楼板系统振动的多输入多输出主动优化控制

轻质楼板系统易受居住者步行、跳舞和有氧运动之类活动激励产生人体不适的振动。如何使用多个激励器、传感器即多输入多输出控制策略抑制楼板振动是一个新挑战,也是近年来国内外研究热点。本文建立起一个新的适于楼板振动控制的多输入多输出主动优化控制策略与算法,可同时优化确认激励器/传感器对的放置位置与每个通道的反馈增益;提出一个新颖的综合性评价性能指标PI,不仅包含楼板系统振动能量,而且包含输入的控制能量,对该指标最小化,不仅实现系统振动能量的最小化,而且实现输入能量的最小化,这将带来激励器经济可行的实现。模拟计算结果表明,提出的新控制策略及其算法能十分有效、快速地抑制楼板系统振动响应。     

Downlink transmission rate-control strategies for closed-loop multiple-input multiple-output systems

A novel downlink transmission rate-control and feedback reduction strategy for closed-loop multiple-input multiple-output (MIMO) multiple-input multiple-output wireless systems is presented. Unlike conventional systems that use signal to interference plus noise ratio at the receiver as an indicator of channel quality, we propose using instantaneous MIMO capacity as an indicator for the downlink transmission rate-control. A set of instantaneous capacity thresholds is first chosen such that the expected weighted capacity loss because of thresholding effects are minimised. While computing the thresholds, we also consider the quality of service and weight function to meet different traffics and user needs. Then a set of codebooks can be constructed minimising the overall capacity loss with given quality of service constraint. Simulation results show that, with only four data rate-control bits, our algorithm gives only 12% capacity loss in 4 times 4 MIMO systems and almost twice better than the current IS-856 standard in single-input single-output systems. In case of 5-bit feedback scenario, the proposed algorithm outperforms conventional systems by minimising instantaneous capacity loss.     

Adaptive opportunistic fair scheduling in power-controlled code division multiple access systems

The authors treat the multiuser scheduling problem for practical power-controlled code division multiple access (CDMA) systems under the opportunistic fair scheduling (OFS) framework. OFS is an important technique in wireless networks to achieve fair and efficient resource allocation. Power control is an effective resource management technique in CDMA systems. Given a certain user subset, the optimal power control scheme can be derived. Then the multiuser scheduling problem refers to the optimal user subset selection at each scheduling interval to maximise certain metric subject to some specific physical-layer constraints. The authors propose discrete stochastic approximation algorithms to adaptively select the user subset to maximise the instantaneous total throughput or a general utility. Both uplink and downlink scenarios are considered. They also consider the time-varying channels where the algorithm can track the time-varying optimal user subset. Simulation results to show the performance of the proposed algorithms in terms of the throughput/ utility maximisation, the fairness, the fast convergence and the tracking capability in time-varying environments are presented.     

Optimisation of limited feedback design for heterogeneous users in multi-antenna downlinks

The authors study the problem of limited channel information feedback in multiple-input single-output (MISO) broadcast channels serving heterogeneous users. The heterogeneous users have different feedback channel capacities because of their physical constraints and limitations. Our objective is to design an optimised limited feedback strategy for multiple users by using a set of multi-resolution codebooks such that, under varying and quantised channel information feedback, the multiuser system can maximise its achievable sum rate. By exploiting the receive antenna combining technique, the authors further generalise the proposed scheme to the multiuser beamforming case with multi-antenna users. Finally, the authors verify the proposed scheme by numerical methods. Simulation results show that the sum rate performance for heterogeneous users can be effectively improved by using the proposed limited feedback scheme, which is optimised according to only statistic channel information of users.     

Minimax optimal control of uncertain quasi-integrable Hamiltonian systems with time-delayed bounded feedback

A minimax optimal control strategy for uncertain quasi-integrable Hamiltonian systems with time-delayed bounded feedback control is proposed. First, a quasi-integrable Hamiltonian system with time-delayed bounded control forces and uncertain excitation and system parameters is converted into a set of Itô stochastic differential equations without time delay. Then, the partially averaged Itô stochastic differential equations for the energy processes are derived by using the stochastic averaging method for quasi-integrable Hamiltonian systems. For these equations together with an appropriate performance index, a worst-case optimal control strategy is derived via solving a stochastic differential game problem. The worst-case disturbances and the optimal bounded controls are obtained by solving a Hamilton–Jacobi–Isaacs (HJI) equation. Finally, two examples are worked out in detail to illustrate the application and effectiveness of the proposed method.     

