Petri网上的禁止状态监控器综合

给出了Petri网上广义互斥约束的最大允许监控器综合方法,其中该监控问题满足两个条件：正权值禁止库所的影响子网是状态机;负权值禁止库所的输入和输出变迁均只有一个输入库所.首先得到了监控器存在的充分和必要条件;其次构造了约束等价转换的方法,该方法可将存在不可控变迁的监控问题简化为相当于变迁全部可控的监控问题.最后通过一个例子说明了该方法的可行性.     

协同监控和诊断中一致性求解技术研究

针对制造过程及设备的状态监控问题,提出了监控对象的模糊量状态空间的表示方 法,在此基础上提出了一种协同一致性融合求解技术,基于监控对象的模糊量状态空间,将 各求解结果一致性融合问题转化为群决策环境下的梯形模糊数表示的模糊意见的综合问题． 文中最后通过FMS中加工系统状态监控的实例对算法进行了验证,证明了方法的有效和可行 性．     

结合水平集方法和形状约束Snake模型的左心室MRI图像分割

提出结合水平集方法和形状约束Snake模型的左心室MRI图像分割算法.由于左心室存在弱边缘、与周围的组织之间存在低对比度区域,Snake模型分割左心室MRI图像时,将会出现变形曲线泄漏现象.通过对训练图像的配准、变化模式的分析,定义左心室的边界形状变化允许空间.根据心脏MRI图像的特点,使用水平集方法在平均形状周围构造形状约束能量场.在Snake模型中增加形状约束能量项后,能够有效处理变形曲线的泄漏问题.通过将演化曲线投影到形状允许空间,对其施加形状约束.心脏MRI图像的分割实验证明了模型的有效性.     

约束满足问题求解的符号OBDD桶消元算法

桶消元算法是求解约束满足问题的一种典型推理方法.针对桶消元算法面临的状态空间爆炸问题,将有序二叉决策图( OBDD)技术与该算法结合起来,给出了约束满足问题的一种求解算法.通过对约束满足问题中变量和域值的编码,将CSP问题转化为命题可满足性问题,给出了约束满足问题的OBDD表示方法;基于桶消元的算法思想,在约束满足问题...     

GC-MCR:有向图约束指导的并发缺陷检测方法

约束求解应用到程序分析的多个领域,在并发程序分析方面也得到了深入的应用.并发程序随着多核处理器的快速发展而得到广泛使用,然而并发缺陷对并发程序的安全性和可靠性造成了严重的影响,因此,针对并发缺陷的检测尤为重要.并发程序线程运行的不确定性导致的线程交织爆炸问题,给并发缺陷的检测带来了一定挑战.已有并发缺陷检测算法通过约减无效线程交织,以降低在并发程序状态空间内的探索开销.比如,最大因果模型算法把并发程序状态空间的探索问题转换成约束求解问题.然而,其在约束构建过程中会产生大量冗余和冲突的约束,大幅度增加了约束求解的时间以及约束求解器的调用次数,降低了并发程序状态空间的探索效率.针对上述问题,提出了一种有向图约束指导的并发缺陷检测方法 GC-MCR (directed graph constraint-guided maximal causalityreduction).该方法旨在通过使用有向图对约束进行过滤和约减,从而提高约束求解速度,并进一步提高并发程序状态空间的探索效率.实验结果表明:GC-MCR方法构建的有向图可以有效优化约束的表达式,从而提高约束求解器的求解速度并减少求解器的调用次...     

基于GMEC转换算法的Petri网结构控制器综合方法

针对含不可控变迁Petri网系统禁止状态控制器设计问题,提出了一种基于矩阵变换和整数线性规划的结构控制器综合方法。该方法的关键是对代表系统合法状态的广义互斥约束(generalized mutual exclusion constraint, GMEC)进行转换。首先,根据Petri网系统的关联矩阵,将库所集分为无关库所集、不可控库所集和补足库所集。其次,通过对非允许GMEC中补足库所的权值和不可控库所的权值进行处理,并运用整数线性规划将非允许GMEC转换为允许GMEC。在允许GMEC的基础上,根据库所不变量原理设计出Petri网系统的结构控制器。最后,以某零件加工系统为例验证了所提方法的泛用性和高效性,为实际智能制造系统的监督控制器设计提供有效参考方案。     

Petri网的一类禁止状态问题的混合型监控器算法设计

针对广义互斥约束下Petri网的不可控影响子网为状态机的一类禁止状态问题,给出了观测器的设计方法,并基于观测器得到了求解最大允许控制策略的算法.利用观测器将广义互斥约束简化为单禁止库所约束,并将存在不可控变迁的问题简化为相当于变迁全部可控的问题,这有效地解决了不可控变迁带来的计算复杂性问题.最后,利用一个地铁交通调度示例验证和说明该监控器设计方法.     

