非一致采样状态反馈预测控制

针对输入更新不等间隔的非一致采样系统,提出一种基于状态观测器的状态反馈预测控制算法。通过提升控制信号,设计了输入输出同周期的非一致采样系统的状态观测器,并且给出了提升控制信号前后观测器系数矩阵的关系。运用线性矩阵不等式方法,解决无穷时域二次型性能指标下的优化问题,给出了状态反馈预测控制器存在的充分条件。仿真结果表明控制算法的有效性。     

EAI技术在电信运营支撑系统中的应用

随着电信运营业务的发展和运营支撑系统的建设，企业中的应用系统越来越多，如何把各个应用系统整合起来，实现应用的统一和数据的共享，成为必须解决的问题。EAI(Enterprise Application Integration)技术是为解决应用系统集成问题而逐渐发展起来的一项技术，介绍EAI技术的基本原理及其在电信运营支撑系统建设中对相应问题的解决办法。     

基于ROM的FPGA多通道PWM调速策略研究

针对目前机电一体化系统中PWM信号通道多和难同步的问题,提出一种基于ROM的FPGA多通道PWM发生器的设计方法。利用FPGA内部ROM资源,通过软件的方法根据需要产生多路PWM信号。多路PWM发生器通过采用同一时基,可实现PWM信号的严格同步和不同调制策略。采用的FPGA数据采集速度达到1 MB/s,独有的NiosII核软处理器可以实现DSP的功能,可以完成复杂的数据处理。设计方法在机械臂控制系统中进行了实际的应用,初步设计了硬件电路并利用quartus II和modelsim给出了仿真结果。实验结果表明设计方案能很好满足系统功能和性能要求,同时设计又具有开放性,可以在此基础上进行扩展。     

8位CISC微处理器的设计与实现

介绍了一种基于FPGA芯片的8位CISC微处理器系统,该系统借助VHDL语言的自顶向下的模块化设计方法,设计了一台具有数据传送、算逻运算、程序控制和输入输出4种功能的30条指令的系统。在QUARTUSII系统上仿真成功,结果表明该微处理器系统可以运行在100 MHz时钟工作频率下,能快速准确地完成各种指令组成的程序。     

融合MLP和DBN的光伏发电预测算法

精确的光伏发电预测对提高电力系统稳定性、保证电能质量、优化电网运行具有重大意义。为了解决现存光伏预测算法精度较低、性能较差的问题,同时为了综合利用多层感知器（MLP）解决非线性问题的能力以及深度信念网络（DBN）有效处理大量复杂数据的优势,构建了一种融合MLP和DBN的光伏预测算法（MLP-DBN）,其基本思想是先利用MLP模型进行初步预测,再将观测值与预测值的残差输入DBN预测模型进行预测,最后用残差预测值对MLP模型的预测值进行修正。利用光伏发电实测数据仿真,探究了不同学习率下模型的预测性能,并对模型的各参数进行了寻找优化设置。使用均方根误差、平均绝对误差以及决定系数等性能指标评估结果表明,与传统的预测算法支持向量机（SVM）以及具有较高预测精度的深度学习算法长短期记忆网络（LSTM）相比,MLP-DBN算法性能有明显的提升,为光伏发电提供了一种高精度高性能的预测算法,可以有效解决光伏发电预测问题。     

CRH1与CRH2动车组牵引变流器性能比较与优化

动车组牵引变流器的性能是评估动车纽安全高效运行的重要指标之一。以CRH1A和CRH2A型动车组牵引变流器为对象,基于Matlab／Simulink仿真软件对两种变流器的性能及其优化进行了研究。根据两种变流器主电路实际设计参数分别建立了各自的仿真模型,比较和分析了相应主电路结构及其对应的控制策略对系统输入输出性能的影响。在此基础上,探究了空间矢量脉宽调制SVPWM控制策略应用在CRH2A型动车组变流器中对改善变流器输入输出特性及动态性能的效果,通过仿真建模初步验证了该结论。仿真结果和数据基本符合预期目标。     

