基于动态矩阵控制的双层结构预测控制的整体解决方案

本文给出一种双层结构预测控制的整体解决方案. 该方案分为开环预测、稳态目标计算和动态控制三个模块. 开环预测基于实测被控变量值和过去的操作变量值, 在假设未来操作变量不再变化的情况下, 估计未来的被控变量值. 稳态目标计算根据开环预测结果和外部目标等要求, 计算操作变量、被控变量的稳态目标值以及软约束的放松量. 动态控制根据开环预测结果和稳态目标输出结果, 计算未来的控制作用增量序列, 采用经典的动态矩阵控制策略. 这个整体解决方案保证了三个模块在模型、约束、目标上的一致性. 该算法是在已有文献的基础上, 将三个模块统一处理得到的. 仿真与应用例子证实了该算法的有效性.     

预测控制偏差区间的一种工程化处理方法

将被控变量的约束区间分为大小两种偏差区,并设计了调整偏差区大小的参数.当被控变量位于大偏差区时,连续调整控制率使控制作用加强,以便被控变量尽快回到小偏差区,从而加快控制系统的调节过程.控制作用从小偏差区过渡到大偏差时是连续变化的,避免了控制作用的跳变,消除了在大小偏差区交界处的震荡现象.仿真结果表明,在模型存在不确定性情况下,该工程化处理方法可使控制系统的动态性能明显改善.     

参考轨迹在线优化的区间预测控制

实际生产过程控制中,特别是多变量耦合的复杂过程中,对某些被控变量其实并没有苛刻的给定值要求,只是满足在给定区间内即可,从而保证整个生产过程控制的平稳.针对这一问题,提出一种基于模型预测控制理论的区间控制算法.该算法对预测控制的优化性能指标进行改进,将性能优化指标中的参考轨迹作为约束变量进行在线优化处理;参考轨迹自动跟踪被控变量输出,使预测控制针对输出在不同的区域范围采取不同的控制强度,从而在实现区间控制的前提下满足平稳控制要求.最后以Shell公司的典型重油分馏塔控制问题为例进行仿真,验证算法的有效性和可行性.     

参考轨迹在线优化的区间预测控制

实际生产过程控制中,特别是多变量耦合的复杂过程中,对某些被控变量其实并没有苛刻的给定值要求,只是满足在给定区间内即可,从而保证整个生产过程控制的平稳.针对这一问题,提出一种基于模型预测控制理论的区间控制算法.该算法对预测控制的优化性能指标进行改进,将性能优化指标中的参考轨迹作为约束变量进行在线优化处理;参考轨迹自动跟踪被控变量输出,使预测控制针对输出在不同的区域范围采取不同的控制强度,从而在实现区间控制的前提下满足平稳控制要求.最后以Shell公司的典型重油分馏塔控制问题为例进行仿真,验证算法的有效性和可行性.     

基于神经网络的多变量协调预估控制器

针对操作变量多于被控变量的“胖”系统，提出了一种多变量协调控制系统，本控制系统在保证对关键变量实施定点控制的同时，还尽可能将操作变量协调至理想操作区，以达到优化操作变量的目的，文中将生产过程中复杂的操作要求和优化规则归纳整为一系列多变量优化协调逻辑，并进而用人工神经网络予以实现，用预估控制技术实现控制。这种协调控制器结构充分发挥了神经元网络和预估控制算法两方面的优势。在石油分馏塔上实现的多变量协调     

间歇过程的批内自优化控制

针对间歇过程的实时优化问题, 提出了一种基于自优化控制的批内优化方法. 以测量变量的线性组合为被控变量, 在单批次内跟踪控制被控变量实现间歇过程的实时优化. 根据是否在间歇过程的不同阶段切换被控变量, 给出了两种自优化控制策略, 对每种策略又分别提出两种设定轨线选取方案. 为求解这些情形下的最优被控变量(组合矩阵), 以最小化平均经济损失为目标, 推导了组合矩阵和经济损失之间的函数关系, 分别将其描述为相应的非线性规划问题. 在此基础上, 进一步引入了扩张组合矩阵, 将这些非线性规划问题归纳为求解扩张组合矩阵的一致形式(扩张组合矩阵具有不同的结构约束), 并推导得到了其中一种方案的解析解计算方法. 以一个间歇反应器为研究对象, 验证了方法的有效性.     