Adaptive subchannel allocation in multiuser multicarrier systems

The problem of multiuser scheduling in multicarrier (MC) systems under practical physical-layer constraints and implementations is considered. Subchannel allocation is an important resource assignment issue in multiuser MC systems. The multiuser scheduler is decoupled into a multiuser selector and a subchannel allocator, which result in a sub-optimum multiuser scheduler with significantly reduced computational complexity. Given an active user subset and a channel set, the multiuser scheduling problem then refers to the optimal subchannel allocation to maximise the instantaneous system throughput subject to certain fairness constraints. Efficient adaptive algorithms are developed for optimal subchannel allocation. The extension of the algorithms for tracking the time-varying optimum, which occurs in non-stationary environments, is also addressed. Simulation results are presented to demonstrate the performance of the proposed algorithms in terms of the throughput maximisation, the fast convergence, the excellent tracking capability in time-varying environments, the achievable throughput of the proposed multiuser scheduler as well as the long-term fairness     

基于部分可观察马尔可夫决策过程的受控无线网络系统动态资源分配

研究了受控无线网络的动态资源分配。针对传统无线通信传输模型的局限性随着无线通信系统架构的发展日益凸显的问题,提出了一种引入反馈控制策略的受控无线网络模型。该模型结合部分可观察马尔可夫决策过程(POMDP),将用户接收功率与数据传输误码率作为反馈观测对象,对通信小区内基站天线开启数与用户接入数进行动态资源最优匹配。仿真结果表明,这种方法能够有效提升系统传输能效性与可靠性,降低传输误码率,改善系统资源动态匹配控制性能。     

基于二阶矩阵微分方程的机械振动系统线性二次型调节器设计

本文讨论机械振动系统线性二次型状态调节器（LQR）问题，直接针对系统二阶运动微分方程，性能指标为一个依赖于二阶导数的泛函。由欧拉-拉格朗日方程得出一个系统矩阵增广的二阶线性微分方程，指出该方程稳定的特征对就是最优控制振动系统闭环特征对，并给出求解最优控制状态反馈矩阵的方法，另外，由本文方法还可得出基于速度和加速度反馈的最优控制反馈矩阵。这里不涉及求解代数矩阵Riccati方程。     

Fast antenna subset selection algorithms for multiple-input multiple-output relay systems

The antenna subset selection technique balances the performance and hardware cost in the multipleinput multiple-output (MIMO) systems, and the problems on the antenna selection in MIMO relay systems have not been fully solved. This paper considers antenna selection on amplify-and-forward (AF) and decode-andforward (DF) MIMO relay systems to maximise capacity. Since the optimal antenna selection algorithm has high complexity, two fast algorithms are proposed. The selection criterion of the algorithm for AF relay is to maximise a lower bound of the capacity, but not the exact capacity. This criterion reduces algorithmic complexity. The algorithm for DF relay is an extension of an existing antenna subset selection algorithm for one-hop MIMO systems. The authors show the derivations of the algorithms in detail, and analyse their complexity in terms of numbers of complex multiplications. Simulation results show that the proposed algorithms for both cases achieve comparable performance to the optimal algorithm under various conditions, and have decreased complexity.     

Joint rate and power adaptation for radio resource management in uplink wideband code division multiple access systems

The benefits of adaptive joint power control and rate allocation for uplink transmission in a wideband code division multiple access cellular system are investigated. Closed-loop power control (CLPC), to adaptively adjust the transmit power, has the effect of maintaining a target signal-to-interference ratio and bit error rate (BER) performance. On the other hand, rate adaptation requires less transmit power, although the BER performance may be poorer. The authors differentiate the power update interval from the data rate update interval, analyse and evaluate the performance of two joint rate/power adaptation algorithms in a fading environment: optimal spreading factor-power control (OSF-PC) and greedy rate packing-power control (GRP-PC). Numerical results show that GRP-PC exhibits superior throughput performance compared with other three adaptation schemes. CLPC alone exhibits throughput and BER performances comparable to those of the OSF-PC scheme, but consumes a significantly higher amount of transmit power. Rate adaptation only is not efficient in enhancing throughput, but its power consumption is minimal.     

