Rapid distributed model predictive control design using singular value decomposition for linear systems

The issue of model predictive control design of distribution systems using a popular singular value decomposition (SVD) technique is addressed. Namely, projection to a set of conjugate structure is dealt with in this paper. The structure of the resulting predictive model is decomposed into small sets of subsystems. The optimal inputs can be separately designed at each subsystem in parallel without any interaction problems. The optimal inputs can be directly obtained and the communication among the subsystems can be significantly reduced. In addition, the design of distribution model predictive control (DMPC) with constraints using the SVD framework is also presented. The unconstraint inputs are checked in parallel in the conjugate space. Without solving the QP problem of each subsystem, the suboptimal solution can be quickly obtained by selecting the bigger singular values and discarding the small singular values in the singular value space. The convergence condition of the proposed algorithm is also proved. Two case studies are used to illustrate the distribution control systems using the suggested approach. Comparisons between the centralized model predictive control method and the proposed DMPC method are carried out to show the advantages of the newly proposed method.     

加权有向图社区发现的子系统划分

提出一种基于加权有向图的社区发现子系统划分方法, 并应用于分布式状态估计设计. 针对一类复杂非线 性系统, 构建考虑连接边强度的加权有向图, 引入社区发现算法将复杂非线性系统划分成多个子系统. 同时考虑子 系统之间连接边的数量和有向图顶点之间的连接强度, 使得划分得到的子系统内部关联较强, 而子系统之间的耦合 强度较弱. 针对划分得到的子系统, 设计基于信息交互的分布式滚动时域估计算法, 并与已有的子系统划分方法对 比, 在相同的状态估计设定下, 所提出的子系统划分方法能够有效提高状态估计的性能.     

Boundary Control for a Flexible Inverted Pendulum System Based on a PDE Model with Input Saturation

This research considers the control problem of a flexible inverted pendulum system (FIPS) in the presence of input saturation. The model for a flexible inverted pendulum system (FIPS) is derived via the Hamilton principle. The FIPS model is divided into a fast subsystem and a slow subsystem via the singular perturbation method. We introduce an auxiliary system to deal with the input saturation of a fast subsystem. To stabilize the fast subsystem, a boundary anti‐windup control force is applied at the free end of the beam. It is proven that the closed‐loop subsystem is stable. For the slow subsystem, a sliding mode control method is employed to design a controller and a new decoupling method to design the sliding surface. Then it is shown that the slow subsystem is stable. Finally, simulation results are provided to confirm the efficacy of the proposed controller.     

An actuator fault reconstruction scheme for linear systems

This paper presents an actuator fault reconstruction scheme that can be classified into an algebraic decomposition approach in which the original system is decomposed into two subsystems: a fault-free subsystem and a fault-dependent one. From the result of such algebraic decomposition, the fault estimate is obtained as a set of combinational functions of the system input, the measurement output, the estimate of certain linear functionals involving the state of the fault-free subsystem and the first-order derivative of the measurement output. Here a functional observer and robust exact differentiators are adopted to provide the estimate of the linear functionals of the state of the fault-free subsystem and the derivative of the measurement output, respectively. The proposed method is verified through two numerical examples of seventh-order aircraft and fourth-order crane models.     

A robust distributed model predictive control algorithm

Although distributed model predictive control (DMPC) has received significant attention in the literature, the robustness of DMPC with respect to model errors has not been explicitly addressed. In this paper, a novel online algorithm that deals explicitly with model errors for DMPC is proposed. The algorithm requires decomposing the entire system into N subsystems and solving N convex optimization problems to minimize an upper bound on a robust performance objective by using a time-varying state-feedback controller for each subsystem. Simulations examples were considered to illustrate the application of the proposed method.     

Composite nonlinear feedback control for linear singular systems with input saturation

This paper investigates the composite nonlinear feedback (CNF) control technique for linear singular systems with input saturation. First, a linear feedback control law is designed for the step tracking control problem of linear singular systems subject to input saturation. Then, based on this linear feedback gain, a CNF control law is constructed to improve the transient performance of the closed-loop system. By introducing a generalized Lyapunov equation, this paper develops a design procedure for constructing the CNF control law for linear singular systems with input saturation. After decomposing the closed-loop system into fast subsystem and slow subsystem, it can be shown that the nonlinear part of the CNF control law only relies on slow subsystem. The improvement of transient performance by the proposed design method is demonstrated by an illustrative example.     

