具有动态不确定性的离散预测控制系统设计*

本文对具有未建模动态及噪声干扰的离散系统预测控制进行讨论，在保留广义预测控制（ＧＰＣ）基本特点的条件下，提出包含ＧＰＣ指标项、灵敏度的综合指标函数，使用闭环系统的鲁棒稳定性增强。     

连续非线性系统预测控制的次优性分析

针对一般连续非线性系统,研究了有终端约束的稳定预测控制策略相对传统最优控 制的次优性问题.通过分析预测控制的有限时域滚动优化性质,得到了预测控制次优性的上 界,并且将该结果应用于连续线性系统,得到了一个量化的次优性评价指标.     

扰动自适应模型预测控制的频域特性分析

扰动模型的准确性对模型预测控制算法的扰动抑制能力有重要影响,当前模型预测控制广泛采用的阶跃扰动模型不能准确描述进入系统的不可测扰动,扰动抑制能力有限.自适应扰动模型可以较好的描述不可测扰动,提高对扰动的预估和抑制能力.本文对采用自适应时间序列扰动模型的预测控制进行分析,研究了扰动自适应预测控制(DMCA)的闭环结构以及带宽、灵敏度函数等频域指标与控制器抗扰性能的关系.带宽大的系统抑制扰动的速度快,灵敏度函数幅值越小则对扰动的抑制能力越强.理论分析和仿真结果表明与动态矩阵控制(DMC)相比,采用自适应扰动模型的DMCA算法能够更好的预测和抑制扰动,被控变量偏离设定值的最大幅度降低60％,带宽是DMC的1.5倍、调节速度更快,在低频段有较小的灵敏度函数值.自适应扰动模型提升了DMCA控制器的扰动抑制性能,对保障系统安全平稳运行和增加效益有重要意义.     

复杂高炉炼铁过程的数据驱动建模及预测算法

高炉炼铁过程的控制意味着控制高炉铁水温度及成份在指定的范围. 本文以高炉炉内热状态的重要指示剂---高炉铁水硅含量为研究对象, 针对机理建模难以准确预测、控制高炉铁水硅含量的发展变化, 利用数据驱动建模的思想, 建立了基于多元时间序列的高炉铁水硅含量数据驱动预测模型. 实例分析表明, 建立的数据驱动预测模型能够很好地预测高炉铁水硅含量, 连续预测167炉高炉铁水硅含量, 命中率高达83.23%, 预测均方根误差为0.07260. 这些指标均优于基于单一硅时间序列所建立的数据驱动模型, 对实际生产具有很好的指导作用.     

多指标的连续预测控制系统设计

本文对多指标的连续预测控制系统设计进行讨论。在设计时除保留连续系统预测控制基本特点外,同时考虑干扰、未建模动态等系统的影响,使得闭环系统鲁棒稳定性增强。     

暂冲式高速风洞连续变速压颤振试验流场控制系统研究

相对于阶梯变速压颤振试验,连续变速压颤振试验具备更高的可靠性和效率,可以为先进飞行器的颤振边界预测及颤振特性分析提供更为安全可靠的技术支撑。为实现定马赫数连续变速压颤振试验技术,设计了连续变速压流场控制系统,通过引入解耦控制与专家PID控制优化了控制效果。风洞验证试验结果表明,该系统具有良好的控制效果,可满足定马赫数连续变速压流颤振试验对风洞流场控制指标的要求。     