风电机组有功功率集散优化控制系统研究

研究了一种风电机组有功功率集散优化控制策略,并基于此提出一套风电机组有功功率集散优化控制系统开发方案,该系统架设于风电场自动发电控制（automatic generation control,AGC）系统以及风电机组机群能量管理系统（energy management system,EMS）之间,不改变AGC系统与EMS的原有架构,并且可综合考虑风机机群上网电价差异、设备稳定性、发电能力及寿命等因素,以利用上网电价较高、稳定性较好的发电设备为目的,从而实现风电机组有功功率的集散优化控制。     

受宽带随机激励的最优反馈控制

文章提出了基于平稳响应的随机最优反馈控制策略，研究了受宽带随机激励的Hamilton系统的最优反馈控制。首先应用拟Hamilton系统随机平均法建立平均后的Ito随机微分方程；接着求解相应的Fokker—Plank—Kol—mogorov（FPK）方程得到系统精确平稳解；然后将控制力的均方值和Hamilton能量均值结合作为系统控制指标，得出最优反馈控制规律；最后用两个例子详细说明了这一控制方法的实施过程的有效性，数字模拟结果与理论结果完全吻合。     

基于最优极点配置的混合轴承-转子系统振动控制

针对电液伺服控制的主动混合滑动轴承,研究了基于最优极点配置的转子振动控制方法和策略.根据扰动压力法求解非线性Reynolds方程及流量平衡方程得到轴承和伺服控制系统线性化的动态特性系数,用以建立系统线性状态空间模型;给出了极点配置和最优控制相结合的状态空间反馈控制策略,以克服多输入系统常规极点配置方法状态反馈不唯一的缺陷.计算结果表明,由于转子不平衡或同步激励引起的转子振动得到了有效抑制,在外部突发激励作用下,转子也具有优越的动态响应特性,验证了控制方法的有效性.     

Throughput fairness and efficiency of link adaptation techniques in wireless networks

Link adaptation techniques aim to maximise the quality of service and resource utilisation in wireless networks. However, fairness must be taken into consideration, particularly, in low-mobility environments where the channel dynamic variation is small. The authors propose and analyse three link adaptation techniques [using joint power control (PC) and adaptive coding and modulation (ACM)] for fairness enhancement. In the first technique, called aggregate throughput maximisation with fairness constraint, the authors formulate the fairness problem as a constrained optimisation problem where the authors try to maximise the aggregate throughput subject to the throughput fairness constraint. In order to solve the optimisation problem, the authors convert the constrained optimisation problem to an unconstrained optimisation one using the penalty method. Then, the unconstrained optimisation problem is solved using the steepest descent technique. The second techniques, called individual throughput balancing, tries to equalise the individual throughput by using a higher throughput level for disadvantaged users and using a lower throughput level for advantaged users. Finally, the third technique, called adaptive virtual maximum power constraint, uses virtual maximum power cap, which is lower than the real maximum power cap. The virtual maximum power cap of each user is variable and it adapts based on the user's individual throughput to compensate disadvantaged users. The authors analyse the three proposed techniques in terms of the throughput fairness and the throughput efficiency and compare them with three basic link adaptation techniques (PC, ACM, and joint PC and ACM). The three proposed techniques are shown to be able to enhance the fairness with different degrees and with different levels of aggregate throughput degradation.     

Order sequencing and capacity balancing in synchronous manufacturing

Synchronous manufacturing aims at achieving the benefits of intermittent production lines in production situations that operate without lines. Benefits such as short and constant throughput times and predictable capacity loading can be acquired through an appropriate design of the synchronous manufacturing system and its control system. The order release mechanism is an essential part of this control system. It determines the sequence in which orders are released to the shop floor. As orders may differ in the amount and distribution of their capacity requirements over subsequent production stages, total capacity load may vary over time. If the available capacity per period is not flexible, capacity balancing becomes an issue in the order release decision. In practice, heuristics or rules of thumb are used to solve this problem, but their effectiveness is questioned. This paper examines the effectiveness of some new heuristics that are based on insights from assembly system design and work load control, and compare their performance with an optimal solution approach. The approaches are evaluated in a rolling schedule environment, and under different levels of capacity fluctuations and problem sizes. The results show that the performance of the heuristic solutions deteriorates if capacity fluctuations between the stages increase. If we measure both the amount and frequency of shortages over a long period of time in a rolling schedule environment, a quite simple rule that only takes the available capacity during the first stage into account outperforms more intelligent rules.     