Data-driven process decomposition and robust online distributed modelling for large-scale processes

With the increasing attention of networked control, system decomposition and distributed models show significant importance in the implementation of model-based control strategy. In this paper, a data-driven system decomposition and online distributed subsystem modelling algorithm was proposed for large-scale chemical processes. The key controlled variables are first partitioned by affinity propagation clustering algorithm into several clusters. Each cluster can be regarded as a subsystem. Then the inputs of each subsystem are selected by offline canonical correlation analysis between all process variables and its controlled variables. Process decomposition is then realised after the screening of input and output variables. When the system decomposition is finished, the online subsystem modelling can be carried out by recursively block-wise renewing the samples. The proposed algorithm was applied in the Tennessee Eastman process and the validity was verified.     

Adaptive stabilization of nonlinear teleoperation systems: An SIIOS approach

In this paper, the adaptive control problem of nonlinear teleoperation system based on the point of view of state‐independent input‐to‐output stability is addressed. By intentionally introducing the switched filter systems, a new IOS‐based control framework based on subsystem decomposition is developed. By designing the proper nonlinear controller, the complete closed‐loop system is first modeled into two interconnected subsystems with some well‐defined auxiliary variables. Then utilizing the small gain theorem, the weakly state‐independent input‐to‐output stability of complete system can be derived by the stability of each subsystem. As an important extension, the proposed control scheme is also proved to be suitable for the control of the single‐master‐multi‐slave teleoperation systems. Finally, the numerical example is given to demonstrate the effectiveness. Copyright © 2016 John Wiley & Sons, Ltd.     

四叉树分解的图像插值

如何在图像插值的同时较好地保持图像的结构信息一直是图像插值的一个难点,提出了一种基于四叉树分解的图像插值方法,基本思想是：用四叉树分解将原始图像划分为一些像素值相近的子块,当分解区域为1×1,用最邻近插值方法,分解区域为2×2时,用双线性方法插值,其余情况用双立方插值扩充子块,再将扩充后的子块映射到输出图像。实验结果表明该方法能较好地保持图像的边缘,同时具有较高的效率,可以应用到图像、视频及医学图像的放大。     

Distributed model predictive control for plant-wide hot-rolled strip laminar cooling process

Since hot-rolled strip laminar cooling (HSLC) process is a large-scale, nonlinear system, a distributed model predictive control (DMPC) framework is proposed for computational reason and enhancing the precision and flexibility of control system. The overall system is divided into several interconnected subsystems and each subsystem is controlled by local model predictive control (MPC). These local MPCs cooperate with its neighbours through the scheme of neighbourhood optimization for the improvement of global performance. The state space representation of each subsystem’s prediction model is designed by finite volume method firstly, and then is linearized around the current operating point at each step to overcome the computational obstacle of nonlinear model. Moreover, since the strip temperature is measurable only at a few positions in water cooling section due to the difficult ambient conditions, an Extended Kalman Filter (EKF) is used to estimate the transient temperature of strip. Both simulation and experiment results prove the efficiency of the proposed method.     

An iterative scheme for distributed model predictive control using Fenchel's duality

We present an iterative distributed version of Han's parallel method for convex optimization that can be used for distributed model predictive control (DMPC) of industrial processes described by dynamically coupled linear systems. The underlying decomposition technique relies on Fenchel's duality and allows subproblems to be solved using local communications only. We investigate two techniques aimed at improving the convergence rate of the iterative approach and illustrate the results using a numerical example. We conclude by discussing open issues of the proposed method and by providing an outlook on research in the field.     

Distributed LQR control for discrete-time homogeneous systems

This paper investigates the distributed linear quadratic regulation (LQR) controller design method for discrete-time homogeneous scalar systems. Based on the optimal centralised control theory, the existence condition for distributed optimal controller is firstly proposed. It shows that the globally optimal distributed controller is dependent on the structure of the penalty matrix. Such results can be used in consensus problems and used to find under which communication topology (may not be an all-to-all form) the optimal distributed controller exists. When the proposed condition cannot hold, a suboptimal design method with the aid of the decomposition of discrete algebraic Riccati equations and robustness of local controllers is proposed. The computation complexity and communication load for each subsystem are only dependent on the number of its neighbours.     

SECOND‐ORDER NONSINGULAR TERMINAL SLIDING MODE DECOMPOSED CONTROL OF UNCERTAIN MULTIVARIABLE SYSTEMS

This paper proposes a second‐order nonsingular terminal sliding mode decomposed control method for multivariable linear systems with internal parameter uncertainties and external disturbances. First, the systems are converted into the block controllable form, consisting of an input‐output subsystem and a stable internal dynamic subsystem. A special second‐order non‐singular terminal sliding mode is proposed for the input‐output subsystem. The control law is designed to drive the states of the input‐output subsystem to converge to the equilibrium point asymptotically. Then the states of the stable zero‐dynamics of the system converge to the equilibrium point asymptotically. The method proposed in the paper has advantages for higher‐dimensional multivariable systems, in the sense that it simplifies the design and makes it possible to realize a robust decomposed control. Meanwhile, because of the adoption of the second‐order sliding mode, the control signal is continuous. Simulation results are presented to validate the design.     