基于模型驱动的软件体系结构

介绍了一种基于模型驱动的软件体系结构。该体系结构将与实现技术无关的功能模型及基于某一特定技术的实现模型分离 ,通过不同模型之间的变换 ,使系统能适应技术的进步 ,解决系统在不同中间件平台上的集成、互操作性、可移植性等问题。此外 ,也讨论了这种体系结构的优缺点及今后需要解决的问题。     

AGC系统中的非线性观测器设计

AGC（自动发电控制）系统是一个十分复杂的系统，其具有严重的非线性，且输入（锅炉、轮机等动力产生部分）和输出（频率负载）均存在未知扰动，为了实现对发电系统的精确控制，设计出输入输出扰动的观测器显得尤为重要。在阐述了一种带有未知输入输出扰动的非线性观测器设计方法的同时，是对传统的只能观测输入环节中的未知扰动观测器的一大改进。通过对系统状态方程和输出方程的一系列解偶变换，将输入、输出扰动从系统中分离出来。解偶后，系统分为受扰子系统和无扰动子系统两部分，通过对无扰动子系统的分析，最终设计出输入输出未知扰动观测器。系统观测器增益矩阵则通过求解一个代数Riccati方程（ARE）得到。将设计出的观测器用于故障检测与隔离（FDI）并取得良好的效果。     

基于收缩约束模型预测控制的无人车辆路径跟踪

针对存在有界扰动的非线性无人驾驶车辆避障过程中最优路径规划跟踪问题,提出一种基于预测时域内系统输入输出收缩约束(PIOCC)的模型预测控制(MPC)方法.首先在构建目标函数时,为扩大可行性解的范围引入软约束思想,将最优规划路径的跟随问题转化为对模型预测控制优化问题的求解;其次为避免短预测时域造成闭环系统发散而导致在约束条件限定下出现无可行性解的情况,采用预测时域内系统输入输出收缩约束的方法,设计模型预测控制器;再次基于Lyapunov稳定性理论证明所设计的模型预测闭环控制系统是渐近稳定的;最后通过仿真实例验证了所提出基于PIOCC的控制策略在解决扩大可行解范围和避免闭环系统发散问题时的有效性,实现了无人驾驶车辆在路径跟踪时具有良好的快速性和稳定性.     

FPGA实现高速实时多端口SDRAM控制器的研究

为了满足多个设备同时存取高速数据的需求,介绍了利用Xilinx高性能可编程逻辑器件Virtex6 FPGA实现高速实时多端口DDR3 SDRAM控制器的原理和方法,在一个实时图像处理系统平台上实现了对单片SO-DIMM DDR3内存条的多设备实时访问控制。通过ChipScope工具采样输入输出数据,验证其可行性,分析计算出端口速率和其他主要时间参数。实验结果显示高速实时多端口SDRAM控制器具有集成度高、传输带宽高、功耗低的优点。在多设备同时读写高速数据的系统中具有很高的实用价值。     

Distributed model predictive control for polytopic uncertain systems subject to actuator saturation

In this paper, we present a distributed model predictive control (MPC) algorithm for polytopic uncertain systems subject to actuator saturation. The global system is decomposed into several subsystems. A set invariance condition for polytopic uncertain system with input saturation is identified and a min–max distributed MPC strategy is proposed. The distributed MPC controller is designed by solving a linear matrix inequalities (LMIs) optimization problem. An iterative algorithm is developed for making coordination among subsystems. Case studies are carried out to illustrate the effectiveness of the proposed algorithm.     

