead of using the traditional way of selecting regularization parameters, we observe the coherence change to estimate noise eliminations. Incidentally, the requirement of coherence control can be realized. The errors of the phase are then studied and a phase control algorithm is introduced at the end as a supplement of cross-power spectra control. The Tikhonov filter and the proposed phase control algorithm are tested numerically and experimentally. The results show that the noises in the vicinity of lightly damped resonant peaks are more stubborn. The response spectra are able to be greatly improved by the combination of these two methods.

Set-point-related Indirect Iterative Learning Control for Multi-input Multi-output Systems

A form of iterative learning control (ILC) is used to update the set-point for the local controller. It is referred to as set-point-related (SPR) indirect ILC. SPR indirect ILC has shown excellent performance: as a supervision module for the local controller, ILC can improve the tracking performance of the closed-loop system along the batch direction. In this study, an ILC-based P-type controller is proposed for multi-input multi-output (MIMO) linear batch processes, where a P-type controller is used to design the control signal directly and an ILC module is used to update the set-point for the P-type controller. Under the proposed ILC-based P-type controller, the closed-loop system can be transformed to a 2-dimensional (2D) Roesser s system. Based on the 2D system framework, a sufficient condition for asymptotic stability of the closed-loop system is derived in this paper. In terms of the average tracking error (ATE), the closed-loop control performance under the proposed algorithm can be improved from batch to batch, even though there are repetitive disturbances. A numerical example is used to validate the proposed results.

Dynamic Modeling and Closed-loop Control of Hybrid Grid-connected Renewable Energy System with Multi-input Multi-output Controller

In this study, a novel approach for dynamic modeling and closed-loop control of hybrid grid-connected renewable energy system with multi-input multi-output(MIMO) controller is proposed. The studied converter includes two parallel DC-DC boost converters, which are connected into the power grid through a single-phase H-bridge inverter. The proposed MIMO controller is developed for maximum power point tracking of photovoltaic(PV)/fuel-cell(FC) input power sources and output power control of the grid-connected DC-AC inverter. Considering circuit topology of the system, a unique MIMO model is proposed for the analysis of the entire system. A unique model of the system includes all of the circuit state variables in DCDC and DC-AC converters. In fact, from the viewpoint of closed-loop controller design, the hybrid grid-connected energy system is an MIMO system. The control inputs of the system are duty cycles of the DC-DC boost converters and the amplitude modulation index of DC-AC inverters. Furthermore, the control outputs are the output power of the PV/FC input power sources as well as AC power injected into the power grid. After the development of the unique model for the entire system, a decoupling network is introduced for system input-output linearization due to inherent connection of the control outputs with all of the system inputs. Considering the decoupled model and small signal linearization, the required linear controllers are designed to adjust the outputs. Finally, to evaluate the accuracy and effectiveness of the designed controllers, the PV/FC based grid-connected system is simulated using the MATLAB/Simulink toolbox.

Design of Decentralized Multi-input Multi-output Repetitive Control Systems

This paper presents the design of decentralized repetitive control （RC） for multi-input multi-output （MIMO） systems. An optimization method is used to obtain a RC compensator that ensures system stability and good tracking performance. The designed compensator is in the form of a stable, low order, and causal filter, in which the compensator can be implemented separately without being merged with the RC internal model. This will reduce complexity in the implementation. Simulation results and comparison study are given to demonstrate the effectiveness of the proposed design. The novelty of design is also verified in experiments on a 2 degrees of freedom （DOF） robot.

A benchmark system to investigate the non-minimum phase behaviour of multi-input multi-output systems

In this paper,the problem of designing a multi-input multi-output(MIMO)systemfor studying the non-minimum phase(NMP)behaviour is considered.For this purpose,a NMP MIMO circuit is proposed and studied under different conditions.The main reason for designing this circuit is the lack of a simple and flexible benchmark for examining different control methods.Due to the simple structure and capability of showing different NMP characteristics,our proposed system is a suitable choice to study the behaviour of these systems.Also,our proposed system can be extended by series and parallel connections to generate more complicated benchmarks.The other advantages of this system are the large number of tunable parameters,adjustable interaction,variable number of poles and zeros,and inexpensive cost.Moreover,this benchmark can be used as a tool for hardware simulation.Finally,an optimal H∞decoupling control is applied to this benchmark to verify its effectiveness.

A multi-input and multi-output design on automotive engine management system

Lookup table is widely used in automotive industry for the design of engine control units(ECU).Together with a proportional-integral controller,a feed-forward and feedback control scheme is often adopted for automotive engine management system(EMS).Usually,an ECU has a structure of multi-input and single-output(MISO).Therefore,if there are multiple objectives proposed in EMS,there would be corresponding numbers of ECUs that need to be designed.In this situation,huge efforts and time were spent on calibration.In this work,a multi-input and multi-out(MIMO) approach based on model predictive control(MPC) was presented for the automatic cruise system of automotive engine.The results show that the tracking of engine speed command and the regulation of air/fuel ratio(AFR) can be achieved simultaneously under the new scheme.The mean absolute error(MAE) for engine speed control is 0.037,and the MAE for air fuel ratio is 0.069.