Input/Output Decoupling of Square Linear Systems by Dynamic Two‐Parameter Stabilizing Control

Analytical expressions of the free parameters of a two‐parameter stabilizing control (TPSC), solving an input/output (I/O) decoupling problem, are presented, and stability conditions are given. Multi‐input‐multi‐output (MIMO), proper, lumped and linear time invariant (LTI) systems are considered. These systems have stabilizable and detectable realizations. The separation principle is applied to design a dynamic output control in a controller‐observer feedback configuration. The I/O relation of the overall system is equivalent to a subsystem, in which the I/O decoupling problem has a solution. Also, if the state dimension of the plant is even, and is double the input dimension of the plant, then coprime factorizations of the plant used for the stabilizing controllers are proposed. The results are illustrated through an example.     

基于减法聚类和自适应神经模糊推理系统的递阶模糊系统的设计

提出了一种设计递阶模糊系统的简易而有效的方法.在得到一个单级模糊系统的基础上,用灵敏度分析法对每一个输入变量的重要性进行排序,从而确定每一级子系统的输入变量.利用减法聚类和自适应神经 模糊推理系统逐级对子系统进行训练.所得到的递阶模糊系统可进一步得到简化.仿真实例证实了设计方法的有效性.     

分解协调的多Agent约束优化算法及应用

针对约束优化系统易陷局部优化的问题,提出了基于分解协调的多Agent约束优化算法（DCMACOA）。对可分系统,不同于传统的分解协调算法,DCMACOA选用各子系统间的关联变量为协调变量,借助于多Agent及生物免疫的进化思想,对各子系统优化及系统协调采用了多Agent免疫优化方法,优化搜索算子主要包括：邻域克隆选择、邻域竞争及邻域协作。工业流程和换热器面积优化仿真实例表明,相比传统的分解协调算法,DCMACOA能改善整体与局部的搜索性能,提高对可分系统的约束优化求解能力,具有较好的全局搜索性能。     

Dynamic input signal design for the identification of constrained systems

In current model predictive control (MPC) practise, the accuracy of the model from system identification is often the crucial factor for the final success. This makes the input signal design a very important step in MPC applications. Because the identification task should move the outputs within some constraints, a constrained design method is needed. Previous constrained signal design methods are usually based on the steady-state gain matrix of a process. Ignoring the system dynamics makes these designs either too conservative when the dynamics are overdamped or allows them to violate the output constraints in the case of underdamped dynamics. In order to address these problems, a new design method making use of the prior approximate estimate of the system dynamics is proposed in this paper. Furthermore, an iterative method of signal design for identification experiments is proposed, and a criterion is defined to compare the accuracy of two successive dynamic models. An example on a subsystem of the challenging Tennessee Eastman process is used to prove the effectiveness of the proposed method.     

基于EMD和灰关联技术的航迹关联方法

针对分布式多传感器融合多目标跟踪系统,提出一种基于经验模态分解(EMD)和灰关联技术的航迹关联方法.首先采用EMD方法将各传感器的航迹信息分解为多个固有模态函数(IMF)之和;然后以各IMF构成矩阵,对其进行奇异值分解;再以奇异值作为各航迹的特征向量,计算特征向量间的灰关联度;最后通过各关联度的融合,得到航迹正确关联的结果.典型情况下的仿真结果表明,与加权法相比,该方法能有效地用于航迹关联.     

An expert system based on wavelet decomposition and neural network for modeling Chua’s circuit

This paper presents an expert system based on wavelet decomposition and neural network for modeling and simulation of Chua’s circuit which is used for chaos studies. The problems which arise in modeling Chua’s circuit by neural networks are high structural complexity and slow and difficult training. With this proposed method a new solutions is produced to solve these problems. Wavelet decomposition is used for new useful feature extracting from input signal and neural network is used for modeling. Test results of proposed wavelet decomposition and neural network model are compared with test results of neural network model. Desired performance is provided by this new model. Test results showed that the suggested method can be used efficiently for modeling nonlinear dynamical systems.     

基于约束理论的Flow-shop分解协调算法

There are many flow shop problems of throughput (denoted by FSPT) with constraints of due date in real production planning and scheduling. In this paper, a decomposition and coordination algorithm is proposed based on the analysis of FSPT and under the support of TOC (theory of constraint). A flow shop is at first decomposed into two subsystems named PULL and PUSH by means of bottleneck. Then the subsystem is decomposed into single machine scheduling problems, so the original NP-HARD problem can be transferred into a serial of single machine optimization problems finally. This method reduces the computational complexity, and has been used in a real project successfully.