双层结构模型预测控制算法性能分析

双层结构模型预测控制是在常规模型预测控制的基础上集成了稳态目标计算层,所增加稳态目标计算层通过对给定外部目标进行调节或执行独立经济优化为底层动态控制提供设定值.本文着重于分析增加稳态目标计算层对常规模型预测控制策略在控制性能、经济性能、及鲁棒性方面的影响.阐明稳态目标计算是在动态不确定性下的最优决策过程,可实时考虑当前工况环境给出最佳设定值,使动态层控制量的计算更加合理,并可保证在动态控制层实现无静差控制;稳态目标计算层可利用包含目标跟踪和经济优化的性能指标,在保证跟踪控制要求的同时利用操作自由度进行经济性优化,有助于提高系统经济性能,此外由于操作自由度增加使得系统对于特定方向的扰动和通道失配具有更强的鲁棒性.最后通过实验仿真验证了所做分析的正确性.     

GPC的预测状态空间形式及预测控制算法形式的统一

提出一种与ＭＡＣ，ＤＭＣ预测状态空间形式算法相似的ＧＰＣ预测状态空间形式的算法，使ＧＰＣ可以进行多步迭代预测，并讨论了ＧＰＣ预测状态空间算法可作为预测控制算法的一种统一形式。     

离线优化在线查表的双层结构预测控制

双层结构预测控制包括稳态优化和动态控制,计算复杂度较大,难以在实时性要求较高或者是主控制器计算能力较弱的场合应用.本文阐述了一种离线计算在线查表的稳态优化方法和详细的技术实施方案.首先采用枚举方式离线计算稳态优化值,然后通过在线查表得到实时优化值.当在线查表不可行时,通过查找距离该查表点最近的多个可查表点,以距离作为加权系数,近似计算最优稳态目标值.最后,通过性能分析和仿真实验证明了本文提出的该方法有效的降低了计算复杂度,减少了计算时间.     

基于子空间辨识的状态空间模型预测控制

针对无法从工业过程中获得准确状态空间模型的问题,提出一种基于子空间辨识的状态空间模型预测控制方法。利用子空间辨识方法得到的状态空间模型作为系统模型,给出约束条件下的预测控制算法。以CD播放器机械臂系统为例,通过状态空间模型预测控制方法实现对系统输出的跟踪控制,仿真结果表明,该方法控制效果良好。     

动态矩阵控制中基于Kalman滤波的开环预测方法

用稳态Kalman滤波器来解释双层结构模型预测控制中的开环预测模块,得到的结果与非基于Kalman滤波导出的开环预测对应和等价.将该结果与文献中已有的Kalman滤波解释进行了详细对比,阐述了其中的不同点.针对双层结构预测控制的整体策略进行仿真,并针对积分输出的Kalman滤波进行仿真,验证了所得结论的有效性.     

基于状态空间的稳定广义预测控制器

给出了稳定广义预测控制（ＳＧＰＣ）算法的状态空间表达形式，并讨论了基于状态空间表达式的ＳＧＰＣ和基于输入输出表达式的ＳＧＰＣ之间的等价性。最后给出了算法的仿真结果。     

双层结构预测控制研究进展

现代工业大系统的优化控制采用递阶结构,其中以预测控制为代表的先进过程控制已经成为重要的一级.目前,主流的工业预测控制技术均采用双层结构,即包含稳态优化层和动态控制层.双层结构预测控制技术可以有效解决复杂工业过程常见的多目标优化、多变量控制的难点问题.本文简要总结了双层结构预测控制的算法,并从控制输入与被控输出稳态关系入手分析了多变量预测控制稳态解的相容性和唯一性,说明了稳态优化的重要性.针对双层结构预测控制与区间预测控制的性能比较、稳态模型的奇异性以及闭环系统动态特性等提出了一些见解,并指出了需要重点研究的主题.     

基于状态空间的有约束稳定广义预测控制

针对一类有约束的稳定广义预测控制问题,提出一种基于状态空间的稳定广义预测控制算法。首先通过传递函数的状态空间实现,得到被控对象的离散状态空间形式;然后引入Deadbeat状态反馈矩阵并给出约束条件的等价性定理,实现了约束条件的等价转化;最后通过等价约束条件优化性能指标函数求解控制律。仿真实例表明该方法具有良好的稳定性。     

一类非线性系统的扩展状态空间预测控制

针对一类具有输出反馈耦合的离散非线性系统,将过程模型表示为扩展状态空间模型,并利用反馈回路非线性函数在参考轨迹附近的局部线性表示,导出一种具有类似离散PI最优调节器结构并具有未来P步设定值前馈控制的新型预测控制器．因其目标函数中考虑了系统状态的变化,所以控制效果要优于仅考虑预测输出误差的情况．仿真结果表明了它的有效性．     

An improved state-space model structure and a corresponding predictive functional control design with improved control performance

Conventional state-space model predictive control requires a state estimator/observer to access the state information for feedback controller design. Its drawbacks are the numerical convergence stability of the observer and closed-loop control performance deterioration with activated plant input/output constraints. The recent direct use of measured input and output variables to formulate a non-minimal state-space (NMSS) model overcomes these problems, but the subsequent controller is too sensitive to model mismatch. In this article, an improved structure of NMSS model that incorporates the output-tracking error is first formulated and then a subsequent predictive functional control design is proposed. The proposed controller is tested on both model match and model mismatch cases for comparison with previous controllers. Results show that control performance is improved. In addition, a linear programming method for constraints dealing and a closed form of transfer function representation of the control system are provided for further insight into the proposed method.     

