舰载机着舰侧向回路的滚动时域控制

舰载机着舰问题是一个十分复杂的难题.由于航母的斜角甲板只有几十米宽,故而舰载机要降落在航母上需要十分精确的控制.在横测向控制中,最重要的是控制偏心距.为保持期望的着舰姿态,建立了舰载机横侧向着舰的非线性动力学模型,通过设定期望的着舰位置与姿态,将舰载机横侧向动力学模型的状态转化为误差状态,在攻角为11.7°,空速为70m/s的平衡点设计控制器,采用滚动时域预测控制来解决舰载机着舰的横侧向控制问题,用VC++构建三维仿真平台,在MATLAB上建立控制器模型,运用网络通信发送到三维仿真平台上,控制舰载机实现自动着舰.仿真结果表明滚动时域优化算法可以很好的实现舰载机着舰侧回路非线性系统的航迹跟踪与姿态跟踪.     

舰载机纵向自动着舰控制

对舰载机纵向着舰控制系统进了分析和研究,根据舰船甲板运动引起舰载机着舰点不断变化的问题,加入甲板运动补偿环节,并对补偿律进行了设计;对常规的纵向导引控制律进行了改进设计,引入模糊控制.仿真结果表明,引入甲板运动补偿环节,有效地减小了由于甲板运动造成的着舰误差,提高了着舰的安全性,模糊PID导引控制律比常规的PID导引控...     

舰载机自动着舰可视化仿真

根据舰载机自动着舰系统的原理建立了航母的甲板运动、舰尾流、甲板运动补偿和舰载机着舰的引导控制模型;使用3D Max软件绘制了飞机、航母的三维模型;利用Visual C++和OpenGL软件进行编程,完成了舰载机自动着舰仿真软件的设计,并实现了舰裁机自动着舰的可视化仿真.     

线性时变参数系统的混合鲁棒H2/H∞控制

研究具有模型参数不确定性和外部扰动的一类系统，即线性变参数系统（LPV）。考虑参数在多胞内变化，系统状态方程可以由多胞矩阵描述，利用H2／H∞范数条件与系统状态空间实现的线性矩阵不等式（LMI）之间的等价性，来求出H2／H∞。设计问题的解，同时设计含有极点配置的多胞状态反馈控制器，使闭环系统具有所期望的混合H2／H∞性能和动态特性。给出一个弹簧阻尼器算例进行分析求解，仿真说明了系统的鲁棒稳定性和对脉冲干扰的抑制作用，验证了设计方案的有效性。     

线性时滞系统对时滞参数的自适应控制

针对实际中线性时滞系统的时滞常数往往不能精确已知的缺点,对具有状态滞后的线性时滞系统提出一种对时滞常数的自适应控制方案,其控制器存在的充分条件由一个线性矩阵不等式（LMI）的形式给出,并对整个闭环系统的稳定性进行了分析。     

基于LESO的舰载机纵向着舰动态面抗饱和控制技术

对具有输入饱和、舰尾流扰动以及强耦合性的舰载机纵向自动着舰系统(ACLS)模型,设计了一种基于线性扩张状态观测器(LESO)的高阶系统动态面控制(DSC)策略。在传统DSC的基础上,引入LESO对系统内外扰动进行实时观测和补偿,并在LESO框架内设计了一种基于误差补偿的抗饱和方法,有效抑制了输入饱和对系统性能的影响。采用线性微分跟踪器(LTD)代替传统动态面方法中的一阶低通滤波器,避免了反步法“微分爆炸”的同时输出了所需的微分信号。根据Lyapunov理论进行了稳定性分析,并在MATLAB/Simulink环境下通过仿真验证了所设计方法的有效性。     

