一类多状态时滞不确定系统的鲁棒预测控制

针对一类多状态时滞多面体不确定对象,提出了一种鲁棒预测控制算法。基于参数相关的李亚普诺夫函数,给出了系统稳定的充分条件。通过求解满足若干线性矩阵不等式条件的上界值的最优化问题,获得了约束范围内满足系统鲁棒性能的最优控制律。优化问题的可行性保证了算法的鲁棒稳定性。仿真实例证明了该算法的有效性。     

一类多输入非线性系统的切换控制

研究了一类具有结构不确定性的多输入非线性级联系统的鲁棒不变切换控制问题.控制思想是首先通过鲁棒无源性技术来镇定线性子系统,再借助切换控制技术得到系统轨迹对于指定区域的正不变性,从而得到了该类系统鲁棒不变切换控制的充分条件.在适当的假设下,得到了系统的半全局渐近稳定.设计中无需ISS假设或非线性项的线性增长条件假设.仿真表明了设计方法的有效性.     

连续时间多面体不确定系统的鲁棒预测控制

运用线性矩阵不等式方法,研究连续时间多面体不确定系统的鲁棒预测控制问题。通过在线求解无穷时域二次型性能指标下的"最小-最大"优化问题,给出了分段连续的状态反馈控制器存在的充分条件。运用可控不变集理论,解决在线优化问题的可行性,证明了初始时刻的可行解可以保证闭环系统渐近稳定。仿真证明了控制算法的有效性。     

不确定模糊系统具有指定衰减率的鲁棒保成本控制

结合系统响应速度的考虑,讨论由T-S模糊模型描述的不确定连续非线性系统具有指定衰减率的鲁棒保成本控制问题.基于扩展稳定性条件和等价投影引理,导出系统鲁棒保成本控制器存在的充分条件,并可将此控制器的最优设计问题转化为一组线性矩阵不等式的凸优化问题.研究表明:所设计的控制器不仅能保证闭环不确定模糊系统以衰减率α鲁棒渐近稳定,而且还给出了保成本上界的一个优化值.最后,数值示例验证了该设计方法的有效性.     

Markov跳变系统的单步约束预测控制

针对离散Markov跳变系统,研究带输出约束的单步预测控制问题。首先设计基于模态的单步预测控制器,驱动系统状态到达相应的终端不变集内,以此保证约束预测控制系统的稳定性,接着进一步讨论了系统存在峰值有界噪声输入情形。其中终端不变集是通过在预测时域外寻求一个虚拟的带约束的状态反馈控制器以保证系统的随机稳定性来获得,为计算方便,控制器的优化问题转化为SDP问题,并得到以LMI描述的优化问题。最后对Markov跳变系统描述的经济学动力系统的仿真结果说明了本控制器设计方法能够在峰值有界噪声输入下保证系统的稳定性,并且使控制变量满足给定的约束条件。     

一类非线性级联系统的鲁棒不变切换控制

研究了一类具有结构不确定性的单输入非线性级联系统的鲁棒不变切换控制问题。通过基于鲁棒严格无源技术和线性子系统状态反馈的切换控制来镇定线性子系统,同时保证了指定区域的正不变性,从而得到了该类系统鲁棒不变切换控制的充分条件。在适当的假设下,得到了系统的半全局渐近稳定。设计中无需ISS假设或非线性项的线性增长条件假设。在MATLAB环境下的仿真结果表明了设计的有效性。     

直升机航向鲁棒保性能控制

针对具有时变不确定性且不确定性界为椭球的线性系统提出了一种新的具有自适应机制的鲁棒保性能控制器设计方法.首先,引入一个具有可由自适应律在线调整的可调参数的目标模型,通过该参数来保证由目标模型与被控模型所获得的误差系统渐近稳定.结合保证目标模型稳定性的设计,最终形成保证闭环系统稳定且控制器增益仿射依赖于可调参数的鲁棒保性能跟踪控制器.应用于安装在试验平台上的小型直升机航向控制中,仿真试验表明了该方法的有效性.     

固体氧化物燃料电池系统的鲁棒反馈模型预测控制

固体氧化物燃料电池系统工作过程中燃料与氧化剂的压力差和燃料利用率的变化值是衡量系统运行安全、稳定长效、具有较好鲁棒性的重要指标.本文提出采用离线计算、在线优化相结合的带有输出反馈的鲁棒模型预测控制方法,分别将燃料与氧化剂的压力差和燃料利用率作为输入和输出约束,离线计算目标函数上界及其系列渐近稳定域,在线时对控制量进行精确定位.仿真结果表明,采用了状态反馈的模型预测控制方法能有效克服模型失配问题并迅速获得被控量的预测值,使系统当负载电流发生波动时能克服变化引起的参数偏差,提高了响应速度,增强了系统鲁棒性.     

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。     

基于递归神经网络的伺服系统自适应反步控制

针对伺服系统的系统参数摄动和非线性动态摩擦补偿问题,提出基于递归神经网络(RNN)的自适应反步控制(RNABC)系统设计方法.RNABC系统由反步控制器和鲁棒控制器组成,反步控制器包含RNN不确定观测器,鲁棒控制器则用来消除由于引入不确定观测器而带来的逼近误差.由于自适应反步控制的自适应律源于Lyapunoy函数的,因此系统的稳定性得到了保证.仿真结果表明,对于系统参数摄动和非线性摩擦干扰RNABC能使伺服系统具有很好的跟踪性能.     