四足机器人对角小跑步态下液压驱动单元位置伺服控制特性参数灵敏度研究

高性能四足仿生机器人的关节由高度集成的液压驱动单元(HDU)驱动,液压传动的引入在带来高性能的同时,也增强了非线性和参数时变性等问题,此外机器人各关节复杂多变的载荷特性,也增大了每个关节HDU的控制难度.为了有针对性地进行对角小跑步态的控制补偿,研究了各HDU位置控制特性的主要影响参数.首先,考虑伺服阀动态特性、压力-流量非线性、伺服缸活塞初始位移及摩擦非线性,搭建其位置伺服控制系统.基于HDU实际结构参数和工作参数建立仿真模型.将HDU实测的摩擦力及对角小跑步态下各关节实测的位移与外负载力数据加载至仿真模型中,得出HDU伺服控制的输出位移及负载力仿真曲线,并进行了试验验证.基于位置伺服控制方程,推导含非线性和时变参数的灵敏度方程表达式,进而求解4个关节工况下HDU输出位移对各参数的灵敏度函数.以采样时间内参数变化引起输出变化的最大值及参数变化引起输出变化绝对值的总和为灵敏度衡量指标,给出指标变化柱形图,分析各参数灵敏度变化规律,并对各HDU中供油压力、比例增益、活塞初始位移及外负载力4个参数的灵敏度指标进行试验验证.最后得到了对角小跑步态下影响各关节HDU位置控制特性的主要和次要影响参数,为位置补偿控制器的设计提供了参考.     

预测控制在电力电子变流器中的应用综述

随着微处理器技术的发展,综合预测控制的诸多优点,预测控制在电力电子变流器中的应用已成为研究热点.阐述了预测控制应用于电力电子变流器的基本原理和特点,对无差拍控制、连续控制集模型预测控制、有限控制器模型预测控制在电力电子变流器中的应用进展进行了总结和评述,展望了预测控制在电力电子变流技术领域的研究方向.     

无穷时域的连续时间广义预测控制

将无穷时域的性能指标引入连续时间的广义预测控制,通过施加新的终端等式约束,把包含无穷时域性能指标的优化问题转化成可解的二次规划问题.利用后退时域性能指标的单调性,给出了保证连续时间广义预测控制闭环稳定性的条件.仿真例子验证了该算法的有效性.     

基于协方差指标预测的MPC实时性能监控

为利用过程数据实时监控模型预测控制(Model predictive control, MPC)的性能, 提出一种基于协方差预测残差的性能监控方法.首先在分析模型预测控制器优 化函数和控制结构的基础上, 构造包含预测误差、控制量和过程输出的监控变量集, 然后利用滑动时间窗口建立基于协方差的实时性能评价 指标.针对协方差指标缺少控制限的问题, 建立实时协方差指标的时间序列模型, 根据协方差指标的预测残差检测模型预测控制性能下降.进 一步利用基于数据集相似度的性能诊断方法确定性能恶化源.最后通过Wood-Berry二元精馏塔上的仿真研究验证了所提方法的有效性.     

Data-driven predictive control for continuous-time linear parameter varying systems with application to wind turbine

A new data-driven predictive control method based on subspace identification for continuous-time linear parameter varying (LPV) systems is presented in this paper. It is developed by reformulating the continuous-time LPV system which utilizes Laguerre filters to obtain the subspace prediction of output. The subspace predictors are derived by QR decomposition of input-output and Laguerre matrices obtained by input-output data. The predictors are then applied to design the model predictive controller. It is shown that the integrated action is incorporated in the control effect to eliminate the steady-state offset. We control the continuous-time LPV systems to obtain the attractive performance with the proposed data-driven predictive control method. The proposed controller is applied to a wind turbine to verify its effectiveness and feasibility.     

Implications of discretization on dissipativity and economic model predictive control

Economic model predictive control, where a generic cost is employed as the objective function to be minimized, has recently gained much attention in model predictive control literature. Stability proof of the resulting closed-loop system is often based on strict dissipativity of the system with respect to the objective function. In this paper, starting with a continuous-time setup, we consider the ‘discretize then optimize’ approach to solving continuous-time optimal control problems and investigate the effect of the discretization process on the closed-loop system. We show that while the continuous-time system may be strictly dissipative with respect to the objective function, it is possible that the resulting closed-loop system is unstable if the discrete-approximation of the continuous-time optimal control problem is not properly set up. We use a popular example from the economic MPC literature to illustrate our results.     