具有多优先级的多变量实时在线决策支持系统

生产过程中,操作人员的操作经验影响生产效益和安全,为此本文结合双层预测控制中稳态目标计算思想,提出了一种具有优先级的实时在线决策支持系统.针对实际生产过程无法确定准确的代价系数的问题,引入操作优先级的思想,结合稳态目标计算层被控变量的优先级优化方法,计算最优操作目标(被控变量和操作变量),解决了模块多变量操作指导中每层模块无法区分变量重要性的问题,并说明了二者在结构上的相似性.最后给出应用本文提及的方法进行决策支持的一个例子,验证该方法的有效性.     

数据中心制冷系统非线性模型预测控制

数据中心制冷系统具有非线性、强耦合和大滞后特性,目前常用的PID方法无法实现系统整体能效提升,而现有非线性优化算法计算量大,不易工程实现.鉴于此,提出一种数据中心制冷系统模型预测控制策略,上层优化层设计预测控制器,其目标为在满足制冷要求的前提下降低系统能耗,优化层采用神经网络作为反馈控制器,将系统整体优化目标函数作为神经网络控制器优化性能指标,结合变分法与随机梯度下降法,通过滚动优化求取下层各回路被控变量最优设定值,算法占用存储区适中、计算量小;下层现场控制层通过实时控制使各回路被控变量跟踪最优设定值,可以在不破坏原有现场控制系统的情况下实现性能优化.构建Trnsys-Matlab联合仿真平台,针对系统夏季、过渡季和冬季的控制策略进行仿真实验.结果表明,所提出控制策略能够在满足数据中心安全运行的前提下,实现系统整体能效提升,且具有良好的鲁棒性.     

飞行器动力系统多变量控制结构设计研究

针对飞行器动力系统多变量控制系统结构设计问题,提出了以相关增益矩阵方法确定发动机多变量控制方案中被控参数与控制量的一一对应关系,阐明了相关增益矩阵的意义.以二输入二输出系统为例推导了相关增益矩阵的比值计算方法.并以某型三输入三输出的变循环发动机为仿真算例,计算了其各被控参数和控制量的相关关系.根据其相关关系,确定了该变循环发动机多变量控制系统结构,并分析了其相关关系对飞行器动力系统多变量控制方案确定的意义.仿真算例表明,利用相关增益矩阵方法可以定量地确定系统各控制量和被控参数的相关关系,从而可以避免传统方法仅通过经验定性确定其相关关系的缺点.     

稳定广义预测控制与性能分析

基于已有的一种稳定广义预测控制(SGPC)结构,以参考信号为优化变量对目标函数进行寻优,求出了控制器的滚动最优控制律,该控制律可以无静差地跟踪常数设定值.文中给出并证明了SGPC闭环控制系统的稳定性充分条件.数值仿真表明,该SGPC控制器可以保证闭环系统无稳态误差,并且适用于具有近似相消零极点的被控对象.     

不确定时滞系统的指定衰减度保代价可靠控制

针对一类含有时变时滞的不确定参数线性系统,研究了在执行器发生故障情况下系统具有指定衰减度的保代价可靠控制器设计问题。系统中的参数不确定性满足广义匹配条件,时变时滞及其变化率有界,执行器失效采用增益故障模型。系统的性能函数是含有指数项和故障输入项的积分二次型函数。经过适当的状态变换,将原系统的指定衰减度保代价可靠控制问题转化为另一个等价系统的保代价可靠控制问题。根据Lyapunov稳定性理论,得到了系统存在指定衰减度保代价可靠控制器应满足的一个矩阵不等式,进一步将这个矩阵不等式转化为线性矩阵不等式（LMI）,并给出了系统的保代价表达式。利用论文方法设计的指定衰减度保代价可靠控制器能够使得时滞系统对于任意允许的不确定量以及执行器故障都保持鲁棒可靠指数稳定,并且使系统具有保代价的性能指标。     

