Distributed nonlinear model predictive control based on contraction theory

A novel distributed model predictive control algorithm for continuous‐time nonlinear systems is proposed in this paper. Contraction theory is used to estimate the prediction error in the algorithm, leading to new feasibility and stability conditions. Compared to existing analysis based on Lipschitz continuity, the proposed approach gives a distributed model predictive control algorithm under less conservative conditions, allowing stronger couplings between subsystems and a larger sampling interval when the subsystems satisfy the specified contraction conditions. A numerical example is given to illustrate the effectiveness and advantage of the proposed approach.     

Distributed MPC for Reconfigurable Architecture Systems via Alternating Direction Method of Multipliers

This paper investigates the distributed model predictive control (MPC) problem of linear systems where the network topology is changeable by the way of inserting new subsystems, disconnecting existing subsystems, or merely modifying the couplings between different subsystems. To equip live systems with a quick response ability when modifying network topology, while keeping a satisfactory dynamic performance, a novel reconfiguration control scheme based on the alternating direction method of multipliers (ADMM) is presented. In this scheme, the local controllers directly influenced by the structure realignment are redesigned in the reconfiguration control. Meanwhile, by employing the powerful ADMM algorithm, the iterative formulas for solving the reconfigured optimization problem are obtained, which significantly accelerate the computation speed and ensure a timely output of the reconfigured optimal control response. Ultimately, the presented reconfiguration scheme is applied to the level control of a benchmark four-tank plant to illustrate its effectiveness and main characteristics.      

改进交替方向乘子法求解冷水机组负荷分配群智能优化问题

群智能控制系统中的多台冷水机组负荷优化分配问题是一个多块优化问题,传统分布式方法难以获得其收敛解.文中将交替方向乘子法(ADMM)引入冷水机组负荷分配群智能优化问题中,并通过一种有效的高斯罚函数(GPF)更新策略改进了交替方向乘子法收敛特性.同时,建立了一种基于ADMM–GPF–GBS双层分布式计算框架的冷水机组负荷优...     

A general dissipativity constraint for feedback system design,with emphasis on MPC

A “general dissipativity constraint” (GDC) is introduced to facilitate the design of stable feedback systems. A primary application is to MPC controllers when it is preferred to avoid the use of “stabilising ingredients” such as terminal constraint sets or long prediction horizons. Some very general convergence results are proved under mild conditions. The use of quadratic functions, replacing GDC by “quadratic dissipativity constraint” (QDC), is introduced to allow implementation using linear matrix inequalities. The use of QDC is illustrated for several scenarios: state feedback for a linear time‐invariant system, MPC of a linear system, MPC of an input‐affine system, and MPC with persistent disturbances. The stability that is guaranteed by GDC is weaker than Lyapunov stability, being “Lagrange stability plus convergence.” Input‐to‐state stability is obtained if the control law is continuous in the state. An example involving an open‐loop unstable helicopter illustrates the efficacy of the approach in practice.     

Distributed tracking control of networked chained systems

This paper considers distributed tracking control of multiple nonholonomic chained systems using neighbours’ information. With the aid of the cascade structure of each system and properties of persistently excited signals, distributed state feedback tracking controllers and distributed output feedback tracking controllers are proposed such that the tracking errors exponentially converge to zero. To show applications of the proposed results, formation control of wheeled mobile robots is considered. Distributed controllers are obtained with the aid of the proposed theorems. Simulation results verify the effectiveness of the proposed results.     

一类连续的K-means 等价聚类模型及其优化算法

聚类作为一种非监督学习方法是数据科学中重要的研究内容.K-means是一种基于划分的聚类算法,一般是利用启发式算法求解一个离散的NP问题.为增强K-means在大数据问题中的应用性,从聚类矩阵的属性出发,设计了一类非凸连续的K-means等价聚类优化模型,并利用ADM M框架给出了该等价模型的快速优化算法.数值实验结果表明了该模型及其优化算法在大数据聚类中的准确性和高效性.此外,还讨论了该模型的性质及等价性问题.     

多特征融合与交替方向乘子法的行人再识别

目的 由于行人图像分辨率差异、光照差异、行人姿态差异以及摄像机视角和成像质量差异等原因,导致同一行人在不同监控视频中的外观区别很大,给行人再识别带来了巨大挑战。为提高行人再识别的准确率,针对以上问题,提出一种基于多特征融合与交替方向乘子法的行人再识别算法。方法 首先利用图像增强算法对所有行人图像进行处理,减少因光照变化产生的影响,然后把处理后的图像进行非均匀分割,同时使用特定区域均值法提取行人图像的HSV和LAB颜色特征以及SILTP（scale invariant local ternary pattern）纹理特征和HOG（histogram of oriented gradient）特征,融合多种特征得到行人图像对的整体与局部相似度度量函数并结合产生相似度函数,最后使用交替方向乘子优化算法更新出最优的测度矩阵实现行人再识别。结果 在VIPeR、CUHK01、CUHK03和GRID这4个数据集上进行实验,其中VIPeR、CUHK01和GRID 3个数据集Rank1（排名第1的搜索结果即为待查询人的比率）分别达到51.5%、48.7%和21.4%,CUHK03 手动裁剪和检测器检测数据集Rank1分别达到62.40%和55.05%,识别率有了显著提高,具有实际应用价值。结论 提出的多特征融合与交替方向乘子优化算法,能够很好地描述行人特征,迭代更新出来的测度矩阵能够很好地表达行人之间的距离信息,较大幅度地提高了识别率。该方法适用于大多数应用场景下的行人再识别。尤其是针对复杂场景下静态图像行人再识别问题,在存在局部遮挡、光照差异和姿态差异的情况下也能保持较高的识别正确率。     