Non-minimal state-space model-based continuous-time model predictive control with constraints

This article proposes a model predictive control scheme based on a non-minimal state-space (NMSS) structure. Such a combination yields a continuous-time state-space model predictive control system that permits hard constraints to be imposed on both plant input and output variables, whilst using NMSS output-feedback without the need for an observer. A comparison between the NMSS and observer-based approaches using Monte Carlo uncertainty analysis shows that the former design is considerably less sensitive to plant-model mismatch than the latter. Through simulation studies, the article also investigates the role of the implementation filter in noise attenuation, disturbance rejection and robustness of the closed-loop predictive control system. The results show that the filter poles become a subset of the closed-loop poles and this provides a straightforward method of tuning the closed-loop performance to achieve a reasonable balance between speed of response, disturbance rejection, measurement noise attenuation and robustness.     

电流型变流器的改进模型预测控制

传统的模型预测控制(MPC)采用脉冲响应的非参数模型作为系统的预测控制模型,计算量大,很难直接应用于实时控制系统.本文提出将传统的MPC进行改进,应用到电流型变流器(CSC)功率因数校正系统中.改进的MPC根据CSC控制量与被控制量的传递函数得出CSC的一阶差分方程作为预测控制模型,同时保留传统MPC反馈校正、动态优化等优点.将此改进的MPC与传统的PID控制进行对比研究,结果表明:改进的MPC用于电流型变流器,比传统的PID控制具有更好的鲁棒性和更快速的动态响应特性.     

Zone model predictive control for pressure management of water distribution network

In the water distribution network (WDN), although the water demand of the node is changing constantly, the water quantity and water pressure of the node need to be met at each moment. To realize energy saving and consumption reduction, it is proposed to control the nodal water head of WDN in an appropriate narrow range. The frequent large fluctuation of the water demand, which may lead the water pressure exceed the expected range, increases the difficulty of the zone control. To realize optimized WND control, a novel zone predictive control is proposed, where two switching cases are considered. The switching condition is whether there are feasible solutions to keep the pressure within the expected region over the prediction horizon. When the condition is satisfied, the controller minimizes the variation of inputs with constraints of pressure range for ensuring the tank level staying within the expected zone and obtaining optimal economic cost. When the current pressure is out of the expected region or the condition is not satisfied due to the large variation of water demand, a reference trajectory of outputs is introduced, which is combined with the inputs as an optimization variable, and the constraints of expected zone are moved from the output to the introduced reference trajectory. Through minimizing the distance between reference trajectory and output, the controller will keep the tank level from deviating too far from the expected zone and will drive the tank level rapidly into the expected zone once the tank level exceeds the expected range. An application of the proposed zone MPC to WDN in Shinan District of Shanghai is given to illustrate its effectiveness.     

Adaptive horizon economic nonlinear model predictive control

In this paper, we present a computationally efficient economic NMPC formulation, where we propose to adaptively update the length of the prediction horizon in order to reduce the problem size. This is based on approximating an infinite horizon economic NMPC problem with a finite horizon optimal control problem with terminal region of attraction to the optimal equilibrium point. Using the nonlinear programming (NLP) sensitivity calculations, the minimum length of the prediction horizon required to reach this terminal region is determined. We show that the proposed adaptive horizon economic NMPC (AH-ENMPC) has comparable performance to standard economic NMPC (ENMPC). We also show that the proposed adaptive horizon economic NMPC framework is nominally stable. Two benchmark examples demonstrate that the proposed adaptive horizon economic NMPC provides similar performance as the standard economic NMPC with significantly less computation time.     

应用模型预测控制的混合动力汽车模式切换动态协调控制

利用传统协调控制策略或模型预测控制(MPC)方法能够解决离合器模式切换的平顺性,但其改善效果不显著,且缺乏深入的细化研究.因此,为了改进混合动力汽车有离合器结合的模式切换过程中的平顺性,本文基于MPC制定有离合器模式切换过程的动态协调控制策略.在对混合动力系统有离合器模式切换模型进行简化的基础上,开展MPC在模式切换动态协调控制过程的原理描述,以减小有离合器模式间切换的冲击度进行基于MPC动态协调控制策略设计,并对不同权重下的冲击度进行了详细的对比.通过实验验证,其结果表明采用MPC的模式切换协调控制最大冲击度从26.3 m/s³下降至9.26 m/s³,降低了64.8%,明显的抑制了模式切换过程中的冲击度,有效的改善了模式切换的平顺性.     

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.     

输入状态稳定的鲁棒预测控制

以有界干扰非线性系统为研究对象,设计一种基于近似可达集的鲁棒预测控制方法。该方法以鲁棒控制不变集作为终端约束集,采用一种简单的三次多项式逼近预测控制的待优化控制律,通过在线优化求解三次多项式的各项系数,并从理论上证明了所设计的鲁棒预测控制律可以使系统输入状态稳定。最后通过仿真实例验证了所提出的鲁棒预测控制方法的可行性和有效性。     

Robust model predictive control for continuous uncertain systems with state delay

This paper is concerned with robust model predictive control for linear continuous uncertain systems with state delay and control constraints, A piecewise constant control sequence is calculated by minimizing the upper-bound of the infinite horizon quadratic cost function, At each sampling time, the sufficient conditions for the existence of the model predictive control are derived, and expressed as a set of linear matrix inequalities. The robust stability of the closed-loop svstems is guaranteed bv the proposed design method. A numerical example is given to illustrate the main results.