Agile MPC system linking manufacturing and market strategies

Increasing complexity and interdependency in manufacturing enterprises require an agile manufacturing paradigm. This paper considers a dynamic control approach for linking manufacturing strategy with market strategy through a reconfigurable manufacturing planning and control (MPC) system to support agility in this context. A comprehensive MPC model capable of adopting different MPC strategies through distributed controllers of inventory, capacity, and WIP is presented. A hierarchical supervisory controller (referred to as decision logic unit, DLU) that intakes the high-level strategic market decisions and constraints together with feedback of the current manufacturing system state (WIP, production, and inventory levels) and optimally manages the distributed controllers is introduced. The DLU architecture with its three layers and their different functionalities is discussed showing how they link the higher management level to the operational level to satisfy the required demand. A case study for an automatic PCB assembly factory is implemented to demonstrate the applicability of the whole approach. In addition, a comparative cost analysis study is carried out to compare between the developed agile MPC system and classical-inventory- and capacity-based MPC policies in response to different demand patterns. Results showed that the developed agile MPC policy is as cost effective as the inventory-based MPC policy in demand patterns with steady trends, as cost effective as capacity-based MPC in turbulent demand patterns, and far superior than both classical MPC polices in mixed-demand patterns.     

On the explicit dead-time compensation for robust model predictive control

Computational simplicity is one of the most important aspects to take into account in robust model predictive control (MPC). In dead-time processes, it is common to use an augmented state-space representation in order to apply robust MPC strategies but, this procedure may affect computational aspects. In this paper, explicit dead-time compensation will be used to avoid augmented representation. This technique will be analyzed in terms of robust stability and constraint satisfaction for discrete-time linear systems. The results of this discussion will be applied to a robust tube-based MPC strategy which is able to guarantee robust stability and constraint satisfaction of a dead-time system by considering a prediction model without dead-time. Moreover, taking advantage of the proposed scheme, the robust MPC will be particularized for first-order plus dead-time models which simplifies significantly controller synthesis. The proposed dead-time compensation method will be applied to different robust MPC strategies in two case studies: (i) a simulated quadruple-tank system, and (ii) an experimental scaled laboratory heater process.     

Distributed model predictive control of constrained weakly coupled nonlinear systems

This paper proposes a distributed model predictive control (MPC) strategy for a large-scale system that consists of several dynamically coupled nonlinear systems with decoupled control constraints and disturbances. In the proposed strategy, all subsystems compute their control signals by solving local optimizations constrained by their nominal decoupled dynamics. The dynamic couplings and the disturbances are accommodated through new robustness constraints in the local optimizations. The paper derives relationships among, and designs procedures for, the parameters involved in the proposed distributed MPC strategy based on the analysis of the recursive feasibility and the robust stability of the overall system. The paper shows that, for a given bound on the disturbances, the recursive feasibility is guaranteed if the sampling interval is properly chosen. Moreover, it establishes sufficient conditions for the overall system state to converge to a robust positively invariant set. The paper illustrates the effectiveness of the proposed distributed MPC strategy by applying it to three coupled cart-(nonlinear) spring–damper subsystems.     

Constrained nonlinear MPC for accelerated tracking piece-wise references and its applications to thermal systems

In this paper, we propose a model predictive control (MPC) strategy for accelerated offset-free tracking piece-wise constant reference signals of nonlinear systems subject to state and control constraints. Some special contractive constraints on tracking errors and terminal constraints are embedded into the tracking nonlinear MPC formulation. Then, recursive feasibility and closed-loop convergence of the tracking MPC are guaranteed in the presence of piece-wise references and constraints by deriving some sufficient conditions. Moreover, the local optimality of the tracking MPC is achieved for unreachable output reference signals. By comparing to traditional tracking MPC, the simulation experiment of a thermal system is used to demonstrate the acceleration ability and the effectiveness of the tracking MPC scheme proposed here.     