不确定广义系统的鲁棒脉冲耗散控制

本文在状态空间下, 基于线性矩阵不等式(LMI)的方法, 研究了一类不确定广义系统在允许的不确定性下的脉冲耗散控制问题, 并且通过解线性矩阵不等式得到了此类不确定广义系统对于允许的不确定性的鲁棒脉冲耗散状态反馈控制器和输出反馈控制器, 最后通过数值算例验证了定理的可行性.     

带状态约束的疏浚最优控制问题转化方法

疏浚作业系统追求疏浚产量最大化,同时需要保证横移土壤切削过程和泥浆管道输送过程稳定安全的运行,可归结为带状态不等式约束的线性二次型最优跟踪控制问题。针对该问题,提出一种系统状态空间增维的转化方法,引入辅助状态变量和控制变量,将状态不等式约束转化为等式约束,并构建具有控制安全性的等价疏浚系统。仿真结果表明,该方法能够较好地提高泥浆浓度,同时又可以限制系统中主要状态量长时间的过载运行,从而有效增强挖泥船疏浚施工的安全性与平稳性。     

基于输入输出线性化的连续系统非线性模型预测控制

非线性约束预测控制关键是求得可行性优化解. 输入输出反馈线性化是非线性控制一种常用的方法, 其系统的初始线性输入约束转化成非线性基于状态的约束, 因而无法采用常规的二次规划(QP)求解优化问题. 针对连续状态空间模型系统, 本文提出迭代二次规划方法来寻求非线性优化解. 为了保证算法的收敛性, 系统加入另外一种迭代算法来保证其在整个预测时域上能得到可行解. 仿真控制结果表明了该方法的有效性.     

Maximally permissive supervisor synthesis based on a new constraint transformation method

The method is proposed to design the maximally permissive and efficient supervisor for enforcing linear constraints, in which the weights of places are not negative, on ordinary Petri nets with uncontrollable transitions. First, the weakly admissible linear constraint is introduced. Second, a method is proposed to design the monitor place for enforcing a weakly admissible linear constraint on Petri nets. Third, a theorem proving that a linear constraint can be equivalently transformed at an uncontrollable transition into a disjunction of new constraints is proposed. Fourth, using this theorem, an algorithm is presented to equivalently transform a linear constraint, each place weight of which is not negative, into a disjunction of weakly admissible ones. Lastly, the supervisor, which consists of the plant net and a set of monitor places, is designed for the weakly admissible linear constraints calculated by the above algorithm.     

基于观测器的具有反馈增益变化的广义系统H∞控制

介绍了一类广义系统H∞控制器的设计过程.通过设计基于观测器的具有反馈增益变 化的广义控制器,借助于带有广义约束的广义代数Riccati不等式(GARI),给出闭环广义系统 容许且传递函数的H∞范数有界的充要条件.通过解带有广义约束的GARI得到该控制器的设 计方法,并且控制器的参数矩阵只需使观测器容许.     

Constraint‐admissible sets for systems with soft constraints and their application in model predictive control

Constraint‐admissible sets have been widely used in the study of control systems with hard constraints. This paper proposes a generalization of the maximal constraint‐admissible set for constrained linear discrete‐time systems to the case where soft or probabilistic constraints are present. Defined in the most obvious way, the maximal probabilistic constraint‐admissible set is not invariant. An inner approximation of it is proposed which is invariant and has other nice properties. The application of this approximate set in a model predictive control framework with probabilistic constraints is discussed, including the feasibility and stability of the resulting closed‐loop system. The effectiveness of the proposed approach is illustrated via numerical examples. Copyright © 2011 John Wiley & Sons, Ltd.     

Affine and predictive control policies for a class of nonlinear systems

The input-state linear horizon (ISLH) for a nonlinear discrete-time system is defined as the smallest number of time steps it takes the system input to appear nonlinearly in the state variable. In this paper, we employ the latter concept and show that the class of constraint admissible N-step affine state-feedback policies is equivalent to the associated class of constraint admissible disturbance-feedback policies, provided that N is less than the system’s ISLH. The result generalizes a recent result in [Goulart, P. J., Kerrigan, E. C., Maciejowski, J. M. (2006). Optimization over state feedback policies for robust control with constraints. Automatica, 42(4), 523-533] and is significant because it enables one: (i) to determine a constraint admissible state-feedback policy by employing well-known convex optimization techniques; and (ii) to guarantee robust recursive feasibility of a class of model predictive control (MPC) policies by imposing a suitable terminal constraint. In particular, we propose an input-to-state stabilizing MPC policy for a class of nonlinear systems with bounded disturbance inputs and mixed polytopic constraints on the state and the control input. At each time step, the proposed MPC policy requires the solution of a single convex quadratic programme parameterized by the current system state.     