可实现多种控制目标的模型预测控制新算法

模型预测控制(MPC)采用集中控制的策略,主要应用于多输入多输出(MIMO)复杂过程的工艺对象.工艺过程对不同的控制变量,往往有不同的控制要求,如定值控制、区间控制或单边控制.当前的控制算法未考虑单边控制功能,而且形式繁琐、求解不便.为此,本文采用目标规划的思想,提出具有线性规划形式并且可实现多目标的模型预测控制新算法.将新算法用于多个典型过程对象,证实算法不仅能够控制常规目标,而且能够解决单边控制问题,实现了真正的多目标控制.     

Sliding mode control for a class of nonlinear discrete-time networked systems with multiple stochastic communication delays

In this article, a sliding mode control problem is studied for a class of uncertain nonlinear networked systems with multiple communication delays. A sequence of stochastic variables obeying Bernoulli distribution is applied in the system model to describe the randomly occurring communication delays. The discrete-time system considered is also subject to parameter uncertainties and state-dependent stochastic disturbances. A novel discrete switching function is proposed to facilitate the sliding mode controller design. The sufficient conditions are derived by means of the linear matrix inequality (LMI) approach. It is shown that the system dynamics in the specified sliding surface is robustly exponentially stable in the mean square if two LMIs with an equality constraint are feasible. A discrete-time SMC controller is designed that is capable of guaranteeing the discrete-time sliding-mode reaching condition of the specified sliding surface. Finally, a simulation example is given to show the effectiveness of the proposed method.     

Robust control for a class of nonlinear networked systems with stochastic communication delays via sliding mode conception

This paper deals with the robust control problem for a class of uncertain nonlinear networked systems with stochastic communication delays via sliding mode conception (SMC). A sequence of variables obeying Bernoulli distribution are employed to model the randomly occurring communication delays which could be different for different state variables. A discrete switching function that is different from those in the existing literature is first proposed. Then, expressed as the feasibility of a linear matrix inequality (LMI) with an equality constraint, sufficient conditions are derived in order to ensure the globally mean-square asymptotic stability of the system dynamics on the sliding surface. A discretetime SMC controller is then synthesized to guarantee the discrete-time sliding mode reaching condition with the specified sliding surface. Finally, a simulation example is given to show the effectiveness of the proposed method.     

Stabilising agent design for the control of interconnected systems

This article presents a new control design strategy for stabilising large-scale interconnected systems operating in semi-automatic control modes. The large-scale system is modelled by subsystems connected to each other in an arbitrary configuration. Each subsystem is regulated by a dedicated multivariable controller that also allows for a manual control mode. The notion of asymptotically positive realness constraint (APRC) is introduced and applied for deriving the interconnection stabilisability condition in the time domain. The interactions between subsystems are taken into consideration in the stability condition. The APRC is subsequently employed in the so-called stabilising agent to accommodate the closed-loop control and man-in-the-loop coexistence. The multipliers of the APRC quadratic supply rate are updated on-the-fly to ensure that the constraint satisfaction of stabilising agents is recursively feasible. The stabilising agents are developed independently from the control law under the same auspice controller. Due to this independence, operational errors from the manual control adjustments, that may destabilise the control systems, can be avoided. The decentralised agents render stabilising bounds for the manipulated variables in the automatic control mode, and at the same time, provide warning signals and manipulation guidance for the operators to prevent possible plant-wide destabilisation in the manual control mode. Our main results are illustrated through numerical simulations for an industrial modular system.     