Multi-output Gaussian Process Regression Model with Combined Kernel Function for Polyester Esterification Processes

In polyester fiber industrial processes,the prediction of key performance indicators is vital for product quality.The esterification process is an indispensable step in the polyester polymerization process.It has the characteristics of strong coupling,nonlinearity and complex mechanism.To solve these problems,we put forward a multi-output Gaussian process regression(MGPR)model based on the combined kernel function for the polyester esterification process.Since the seasonal and trend decomposition using loess(STL)can extract the periodic and trend characteristics of time series,a combined kernel function based on the STL and the kernel function analysis is constructed for the MGPR.The effectiveness of the proposed model is verified by the actual polyester esterification process data collected from fiber production.

多接收子阵下特征波束的角度空间调制方法

基于波束赋形的波束切换空间调制算法,不仅可以提高频谱效率,还可以利用天线阵列增益提高能量效率。该文利用信道响应构造统计平均的协方差矩阵,构造正交特征向量作为阵列的加权矢量,形成相互独立的多个等效传输信道,利用波束赋形增益和分集增益提高无线通信的传输可靠性和传输能力。进一步采用天线子阵分组方法,克服信道相关性带来的不利影响。性能分析和仿真结果表明,特征波束角度空间调制方案可获得更多的波束组合数目,会明显改善译码性能和频谱效率。

浅析基于AI的信道信息预测在6G中的应用

深入探讨了基于AI的信道信息预测在6G通信系统中的应用,并从信道信息预测的信道模型、信道信息预测方案、数据收集方法、模型监控技术、仿真验证结果,以及基于AI的信道信息预测所面临的机会与挑战这6个维度展开分析。在信道信息预测方案的分析中,广泛探索了多种信道信息预测策略;在数据收集方面,详细分析了不同参考信号传输类型及数据收集范围的影响;在模型监控方面,讨论了3种可能的监控方式。此外,通过仿真验证,充分展示了基于AI的信道信息预测技术的有效性和优越性。

带有双球面摆和变绳长效应的桥式起重机轨迹规划

带有双球面摆和变绳长效应的桥式起重机具有多输入多输出以及欠驱动的动力学特性,目前仍缺乏有效的控制策略.在台车移动、桥架移动、负载升降同步作业过程中,吊钩和负载两级球面摆动特性更为复杂,各状态量之间的非线性耦合关系更强,桥式起重机的防摆控制更具挑战性.不仅如此,现有方法无法保证桥式起重机系统全状态量的暂态控制性能.为解决上述问题,提出一种基于多项式的优化轨迹规划方法.首先,在未进行近似简化的前提下,使用拉格朗日方法建立带有双球面摆和变绳长效应的7自由度(Seven degree-of-freedom,7-DOF)桥式起重机的精确动力学模型.在此基础上,构造一组包含各状态量的辅助信号,将施加在台车、桥架、绳长以及吊钩、负载摆动上的约束转化为对辅助信号的约束,从而将桥式起重机的轨迹规划问题转化为与辅助信号相关的时间优化问题,并使用二分法求解.该轨迹规划方法不仅缩短了吊运时间,而且确保了全状态量满足约束条件.最后,仿真结果证明了动力学模型的准确性和轨迹规划方法的有效性.

基于多智能体深度强化学习的解耦控制方法

[目的]在现代工业生产过程中,实现复杂非线性多输入多输出系统的解耦控制对于生产过程的操作和优化都具有至关重要的意义.[方法]本文基于多智能体深度确定性策略梯度(MADDPG)算法,提出了一种解决复杂非线性多输入多输出系统解耦控制问题的设计方案,并通过连续搅拌反应过程的解耦控制仿真计算,验证了设计方案的有效性.[结果]验证结果表明:本文所提出的方案能够同时对连续搅拌反应过程中反应温度、产物摩尔流量两个被控量的设定控制目标进行跟踪调节,且在同样的控制目标下,该设计方案比单智能体方案和PID(proportional-integral-derivative control)控制方案都具有更好的稳定性与更小的稳态控制误差.[结论]仿真结果表明:针对复杂非线性多输入多输出系统的解耦控制问题,多智能体强化学习算法能够在不依赖于过程模型的基础上,实现复杂非线性多输入多输出系统的解耦控制,并保证较好的控制性能.

