具有输入约束的模糊Markov跳变系统H∞控制

针对由Takagi-Sugeno模糊模型描述的非线性Markov跳变系统,讨论其在控制输入受约束情况下的H∞控制问题。基于不变集理论,通过模态依赖的椭圆状态集合,实现对控制输入的范数约束,并提出使得模糊跳变系统随机稳定且满足一定输入输出H∞增益的模态依赖的控制器存在条件。结合线性矩阵不等式变换,给出状态反馈最优H∞控制器的设计方法,仿真示例说明了设计方法的有效性。     

基于观测器的不确定时滞Markov跳变系统H∞控制

讨论了一类线性不确定时滞Markov跳变系统的控制问题,针对能量有界的输入噪声,设计了基于观测器的优化鲁棒H∞控制器。基于鲁棒控制理论,通过对重构的观测器系统的分析,提出了使得系统随机稳定且满足一定输入输出H∞增益的模态依赖的控制器存在条件。结合构造的Lyapunov函数和线性矩阵不等式变换,给出了H∞控制器的设计方法,并将其描述为一个优化问题。基于观测器设计的优化H∞控制器使系统具有随机稳定性,状态的跟踪性能好,抑制干扰能力强,满足所给的范数指标。仿真示例说明了设计方法的有效性。     

Markov 跳变系统输出峰值控制

针对具有时间演化和事件驱动两类动态机制的Markov线性跳变系统,研究其被控输出的峰值控制问题,当系统外部输入能量有界时,保证系统输出的峰值在给定的上限内.从工程实现的角度,利用随机稳定性分析,以一种次优的峰值指标代替最优性能,反馈控制器的构造依赖于系统模态,可直接利用耦合线性矩阵不等式求解.仿真示例对结果进行了验证,并对比讨论了应用鲁棒控制思想的控制器设计情形,说明模态跳变转移信息对系统指标与控制的影响.     

基于收缩序列集的约束PWA系统鲁棒预测控制

针对一类有附加有界扰动的约束离散时间分段仿射系统,提出了一种具有鲁棒稳定性保证的模型预测控制方法.通过多参数规划采用一步预测时域法离线计算具有收缩性质的鲁棒可稳定集系列,把它们作为优化问题的稳定约束,并选择一个鲁棒正不变集作为终端约束,使得优化问题的可行性即保证了系统的鲁棒稳定性.仿真实例表明提出的控制方法可使系统渐近稳定到鲁棒正不变集.     

具有饱和执行器跳变系统的鲁棒模型预测控制

针对一类具有饱和执行器的不确定离散Markov跳变系统,提出鲁棒模型预测控制器设计方法。为便于工程应用,考虑被控跳变系统各模态下的动态系统参数以及各模态间的跳变转移模态均存在不确定性的情形,这些不确定性均以凸多面体的形式给出。预测控制器的求解通过在每一个采样时刻优化无穷时域的最坏二次性能指标实现,该预测控制序列在每个采样时刻表现为具有饱和特性的状态反馈控制律,从而使得所形成的闭环系统鲁棒均方稳定。为方便求解,将控制器的求解转化为以线性矩阵不等式形式给出的正半定规划(SDP)问题。数值示例验证了所提方法的有效性。     

基于LMIs方法-不确定性随机分布型时滞Markov跳变系统的鲁棒镇定

研究了一类具有不确定性Markov跳变参数的线性分布型时滞系统.考虑了具不确定性、Markov跳变参数、及分布型时滞系统的鲁棒均方稳定性问题.设计了系统的鲁棒状态反馈镇定控制器.采用了Grownwall Bellman不等式,基于线性矩阵不等式(LMIs)方法,给出了鲁棒稳定性的LMI时滞依赖的代数判据,从而降低了判据的保守性.同时得到了相应的离散时滞情形下的结果.     

离散Markov跳变线性系统最优控制

针对离散Markov跳变系统,研究其最优控制问题。首先确立一个二次型代价函数,然后运用随机贝尔曼动态规划法,结合Markov跳变系统特性求解贝尔曼方程,获得了完全状态信息情形下Markov跳变系统的最优控制器和黎卡提差分方程;进而将其推广到不完全状态信息情形,利用观测向量获得状态的后验概率密度函数,推导了最优控制器的解析结构和相应的求解算法;最后通过数值仿真验证了所得控制器的有效性。     

多智能体系统的分布式预测控制器设计

针对多智能体系统的跟踪及编队控制问题,研究其分布式预测控制器的设计方法。考虑时变通讯拓扑情况,在优化问题中附加一项辅助约束,保证了优化问题的递归可行性及多智能体系统的闭环指数稳定性。在每个采样时刻,各智能体根据通讯拓扑实时更新其代价函数及辅助约束,通过求解带有状态、输入及辅助约束的优化问题来计算其控制输入。仿真结果验证了结论的有效性。     

输入受限的参数摄动分段仿射系统鲁棒控制

研究了一类有输入约束和参数摄动的离散分段仿射系统的稳定性及其控制器的设计方法。提出了各分段仿射子系统离散时间状态集的椭圆集表示方法,结合Lyapunov意义的稳定约束,以LMIs形式给出了闭环分段仿射系统的鲁棒稳定充分条件和控制器的设计方法。同时将常见的输入约束转换成LMIs,通过LMIs的凸约束保证了求解控制器的可行性。最后通过仿真实例验证了所提方法的有效性。     

一类非线性时滞系统的模糊预测控制

利用模糊T-S模型对一类输入受限的非线性时滞系统进行建模;在此基础上,提出了模糊预测控制,推导了预测性能指标上界,将稳定性约束,输入约束变换成容易求解的线性矩阵不等式形式。采用了并行分布补偿预测控制器,基于李雅普诺夫函数和线性矩阵不等式方法给出滚动时域优化的充分条件,证明了闭环系统的稳定性,仿真结果验证了所提方法的有效性。     