基于容许集的变终端约束MPC控制

针对一类输入和状态受约束的离散线性系统,提出一种基于Ⅳ步容许集的变终端约束集模型预测控制方法.首先给出多面体不变集序列作为终端约束集的离线模型预测控制算法,扩大了终端约束集.为进一步扩大初始状态可镇定区域,引入N步容许集,设计了基于容许集的变终端约束集模型预测控制方法.该算法采用离线设计、在线优化方法,实现了系统渐近稳定,不仅降低了在线运算量,而且扩大了初始状态可镇定区域.仿真结果表明了算法的有效性.     

Optimized robust control invariance for linear discrete-time systems: Theoretical foundations

This paper introduces the concept of optimized robust control invariance for discrete-time linear time-invariant systems subject to additive and bounded state disturbances. A novel characterization of two families of robust control invariant sets is given. The existence of a constraint admissible member of these families can be checked by solving a single and tractable convex programming problem in the generic linear-convex case and a standard linear/quadratic program when the constraints are polyhedral or polytopic. The solution of the same optimization problem yields the corresponding feedback control law that is, in general, set-valued. A procedure for selection of a point-valued, nonlinear control law is provided.     

含不可控变迁的Petri网监控器设计

在基于Petri网建模的含不可控事件的离散事件系统监控器设计中,当给定的控制目标为Parikh矢量约束时,提出通过转换矩阵R将不可控变迁导致的非法不等式约束变换为允许约束,并求得相应监控器.构造矩阵方程求解R,通过矩阵方程的相容性判断R的存在性,并给出利用广义逆矩阵求解R的算法,得到对应的允许约束和监控器.同时提出代价函数,用于寻找控制观测代价最小的监控器.最后通过实例验证了该算法的正确性和有效性.     

Optimal MPC for tracking of constrained linear systems

Model predictive control (MPC) is one of the few techniques which is able to handle constraints on both state and input of the plant. The admissible evolution and asymptotic convergence of the closed-loop system is ensured by means of suitable choice of the terminal cost and terminal constraint. However, most of the existing results on MPC are designed for a regulation problem. If the desired steady-state changes, the MPC controller must be redesigned to guarantee the feasibility of the optimisation problem, the admissible evolution as well as the asymptotic stability. Recently, a novel MPC has been proposed to ensure the feasibility of the optimisation problem, constraints satisfaction and asymptotic evolution of the system to any admissible target steady-state. A drawback of this controller is the loss of a desirable property of the MPC controllers: the local optimality property. In this article, a novel formulation of the MPC for tracking is proposed aimed to recover the optimality property maintaining all the properties of the original formulation.     

基于虚约束的Acrobot动态伺服控制统一设计方法

提出一种基于虚约束的统一设计方法,以解决Acrobot系统中动态伺服控制问题,使系统沿着经过目标点的周期轨迹运动.将虚约束设计、虚约束作用下系统零动态微分方程分析以及轨道周期性判定相结合,获得了符合目标的周期轨道方程;基于Lyapunov方法设计了光滑反馈控制器,克服了基于线性二次型调节器（LQR）的控制器对零动态微分方程解析解的依赖性问题.实际算例的仿真结果表明了统一设计方法的有效性.     

MPC for tracking piecewise constant references for constrained linear systems

In this paper, a novel model predictive control (MPC) for constrained (non-square) linear systems to track piecewise constant references is presented. This controller ensures constraint satisfaction and asymptotic evolution of the system to any target which is an admissible steady-state. Therefore, any sequence of piecewise admissible setpoints can be tracked without error. If the target steady state is not admissible, the controller steers the system to the closest admissible steady state.These objectives are achieved by: (i) adding an artificial steady state and input as decision variables, (ii) using a modified cost function to penalize the distance from the artificial to the target steady state (iii) considering an extended terminal constraint based on the notion of invariant set for tracking. The control law is derived from the solution of a single quadratic programming problem which is feasible for any target. Furthermore, the proposed controller provides a larger domain of attraction (for a given control horizon) than the standard MPC and can be explicitly computed by means of multiparametric programming tools. On the other hand, the extra degrees of freedom added to the MPC may cause a loss of optimality that can be arbitrarily reduced by an appropriate weighting of the offset cost term.