基于逆系统解耦的三电平Buck变换器反步滑模控制

多电平变换器因其可以降低开关管承受的电压应力、减小滤波电感和滤波电容体积,被广泛应用在直流微电网中。三电平Buck变换器的飞跨电容电压和输出电压存在耦合,是一个多输入多输出、强耦合的非线性系统。针对这一问题,提出一种逆系统解耦反步滑模控制方法。采用逆系统方法实现输出电压控制和飞跨电容电压控制的解耦,采用反步滑模法控制保证输出电压的稳定性和鲁棒性,通过状态反馈控制将飞跨电容电压平衡在输入电压的1/2处。仿真和实验结果表明,所提控制策略能使得飞跨电容电压和输出电压均具有良好的稳态和动态特性。

面向模分复用系统的遗传-MIMO均衡参数优化技术

在模分复用系统的数字信号处理单元中,多输入多输出(Multi-Input Multi-Output,MIMO)均衡技术可用来补偿由各类模式相关噪声引起的信号误码率(Bit Error Rate,BER)劣化问题.而MIMO均衡算法的工作性能严重依赖于步长因子μ以及抽头数K,因此在固化均衡器之前,确定MIMO均衡算法中μ-K参数组合的最优值至关重要.为提高参数优化效率,提出了一种基于遗传算法(Genetic Algorithm,GA)的MIMO均衡参数优化方案,即遗传-MIMO(GAMIMO),在保证最小BER输出的同时降低参数优化过程所需的计算开销.为验证GA-MIMO的工作性能,构建了基于10 km六模光纤的点对点通信实验系统,使用新方案补偿并行通信的六路数据,并与最速下降法和迭代算法进行性能比较.实验结果表明,所提GA方案可实现MIMO均衡中最高99.98%的最优μ-K参数命中率,且GA-MIMO算法的全局搜索性使其相比于最速下降法和迭代算法可最多分别节省86.14%和90.3%的均衡算法调用次数,有效降低了确定最优μ-K参数组合时的计算开销.

多用户大规模MIMO-OTFS信道参数估计方法

高速移动环境会导致信道的双弥散效应,给无线通信系统带来巨大挑战。正交时频空间(orthogonal time frequency space,OTFS)调制通过将时-频域的双弥散信道转换为时延-多普勒域的平坦衰落信道,能够有效缓解双弥散信道带来的频率和时间选择性衰落的影响。针对多用户大规模多输入多输出(multiinput multi-output,MIMO)OTFS系统中的信道参数估计问题,通过对多天线信道结构特征进行深入分析,将用户与基站间的信道建模为稀疏结构模型。将大规模MIMO信道划分为多个群组,设计了适用于多用户大规模MIMO-OTFS系统的导频图案,提出了基于群组块共稀疏阈值结构化贝叶斯学习信道估计算法。利用估计得到的信道状态信息设计了分数多普勒频移、到达角度等信道参数估计方法,从而进一步感知用户状态。仿真结果表明,提出的信道参数估计算法具有更高的估计精度和系统频谱效率。

基于深度学习的RCF-mmMIMO系统信道估计算法

针对无蜂窝毫米波大规模多输入多输出(CF-mmMIMO)系统信道估计存在的计算复杂度高、导频开销大等问题,提出一种基于深度学习的可重构智能表面(RIS)辅助CF-mmMIMO(RCF-mmMIMO)系统的信道估计算法。该算法引入RIS替代一部分毫米波接入点(AP),增大CF-mmMIMO下的毫米波覆盖范围和信号强度,在一定程度上解决无蜂窝架构下毫米波AP数量众多硬件成本高的问题。对同一AP上不同组天线的RIS级联信道映射关系的存在性进行证明,并通过FCNN神经网络对该信道映射进行建模,显著降低无蜂窝系统中央处理单元处理信道估计时高计算量和高导频开销。仿真实验表明,信道估计算法能够在低计算复杂度、低导频开销下有效提高信道估计的准确性,显著提高系统传输效率。

An Industrial Intrusion Detection Method Based on Hybrid Convolutional Neural Networks with Improved TCN

Network intrusion detection systems(NIDS)based on deep learning have continued to make significant advances.However,the following challenges remain:on the one hand,simply applying only Temporal Convolutional Networks(TCNs)can lead to models that ignore the impact of network traffic features at different scales on the detection performance.On the other hand,some intrusion detection methods considermulti-scale information of traffic data,but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features.To address both of these issues,we propose a hybrid Convolutional Neural Network that supports a multi-output strategy(BONUS)for industrial internet intrusion detection.First,we create a multiscale Temporal Convolutional Network by stacking TCN of different scales to capture the multiscale information of network traffic.Meanwhile,we propose a bi-directional structure and dynamically set the weights to fuse the forward and backward contextual information of network traffic at each scale to enhance the model’s performance in capturing the multi-scale temporal features of network traffic.In addition,we introduce a gated network for each of the two branches in the proposed method to assist the model in learning the feature representation of each branch.Extensive experiments reveal the effectiveness of the proposed approach on two publicly available traffic intrusion detection datasets named UNSW-NB15 and NSL-KDD with F1 score of 85.03% and 99.31%,respectively,which also validates the effectiveness of enhancing the model’s ability to capture multi-scale temporal features of traffic data on detection performance.