Stabilizing model predictive control scheme for piecewise affine systems with maximal positively invariant terminal set

An efficient algorithm is proposed for computing the solution to the constrained finite time optimal control (CFTOC) problem for discrete-time piecewise affine (PWA) systems with a quadratic performance index. The maximal positively invariant terminal set, which is feasible and invariant with respect to a feedback control law, is computed as terminal target set and an associated Lyapunov function is chosen as terminal cost. The combination of these two components guarantees constraint satisfaction and closed-loop stability for all time. The proposed algorithm combines a dynamic programming strategy with a multi-parametric quadratic programming solver and basic polyhedral manipulation. A numerical example shows that a larger stabilizable set of states can be obtained by the proposed algorithm than precious work.     

Observer-based robust predictive control of singular systems with time-delay and parameter uncertainties

The problem of observer-based robust predictive control is studied for the singular systems with norm-bounded uncertainties and time-delay, and the design method of robust predictive observer-based controller is proposed. By constructing the Lyapunov function with the error terms, the infinite time domain "min-max"optimization problems are converted into convex optimization problems solving by the linear matrix inequality (LMI), and the sufficient conditions for the existence of this control are derived. It is proved that the robust stability of the closed-loop singular systems can be guaranteed by the initial feasible solutions of the optimization problems, and the regular and the impulse-free of the singular systems are also guaranteed. A simulation example illustrates the efficiency of this method.     

Robust model predictive control of continuous uncertain systems*

The authors concern robust model predictive control for linear continuous systems with polytopic uncertainties and input constraints. At each sampling time, a piecewise constant control sequence is obtained by solving a set of linear matrix inequalities. The sufficient conditions on the existence of the model predictive control are given, and the robust stability of the closed-loop systems is guaranteed. A simulation example illustrates the efficiency of the proposed method.     

输入受限时滞系统的模型预测控制

对于输入受限的离散时滞系统，提出了一种模型预测控制方法。该方法采用双模控制结构，基于Lyapunov-Krasovskii函数构造了时滞系统的终端不变椭圆集，保证闭环系统稳定性。以LMIs(Linear Matrix Inequalities)形式给出了系统稳定的充分条件，使得该方法简单，易于实现。最后通过仿真验证了本方法的有效性。     

Discrete sliding mode prediction control of uncertain switched systems

The robust stabilization problem for a class of uncertain discrete-time switched systems is presented. A predictive sliding mode control strategy is proposed, and a discrete-time reaching law is improved. By applying a predictive sliding surface and a reference trajectory, combining with the state feedback correction and rolling optimization method in the predictive control strategy, a predictive sliding mode controller is synthesized, which guarantees the asymptotic stability for the closed-loop systems. The designed control strategy has stronger robustness and chattering reduction property to conquer with the system uncertainties. In addition, a unique nonswitched sliding surface is designed. The reason is to avoid the repetitive jump of the trajectories of the state components of the closed-loop system between sliding surfaces because it might cause the possible instability. Finally, a numerical example is given to illustrate the effectiveness of the proposed theory.     

信息不确定下的主动打断项目组合选择问题鲁棒优化

在项目组合选择问题中,历史数据的缺乏以及预测和估计过程中出现的不可避免的误差,会导致模型中的参数无法被准确地估计,进而给决策带来巨大的风险.因此,构建合适的鲁棒优化模型,为企业提供能有效应对参数不确定性的鲁棒解,对企业的风险防范具有极其重要的现实意义.本文首先对确定参数下的主动打断项目组合选择问题数学模型的特点进行了分析.进一步地,介绍了鲁棒优化问题中不确定情境集的概念,并给出了允许管理者根据其偏好确定不确定情境集大小的方法,构建了全新的基于情境的鲁棒优化模型,进而计算出在所规定的不确定情境集内的最坏情境下能保持可行性与最优性的鲁棒解,实现了鲁棒性与最优性间的权衡,最后,通过GAMS/BARON进行了算例分析,验证了模型的合理性与有效性.从理论上,本文首次将鲁棒优化理论扩展到了主动打断项目组合选择问题中,针对现有的项目组合选择问题鲁棒优化理论仅能应对有限个可行解的不足之处,提出了一类新的鲁棒优化方法,使其能够应对具有无穷多可行解的主动打断项目组合问题.从实践上,随着我国高新产业的发展,具有超前性与特殊性的研究与发展(RD)、信息科技与信息系统(IT/IS)等新兴项目的投资日益受到重视.相较于传统项目,这类项目的高度不确定性使得探究项目组合选择问题的鲁棒优化理论日益迫切.故而本文的研究具有明显的理论价值和现实意义.     

Robust predictive control of polytopic uncertain systems with both state and input delays

A robust model predictive control algorithm for discrete linear systems with both state and input delays subjected to constrained input control is presented,where the polytopic uncertainties exist in both state matrices and input matrices.The algorithm optimizes an upper bound with respect to a state feedback control law.The feedback control law is presented based on the construction of a parameter-dependent Lyapunov function.The above optimization problem can be formulated as a LMI-based optimization.The feasibility of the optimization problem guarantees that the algorithm is robustly stable.The simulation results verify the effectiveness of the proposed algorithm.     

Robust passive control for discrete-time T-S fuzzy systems with delays

This article deals with the robust stability analysis and passivity of uncertain discrete-time Takagi-Sugeno (T-S) fuzzy systems with time delays. The T-S fuzzy model with parametric uncertainties can approximate nonlinear uncertain systems at any precision. A sufficient condition on the existence of robust passive controller is established based on the Lyapunov stability theory. With the help of linear matrix inequality (LMI) method, robust passive controllers are designed so that the closed-loop system is robust stable and strictly passive.  urthermore, a convex optimization problem with LMI constraints is formulated to design robust passive controllers with the maximum dissipation rate. A numerical example illustrates the validity of the proposed method.