MPC for tracking zone regions

This paper deals with the problem of tracking target sets using a model predictive control (MPC) law. Some MPC applications require a control strategy in which some system outputs are controlled within specified ranges or zones (zone control), while some other variables – possibly including input variables - are steered to fixed target or set-point. In real applications, this problem is often overcome by including and excluding an appropriate penalization for the output errors in the control cost function. In this way, throughout the continuous operation of the process, the control system keeps switching from one controller to another, and even if a stabilizing control law is developed for each of the control configurations, switching among stable controllers not necessarily produces a stable closed loop system. From a theoretical point of view, the control objective of this kind of problem can be seen as a target set (in the output space) instead of a target point, since inside the zones there are no preferences between one point or another. In this work, a stable MPC formulation for constrained linear systems, with several practical properties is developed for this scenario. The concept of distance from a point to a set is exploited to propose an additional cost term, which ensures both, recursive feasibility and local optimality. The performance of the proposed strategy is illustrated by simulation of an ill-conditioned distillation column.     

Nonlinear dynamic allocator for optimal input/output performance trade-off: Application to the JET tokamak shape controller

In this paper, we revisit the dynamic allocation scheme for input redundant plants proposed in Zaccarian (2009). within a set-point regulation setting and propose generalizations that apply to cases where the plant under consideration is not input redundant but the control specifications allow us to modify the set-point regulation within certain bounds. In this case, the input allocator is extended to the nonredundant case by allowing it to improve the input allocation selection at the price of some output regulation error. We also establish here desirable convergence properties of the allocator, which were only addressed for the linear case in Zaccarian (2009). In particular, we design the allocator dynamics as the gradient of a cost function and establish its convergence to the minimum of the cost function under some mild conditions. The allocation scheme is applied to the JET tokamak shape controller by illustrating its capabilities to enforce coil currents selections that tend to move away from the saturation limits within the allowable degrees of freedom.     

Model predictive position control of permanent magnet synchronous motor servo system with sliding mode observer

In order to improve the control accuracy and robustness of permanent magnet synchronous motor (PMSM) servo system, a new non-cascade predictive control strategy is proposed to replace the traditional three-closed-loop servo control in this paper. The design process is as follows. First, the state variables and a discrete-time state space model of motor are defined; meanwhile, the lumped term of disturbance is included in the motor model. Second, the optimal cost function is chosen to design the model predictive controller (MPC). Then, according to the uncertain parameters and external disturbance, the sliding mode observer (SMO) is introduced to estimate and compensate the disturbance. Finally, the comparative experimental result proves that the proposed predictive control method not only has less regulation parameters but also has high position tracking accuracy and strong anti-interference ability even under different conditions.     

Boundary feedback stabilisation of a flexible robotic manipulator with constraint

In this paper, the flexible robotic manipulator is modelled as a distributed parameter system, represented by a group of partial differential equations and ordinary differential equations. Control is designed at the boundary of the robotic manipulator based on integral-barrier Lyapunov function to suppress the vibration of the elastic deflection and track the desired angular position. With the proposed boundary control, the manipulator can be driven to the desired set-point with angular position and elastic deflection stay under the former setting constraint. Uniformed boundedness of the closed-loop system under the unknown time-varying disturbance is achieved. Stability analysis of the closed-loop system is given by employing the Lyapunov stability theory. Simulation results illustrate the effectiveness of the proposed boundary controller for ensuring output constraint and suppressing vibrations.     

Advanced control and on-line process optimization in multilayer structures

The paper demonstrates the place, role and mutual interaction of advanced control algorithms and on-line set-point optimization in process control structures. First, a multilayer control structure resulting from a functional decomposition is briefly presented. The role and selected realizations of advanced control algorithms, in particular mostly applied now model predictive control (MPC) ones, at direct control and supervisory constraint control layers is discussed. Then possible solutions to on-line set-point optimization, depending of disturbance dynamics, are presented: dynamic set-point optimization including involved structures based on temporal decomposition, and steady-state set-point optimization for cases with disturbance dynamics both much slower than and comparable with the process dynamics. For the last case, important in industrial practice, different structures of interaction and even integration of MPC and steady-state optimization are discussed. The topics are illustrated by briefly presented examples, selected from given references.