不确定关联大系统对时变参数的自适应控制

考虑具有时滞的不确定非线性关联大系统的鲁棒控制问题．假设不确定时变参数为半线性或非线性系统的有界输出，通过对时变不确定参数设计自适应律，从而对不确定参数进行估计．利用线性矩阵不等式技术和自适应参数估计方法，设计出鲁棒自适应控制器，从而保证闭环系统渐近稳定．建立了可由线性矩阵不等式表示的镇定条件．仿真示例说明该方法是有效的．     

线性时变参数系统切换H∞输出反馈控制

考虑时变参数系统的切换H∞控翻问题.提出了由参数触发的切换策略,由此在最小驻冒时间的限制下,将线性时变参数系统分解为若干具有范数有界不确定性的子系统.利用多Lyapunov函数方法分别设计各子系统的输出动态反馈控制器.使在切换策略驱动下构成的闭环系统满足H∞控制性能.仿真算例完整地实现了理论方法,并验证了其有效性.     

变约束高效预测控制

为改善离线鲁棒预测控制算法的最优性引入了变终端约束集的思想,即在原离线方法基础上通过在线优化方法获得构建当前反馈矩阵的凸组合系数,然后再产生当前控制量.与原方法相比增加了在线求解的自由度,改善了系统的最优性.在此基础上,又提出了变约束预测控制(VC-MPC)算法,根据状态所处的不同稳定椭圆域确定其对应的控制约束,在取得...     

Nonlinear model predictive control for models in quasi‐linear parameter varying form

This article presents a nonlinear model predictive control (NMPC) approach based on quasi‐linear parameter varying (quasi‐LPV) representations of the model and constraints. Stability of the proposed algorithm is ensured by the offline solution of an optimization problem with linear matrix inequality constraints in conjunction with an online terminal state constraint. Furthermore, an iterative approach is presented with which the NMPC optimization problem can be handled by solving a series of Quadratic Programs at each time step, this being highly computationally efficient. A practical and simple way of obtaining quasi‐LPV representations of the system using velocity‐based linearization is presented in two examples.     

An improved robust model predictive control for linear parameter‐varying input‐output models

This paper describes a new robust model predictive control (MPC) scheme to control the discrete‐time linear parameter‐varying input‐output models subject to input and output constraints. Closed‐loop asymptotic stability is guaranteed by including a quadratic terminal cost and an ellipsoidal terminal set, which are solved offline, for the underlying online MPC optimization problem. The main attractive feature of the proposed scheme in comparison with previously published results is that all offline computations are now based on the convex optimization problem, which significantly reduces conservatism and computational complexity. Moreover, the proposed scheme can handle a wider class of linear parameter‐varying input‐output models than those considered by previous schemes without increasing the complexity. For an illustration, the predictive control of a continuously stirred tank reactor is provided with the proposed method.     

Aperiodic event-triggered robust model predictive control for linear parameter-varying system with round-robin protocol

This article investigates the robust model predictive control (MPC) problem for networked control systems represented by the linear parameter-varying model, in which an event-triggered strategy and the round-robin (RR) protocol scheduling locate at the sensor-to-controller and controller-to-actuator channels, respectively. By considering the problems of system state immeasurable and communication burden in engineering application, an output feedback controller that combines the aperiodic event-triggered strategy is applied, where the triggering condition is designed in a time-varying fashion. In addition, in order to avoid unexpected data collisions, the RR protocol is utilized to schedule a shared network and guarantee the efficiency of the control system. The controller parameters are obtained by solving an online convex robust MPC optimization problem, and the feasibility of the optimization problem and closed-loop stability are also addressed. The effectiveness of the proposed theoretical results is illustrated by a numerical simulation example.     

An improved robust model predictive control approach to systems with linear fractional transformation perturbations

In this paper, a robust model predictive control approach is proposed for a class of uncertain systems with time-varying, linear fractional transformation perturbations. By adopting a sequence of feedback control laws instead of a single one, the control performance can be improved and the region of attraction can be enlarged compared with the existing model predictive control (MPC) approaches. Moreover, a synthesis approach of MPC is developed to achieve high performance with lower on-line computational burden. The effectiveness of the proposed approach is verified by simulation examples.     