连续子空间预测控制方法及其应用

针对工业过程中常见的连续时间系统,提出一种新型的连续子空间预测控制方法。首先,利用拉格朗日滤波器对连续时间系统模型进行转换,得到子空间预测输出;然后,对构造的输入输出和拉格朗日矩阵进行QR分解得到子空间预估器,将子空间预估器用于构造增量型未来预测输出值,进而设计出连续子空间预测控制器;最后,通过工业蒸发器系统过程控制仿真实验,验证了所提出控制方法的有效性。     

Tube-based robust sampled-data MPC for linear continuous-time systems

This note extends to the continuous-time case the “tube-based” approach for the design of discrete-time robust model predictive control (MPC) algorithms developed in Mayne, Seron, and Rakovi? (2005). This extension is of interest in view of the simplicity and popularity of the method as well as of the industrial relevance of continuous-time implementations of MPC. The proposed robust control law is composed of two terms: (1) a sampled-data MPC control law and (2) a continuous-time state feedback term.     

连续时间域内具有积分作用的广义预测控制（CIGPC）

文献[1]推导了连续时间域内的广义预测控制规律,但由该方法所获得的控制作用不具有积分作用。因此,对于大量具有自平衡能力的过程,当设计模型的比例增益同实际过程存在误差时,将导致系统输出的稳态偏差,本文在文[1]的基础上提出了一种具有积分作用的广义预测控制方法,仿真结果表明,本文的方法不仅能够消除系统的稳态偏差,而且可使系统具有较好的控制品质。     

TS-based sampled-data model predictive controller for continuous-time nonlinear systems

This paper proposes a new fuzzy model predictive control approach for continuous-time nonlinear systems in terms of linear matrix inequalities (LMIs). The proposed approach is based on the Takagi–Sugeno fuzzy modeling, a quadratic Lyapunov function, and a sampled-data parallel distributed compensation controller with constant sampling time. The goal is designing the sampled-data controller such that at each sampling time, the stability of the closed-loop system is guaranteed and an infinite horizon cost function is minimised. The main advantage of the proposed approach is to eliminate the approximations induced from discretizing the original system and cost function upper bound minimisation. Consequently, a lower bound of the cost function is obtained and the performance of the proposed model predictive controller is improved compared to the recently published papers in the same field of interest. In addition, the Euclidean norm constraint of the control input vector is derived in terms of LMIs. To illustrate the merits of the proposed approach, the proposed technique is applied to a continuous stirred tank reactor system.     

约束非线性系统构造性模型预测控制

研究了连续时间约束非线性系统模型预测控制设计.利用控制Lyapunov函数离线构造单变量可调预测控制器,再根据性能指标在线优化可调参数,其中该参数近似于闭环系统的"衰减率".同时,控制Lyapunov函数保证了算法的可行性和闭环系统的稳定性.最后通过数值仿真验证了该算法的有效性.     

A receding horizon control approach to sampled-data implementation of continuous-time controllers

We propose a novel way for sampled-data implementation (with the zero order hold assumption) of continuous-time controllers for general nonlinear systems. We assume that a continuous-time controller has been designed so that the continuous-time closed-loop satisfies all performance requirements. Then, we use this control law indirectly to compute numerically a sampled-data controller. Our approach exploits a model predictive control (MPC) strategy that minimizes the mismatch between the solutions of the sampled-data model and the continuous-time closed-loop model. We propose a control law and present conditions under which stability and sub-optimality of the closed loop can be proved. We only consider the case of unconstrained MPC. We show that the recent results in [G. Grimm, M.J. Messina, A.R. Teel, S. Tuna, Model predictive control: for want of a local control Lyapunov function, all is not lost, IEEE Trans. Automat. Control 2004, to appear] can be directly used for analysis of stability of our closed-loop system.     

Distributed predictive control of continuous-time systems

In the last years focus has been put in the development of distributed Model Predictive Control (MPC) algorithms. With a few exceptions, they have been mostly developed in the discrete-time framework. However, discretization of large-scale systems may destroy the sparsity of the original continuous-time models, making distributed control design and implementation more difficult. Also, more in general, discrete-time control of continuous-time systems does not allow to consider the process inter-sampling behavior. In this paper we present a novel non-cooperative distributed predictive control algorithm for continuous-time systems based on robust MPC concepts. The convergence properties of the proposed control scheme are stated, and its realizability is tested through a simulation case study.