A RBF-ARX model-based robust MPC for tracking control without steady state knowledge

A RBF-ARX modeling and robust model predictive control (MPC) approach to achieving output-tracking control of the nonlinear system with unknown steady-state knowledge is proposed. On the basis of the RBF-ARX model with considering the system time delay, a local linearization state-space model is obtained to represent the current behavior of the nonlinear system, and a polytopic uncertain linear parameter varying (LPV) state-space model is built to represent the future system’s nonlinear behavior. Based on the two models, a quasi-min–max MPC algorithm with constraint is designed for output-tracking control of the nonlinear system with unknown steady state knowledge. The optimization problem of the quasi-min–max MPC algorithm is finally converted to the convex linear matrix inequalities (LMIs) optimization problem. Closed-loop stability of the MPC strategy is guaranteed by the use of parameter-dependent Lyapunov function and feasibility of the LMIs. Two examples, i.e. the modeling and control of a continuously stirred tank reactor (CSTR) and a two tank system demonstrate the effectiveness of the RBF-ARX modeling and robust MPC approach.     

Feedback noncausal model predictive control of wave energy converters

In this paper, a novel feedback noncausal model predictive control (MPC) strategy for sea wave energy converters (WECs) is proposed, where the wave prediction information can be explicitly incorporated into the MPC strategy to improve the WEC control performance. The main novelties of the MPC strategy proposed in this paper include: (i) the recursive feasibility and robust constraints satisfaction are guaranteed without a significant increase in the computational burden; (ii) the information of short-term wave prediction is incorporated into the feedback noncausal MPC method to maximise the potential energy output; (iii) the sea condition for the WEC to safely operate in can be explicitly calculated. The proposed feedback noncausal MPC algorithm can also be extended to a wide class of control design problems, especially to the energy maximisation problems with constraints to be satisfied and subject to persistent but predictable disturbances. Numerical simulations are provided to show the efficacy of the proposed feedback noncausal MPC.     

Quasi-min-max output-feedback model predictive control for LPV systems with input saturation

In the research field of model predictive control (MPC), an output-feedback-type MPC method is consistently required for controlling a wide range of constrained systems. In this paper, we propose a two-stage control strategy for polytopic linear parameter varying (LPV) systems subject to input constraints. This strategy consists of a modified quasi-min-max output-feedback MPC method and a novel terminal output-feedback robust control technique. The proposed control mechanism involves the system states to be first controlled via the MPC method to be driven into a prescribed neighborhood of the origin, and then, the terminal output-feedback robust control method guaranteeing the input constraints is applied to make such states converge to the origin. It is also verified that our control method guarantees the closed-loop stability and feasibility in the presence of model uncertainties and input constraints. Finally, a numerical example is given to demonstrate its effectiveness.     

Robust event‐triggered model predictive control for constrained linear continuous system

Model predictive control (MPC) is capable to deal with multiconstraint systems in real control processes; however, the heavy computation makes it difficult to implement. In this paper, a dual‐mode control strategy based on event‐triggered MPC (ETMPC) and state‐feedback control for continuous linear time‐invariant systems including control input constraints and bounded disturbances is developed. First, the deviation between the actual state trajectory and the optimal state trajectory is computed to set an event‐triggered mechanism and reduce the computational load of MPC. Next, the dual‐mode control strategy is designed to stabilize the system. Both recursive feasibility and stability of the strategy are guaranteed by constructing a feasible control sequence and deducing the relationship of parameters, especially the inter‐event time and the upper bound of the disturbances. Finally, the theoretical results are supported by numerical simulation. In addition, the effects of the parameters are discussed by simulation, which gives guidance to balance computational load and control performance.     

基于强化学习的大时延过程控制策略研究

具有长时延的过程控制被公认为是较难的系统过程控制.模型预测控制(MPC)是一种适用于大时延过程的新的过程控制方法.相比于PID等传统的控制方法,MPC基于模型对未来状态的预测进行决策,能够兼顾及时反馈与长期规划.但MPC对于过程的预测步数依然是有限的.强化学习作为机器学习的重要部分,原则上能够预测策略在无限长时间内的收...