互联电力系统鲁棒分布式模型预测负荷频率控制

负荷频率控制是现代互联电力系统运行的重要保障.本文针对含有不确定因素和负荷扰动的多区域互联电力系统提出了一种基于线性矩阵不等式参数可调节的鲁棒分布式预测控制算法.设计各个区域控制器目标函数引入相邻区域的状态变量和输入变量,同时考虑发电机变化速率约束和阀门位置约束,将求解一组凸优化问题转化成线性矩阵不等式求解,得到各个区域的控制律,在线性矩阵不等式中引入一组可调参数,将优化一个上限值转化成优化吸引区,降低算法的保守性.仿真结果验证了该算法在负荷扰动、系统参数不确定和结构不确定性情况下具有鲁棒性.     

Efficient robust constrained model predictive control with a time varying terminal constraint set

An efficient robust constrained model predictive control algorithm with a time varying terminal constraint set is developed for systems with model uncertainty and input constraints. The approach is novel in that it off-line constructs a continuum of terminal constraint sets and on-line achieves robust stability by using a relatively short control horizon (even N=0) with a time varying terminal constraint set. This algorithm not only dramatically reduces the on-line computation but also significantly enlarges the size of the allowable set of initial conditions. Moreover, this control scheme retains the unconstrained optimal performance in the neighborhood of the equilibrium. The controller design is illustrated through a benchmark problem.     

Nonlinear model predictive control using parameter varying BP-ARX combination model

A novel back-propagation AutoRegressive with eXternal input (BP-ARX) combination model is constructed for model predictive control (MPC) of MIMO nonlinear systems, whose steady-state relation between inputs and outputs can be obtained. The BP neural network represents the steady-state relation, and the ARX model represents the linear dynamic relation between inputs and outputs of the nonlinear systems. The BP-ARX model is a global model and is identified offline, while the parameters of the ARX model are rescaled online according to BP neural network and operating data. Sequential quadratic programming is employed to solve the quadratic objective function online, and a shift coefficient is defined to constrain the effect time of the recursive least-squares algorithm. Thus, a parameter varying nonlinear MPC (PVNMPC) algorithm that responds quickly to large changes in system set-points and shows good dynamic performance when system outputs approach set-points is proposed. Simulation results in a multivariable stirred tank and a multivariable pH neutralisation process illustrate the applicability of the proposed method and comparisons of the control effect between PVNMPC and multivariable recursive generalised predictive controller are also performed.     

A robust distributed model predictive control algorithm

Although distributed model predictive control (DMPC) has received significant attention in the literature, the robustness of DMPC with respect to model errors has not been explicitly addressed. In this paper, a novel online algorithm that deals explicitly with model errors for DMPC is proposed. The algorithm requires decomposing the entire system into N subsystems and solving N convex optimization problems to minimize an upper bound on a robust performance objective by using a time-varying state-feedback controller for each subsystem. Simulations examples were considered to illustrate the application of the proposed method.     

具有结构不确定性的时滞系统的闭环鲁棒预测控制器

针对具有结构不确定性的时滞系统,设计了闭环鲁棒预测控制算法.该控制算法基于控制不变集方法,通过采用双模控制和闭环控制策略,增加了控制设计的自由度,进而扩大了系统的初始可行域并能获得较优的控制性能.仿真结果验证了该方法的有效性.     

Stochastic model predictive control: Insights and performance comparisons for linear systems

In this paper, we define several instances of model predictive control (MPC) for linear systems, including both deterministic and stochastic formulations. We show by explicit computation of the associated control laws that, under certain conditions, different formulations lead to identical results. This paper provides insights into the performance of stochastic MPC. Amongst other things, it shows that stochastic MPC and traditional MPC can give identical results in special cases. In cases where the solutions are different, we show that the explicit formulation of the problem can give insight into the performance gap.