基于标准神经网络模型的非线性系统分布式无线网络化控制

针对采用标准神经网络模型(SNNM)描述的非线性系统,提出一种基于无线控制网络(WCN)的全分布式控制方法.采用置信因子模拟WCN中无线通信链路的不确定性,利用Lyapunov理论和Lur’e系统方法,将无线网络化控制系统(WNCS)的稳定性分析转化为一个具有线性矩阵不等式(LMI)约束的凸优化问题;使用CVX工具包求解该凸优化问题,得到了保证闭环系统全局渐近稳定的WCN配置参数.仿真结果验证了所提出控制策略的正确性和有效性.     

An optimal control based approach to designing plantwide control system architectures

An approach to designing decentralized plantwide control system architectures is presented. The approach is based on splitting the optimal controller gain matrix that results from solving an output optimal control problem into feedback and feedforward parts. These two parts are then used to design and evaluate decentralized control systems. Results for the application of the methodology to a realistic, 4 by 4 reactor with recycle process are given. For this system, the optimal control based approach suggests feedback pairings that are significantly different than those suggested by the steady state RGA. The approach presented can give an indication if MPC is preferred over a decentralized approach to plantwide control. Comparison of the results produced by the best decentralized plantwide system and a model predictive control system are presented.     

基于局部和非局部正则化的图像压缩感知

基于低秩正则化的非局部低秩约束(Nonlocal low-rank regularization, NLR)算法利用相似块的结构稀疏性,获得了目前最好的重构结果。但是它仅仅利用了图像的非局部信息,忽略了图像像素间的局部信息,不能有 效地重建图像的边缘,同时Logdet函数不能很好地替代矩阵秩,因为它跟真实解之间存在着不可忽视的差距。因此,本文提出了一种基于局部和非局部正则化的压缩感知图像重建方法,同时考虑图像的非局部低秩性和图像像素的局部稀疏梯度性。选择利用Schatten-p 范数来替代矩阵秩,同时选择交替方向乘子算法求解产生的非凸优化问题。实验 结果表明,与传统的稀疏性先验重建算法和NLR算法相比,本文算法能够获得更高的图像重构质量。     

Multi-rate dissipativity based control of process networks

An approach to multi-rate distributed control design for process networks is presented, where the local measurements, local control and controller communication are allowed to operate at different sampling rates. Dissipative systems theory is used to facilitate stability and performance analysis of the process network, based upon dynamic supply rates which have been lifted into a global sampling rate. Quadratic difference forms are used as supply rates and storage functions, leading to less conservative stability and performance conditions as compared to classical types of supply rates. These theoretical results are illustrated by a case study of a heat exchanger network.     

基于单神经元自适应PSD算法的分布式智能灯光控制

针对已有灯光控制算法无法应对系统模型变化的问题,提出将单神经元自适应PSD（proportion sum differential）算法应用于分布式智能灯光控制。利用单神经元自适应PSD算法的自学习能力,控制器根据系统误差实时修改参数,并与无线传感器/执行器网络中的分簇机制相结合,形成了一套完整的自适应分布式智能灯光控制算法。并以基于无线传感器/执行器网络的灯光控制实验平台为被控对象,设计控制器并进行了仿真研究。仿真实验表明,当系统模型发生改变,与已有的分布式PID灯光控制算法相比,本文的控制算法具有更好的控制效果、鲁棒性更强。     

基于模糊控制技术的分布式入侵检测系统

模糊控制技术在工业控制等众多领域已得到了广泛的应用,其理论和技术已经较为成熟。为进一步提高网络系统的安全性,将模糊控制理论应用于分布式入侵检测系（distributed intrusion detection system,DIDS）中,并设计出具体的应用数据模型。通过模糊控制技术的引入,可有效提高分布式入侵检测系统的预报可信度和系统的辅助决篆能力。     

基于自适应最优控制的有限时间微分对策制导律

针对固定末端时刻拦截机动目标的制导系统,本文首先构建了非线性有限时间微分对策框架,将导弹拦截非线性系统的最优问题转化为一般非线性系统的最优控制问题,并通过自适应动态规划算法(adaptive dynamic programming, ADP)获得近似最优值函数与最优控制策略.为了有效实现该算法,本文利用一个具有时变权值和激活函数的评价网络来逼近Hamilton-Jacobi-Isaacs(HJI)方程的解,并在线更新.通过李雅普诺夫法来证明本文提出的控制策略可保证闭环微分对策系统稳定性和评价网络权值近似误差的有界性.最后给出一个非线性导弹拦截目标系统的仿真例子验证了该方法的可行性和有效性.     