Constrained predictive control of nonlinear stochastic systems

The receding horizon control (RHC) problem is considered for nonlinear Markov jump systems which can be represented by Takagi-Sugeno fuzzy models subject to constraints both on control inputs and on observe outputs. In the given receding horizon, for each mode sequence of the T-S modeled nonlinear system with Markov jump parameter, the cost function is optimized by constraints on state trajectories, so that the optimization control input sequences are obtained in order to make the state into a terminal invariant set. Out of the receding horizon, the stability is guaranteed by searching a state feedback control law. Based on such stability analysis, a linear matrix inequality approach for designing receding horizon predictive controller for nonlinear systems subject to constraints both on the inputs and on the outputs is developed. The simulation shows the validity of this method.         

一类不确定混合线性时滞系统鲁棒控制问题研究

讨论了一类不确定混合线性时滞系统在其Markov跳跃参数所处模态非精确可测得情况下的鲁棒控制问题。给出了非匹配条件下系统不确定部分范数上界已知时,使系统呈均方意义下指数稳定的保代价控制充分条件。并根据混合系统模式下的LaSalle稳定性定理,对匹配条件下系统不确定部分的未知范数上界给出了一种参数自适应估计方法,设计了相应的鲁棒自适应控制律以实现混合线性时滞系统以概率1渐近稳定。鲁棒自适应控制律的设计仅依赖于跳跃参数的非精确测量值。     

有数据包丢失的网络控制系统H∞状态反馈控制

研究了有数据包丢失的网络控制系统H∞状态反馈控制问题。考虑网络控制系统中存在数据包丢失的情况,丢包过程建模为有限状态的马尔可夫过程。在此模型的基础上,利用马尔可夫跳变线性系统的理论和线性矩阵不等式方法,给出了保持系统均方稳定且满足H∞性能的控制器存在的充分条件,并给出了相应的控制器设计方法。最后数例仿真结果表明了控制器设计方法的有效性。     

Receding horizon H∞ control for discrete-time Markovian jump linear systems

Receding horizon H∞ control scheme which can deal with both the H∞ disturbance attenuation and mean square stability is proposed for a class of discrete-time Markovian jump linear systems when minimizing a given quadratic performance criteria. First, a control law is established for jump systems based on pontryagin’s minimum principle and it can be constructed through numerical solution of iterative equations. The aim of this control strategy is to obtain an optimal control which can minimize the cost function under the worst disturbance at every sampling time. Due to the difficulty of the assurance of stability, then the above mentioned approach is improved by determining terminal weighting matrix which satisfies cost monotonicity condition. The control move which is calculated by using this type of terminal weighting matrix as boundary condition naturally guarantees the mean square stability of the closed-loop system. A sufficient condition for the existence of the terminal weighting matrix is presented in linear matrix inequality (LMI) form which can be solved efficiently by available software toolbox. Finally, a numerical example is given to illustrate the feasibility and effectiveness of the proposed method.     

On Stabilization of It Stochastic Time-Varying Systems

The stabilization with receding horizon control(RHC) of It stochastic time-varying systems is studied in this paper. Based on monotonically non-increasing of optimal cost and stochastic Lyapunov stability theory, a necessary and sufficient stabilization condition on the terminal weighting matrix is proposed, which guarantees the mean-square stability of the closed-loop system. The explicit receding horizon controller is obtained by employing stochastic maximum principle. Simulations demonstrate the effectiveness of the proposed method.     

Robust H∞ filtering for discrete-time systems with Markovian switching and time-delays

The robust H∞ filtering problem for uncertain discrete-time Markovian jump linear systems with modedependent time-delays is investigated. Attention is focused on designing a Markovian jump linear filter that ensures robust stochastic stability while achieving a prescribed H∞ performance level of the resulting filtering error system, for all admissible uncertainties. The key features of the approach include the introduction of a new type of stochastic Lyapunov functional and some free weighting matrix variables. Sufficient conditions for the solvability of this problem are obtained in terms of a set of linear matrix inequalities. Numerical examples are provided to demonstrate the reduced conservatism of the proposed approach.     

一类模糊跳变系统的采样控制器设计

针对含有马尔可夫跳变参数的模糊系统,采用多李亚普诺夫函数方法,设计采样控制器,保证模糊跳变系统指数均方稳定和随机稳定。为便于工程应用,考虑到控制系统多为数字控制系统,计算机的输入、输出控制信号是数字信号,而被控对象常为连续时间系统,文中采用离散的控制器控制连续的被控对象。系统模型中的每个跳变模态对应的子系统均是T-S模糊系统,对每个模糊子系统采用平行分布补偿的控制方法,保证子系统渐近稳定。控制器的求解等效为线性矩阵不等式的可解性问题,便于求解。数值仿真结果验证了方法的可行性和有效性。     

Stabilization of switched linear systems with constrained inputs

The stabilization of switched linear systems with constrained inputs(SLSCI) is considered. The authors design admissible linear state feedbacks and the switching rule which has a minimal dwell time(MDT) to stabilized the system.First,for each subsystem with constrained inputs,a stabilizing linear state feedback and an invariant set of the closed-loop system are simultaneously constructed, such that the input constraints are satisfied if and only if the closed-loop system’s states lie inside this set.Then,by constructing a quadratic Lyapunov function for each closed-loop subsystem,an MDT is deduced and an MDT-based switching strategy is presented to ensure the stability of the switched system.