Three-dimensional channel modelling using spherical statistics for multiple-input multiple-output systems

Recently, the third generation partnership standards bodies (3GPP/3GPP2) have defined a two-dimensional channel model for multiple-input multiple-output (MIMO) systems, where the propagating plane waves are assumed to arrive only from the azimuthal direction and therefore not include the elevation domain. As a result of this assumption, the derived angle-of-arrival (AoA) distribution is characterised only by the azimuth direction of these waves. The AoA distribution of multipaths is implemented with a novel three-dimensional approach. The von Mises- Fisher (VMF) probability density function is used to describe their distribution within the propagation environment in both azimuth and co-latitude. More specifically, the proposed model uses a mixture of VMF distributions. A mixture can be composed of any number of clusters and this is clutter specific. The parameters of the individual cluster of scatterers within the mixture are derived and an estimation of those parameters is achieved using the spherical K-means algorithm and also the expectation maximisation algorithm. Statistical tests are provided to measure the goodness of fit of the proposed model. The results indicate that the proposed model fits well with MIMO experimental data obtained from a measurement campaign in Germany.     

Resource Allocation and Power Control Policy for Device-toDevice Communication Using Multi-Agent Reinforcement Learning

Device-to-Device (D2D) communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity. In this paper, we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput. Firstly, we treat each D2D pair as an independent agent. Each agent makes decisions based on the local channel states information observed by itself. The multi-agent Reinforcement Learning (RL) algorithm is proposed for our multi-user system. We assume that the D2D pair do not possess any information on the availability and quality of the resource block to be selected, so the problem is modeled as a stochastic non-cooperative game. Hence, each agent becomes a player and they make decisions together to achieve global optimization. Thereby, the multi-agent Q-learning algorithm based on game theory is established. Secondly, in order to accelerate the convergence rate of multi-agent Q-learning, we consider a power allocation strategy based on Fuzzy Cmeans (FCM) algorithm. The strategy firstly groups the D2D users by FCM, and treats each group as an agent, and then performs multi-agent Q-learning algorithm to determine the power for each group of D2D users. The simulation results show that the Q-learning algorithm based on multi-agent can improve the throughput of the system. In particular, FCM can greatly speed up the convergence of the multi-agent Q-learning algorithm while improving system throughput.     

On the capacity of orthogonalised correlated MIMO channels under different adaptive transmission techniques

Orthogonal space-time block codes (STBCs) are known to orthogonalise the multiple-input multiple-output (MIMO) wireless channel, thus reducing the space-time vector detection to a simpler scalar detection problem. The capacity of STBCs over correlated Rayleigh and Ricean flat-fading MIMO channels under different adaptive transmitting techniques is studied. Three adaptive schemes known as optimal power and rate allocation, total channel inversion with fixed rate policy and its truncated variant are studied. Taking into account the effect of channel correlation, closed-form expressions are obtained for the capacity of orthogonalised Rayleigh and Ricean MIMO channels under these adaptive transmission techniques in order to avoid Monte-Carlo simulations     

Coordinated Control of Sliding Mode and Hamiltonian for PWM Rectifier

Aiming at the PWM rectifier control strategy of sliding mode control,steady state performance weak Hamiltonian control dynamic tracking performance is poor,the coordinated compound control is proposed,the feedback linearization controller and sliding mode controller Hamiltonian system is obtained,and the design of a coordinated control strategy. In order to verify the accuracy of this method,MATLAB/Simulink is used for simulation analysis. The simulation results show that the composite control can achieve the coordinated dynamic rapid tracking and constant DC output and unit power factor operation,and satisfy the control requirements of the rectifier,effectively reducing the disturbance effect on the system. Compared with Hamiltonian control,the proposed method combines the advantages of the two methods,which have the fast tracking performance and excellent steady-state characteristics,and the research prospect is broad.     

THROUGHPUT MAXIMIZATION IN FLEXIBLE MANUFACTURING SYSTEMS

Maximizing the throughput (or revenue generation) rate has become one of die most important criteria in die design and management of Flexible Manufacturing Systems (FMSs). This paper develops dynamic part-allocation policies for FMSs having finite storage capacity at each work station. Maximizing the throughput rate means that the resulting queueing network model has a state-dependent arrival process; therefore, product-form solutions do not hold. Consequently, several alternative modeling approaches are described and formulated for deriving die optimal part-routing policies. One of these optimal policies is based on a new initiated-suspension part-routing strategy. This strategy results in a reduced load on the material handling system while increasing die expected throughput (or revenue generation) rates of the manufacturing work stations. We also propose several efficient closed-loop heuristic policies that exploit die response structure of die optimal policies. These heuristic policies are of practical significance because they are extremely easy to compute and to implement, while die resulting FMS performance is nearly optimal.