ADMM稀疏非负矩阵分解语音增强算法

提出一种基于交替方向乘子法的（Alternating Direction Method of Multipliers,ADMM）稀疏非负矩阵分解语音增强算法,该算法既能克服经典非负矩阵分解（Nonnegative Matrix Factorization,NMF）语音增强算法存在收敛速度慢、易陷入局部最优等问题,也能发挥ADMM分解矩阵具有的强稀疏性。算法分为训练和增强两个阶段：训练时,采用基于ADMM非负矩阵分解算法对噪声频谱进行训练,提取噪声字典,保存其作为增强阶段的先验信息;增强时,通过稀疏非负矩阵分解算法,从带噪语音频谱中对语音字典和语音编码进行估计,重构原始干净的语音,实现语音增强。实验表明,该算法速度更快,增强后语音的失真更小,尤其在瞬时噪声环境下效果显著。     

An industrial and academic perspective on plantwide control

The purpose of this paper is to stress the importance of selecting the right plantwide control structure and the need for a formalized approach that can encompass the many issues that arise in plantwide control design. Since the concept of process control design based on a holistic view of the process came about, the variety of procedures and approaches to the design problem has illustrated the difficulty of a unified approach. Using examples, the need and advantages of using a systematic approach based on considering the plant economics are highlighted. The examples deal with disturbance rejection, throughput maximization and economic optimization of plants consisting of parallel units.     

Implications of dissipativity,inverse optimal control,and stability margins for nonlinear stochastic feedback regulators

In this paper, we derive stability margins for optimal and inverse optimal stochastic feedback regulators. Specifically, gain, sector, and disk margin guarantees are obtained for nonlinear stochastic dynamical systems controlled by nonlinear optimal and inverse optimal Hamilton‐Jacobi‐Bellman controllers that minimize a nonlinear‐nonquadratic performance criterion with cross‐weighting terms. Furthermore, using the newly developed notion of stochastic dissipativity, we derive a return difference inequality to provide connections between stochastic dissipativity and optimality of nonlinear controllers for stochastic dynamical systems. In particular, using extended Kalman‐Yakubovich‐Popov conditions characterizing stochastic dissipativity, we show that our optimal feedback control law satisfies a return difference inequality predicated on the infinitesimal generator of a controlled Markov diffusion process if and only if the controller is stochastically dissipative with respect to a specific quadratic supply rate.     

基于极大熵差分进化混合算法求解非线性方程组*

针对非线性方程组,给出了一种新的算法——极大熵差分进化混合算法。首先把非线性方程组转换为一个不可微优化问题;然后用一个称之为凝聚函数的光滑函数直接代替不可微的极大值函数,从而可把非线性方程组的求解转换为无约束优化问题,利用差分进化算法对其进行求解。计算结果表明,该算法在求解的准确性和有效性均优于其他算法。     

基于差分进化的伺服电机自抗扰控制

针对传统PID、模糊控制算法设计简单,无法对实际系统进行扰动补偿,导致外部未知干拢影响伺服电机的控制,提出一种基于差分进化的伺服电机自抗扰控制技术;通过对自抗扰控制中参数kp、kd的动态整定,对比了基于差分进化算法(DE ADRC)、自抗扰控制算法(ADRC)、PID算法的跟踪误差,结果显示DE-ADRC算法具有明显优势,较ADRC算法、PID算法的跟踪误差分别降低了54％和49％;进一步利用蒙特卡罗统计学算法对控制对象进行仿真,以超调量、调节时间和误差绝对值积分为控制性能评估指标证明所提出的DE-ADRC算法比PID算法和ADRC算法具有更强的鲁棒性和系统抗干扰能力,为伺服电机在实际工程中抗干扰能力的增强提供了技术支持.     

多任务正则极限学习机的研究与应用

MT-ELM通过隐含层共享不同任务间的数据特性实现多任务学习，但MT-ELM忽略任务间关联程度的差异以及存在的过拟合问题，为此提出基于MT-RELM软测量建模方法。首先，利用RELM解决过拟合问题；其次，考虑任务之间关联度的差异，基于相关性较强的任务其权值向量也较相似的假设，在每个任务输出权值的基础上加入约束条件，利用此约束条件表示任务间的相关程度；最后，利用ADMM算法迭代求解得到MT-RELM的模型参数。基于合成数据集与湿式球磨机数据集的结果表明，此算法可有效地提高模型的预测精度以及泛化能力。