基于Matlab的用户界面开发及其在水污染控制决策支持系统中的应用

Ｍａｔｌａｂ是一个功能强大的数值计算和仿真分析软件包,利用它所提供的工具箱可以快速、便利地开发各种仿真系统。本文针对它的特点并结合人机界面的设计理论,阐述了在Ｍａｔｌａｂ开发环境下,如何设计和开发一个仿真系统的用户界面,并且通过其在水污染控制决策支持系统中的应用加以论证。     

Matlab软件中的Fuzzy Logic工具箱在模糊控制系统仿真中的应用

该文根据作者对Ｍａｔｌａｂ仿真软件包的应用研究,阐述了如何在Ｍａｔｌａｂ软件中利用Ｓｉｍｕｌｉｎｋ平台和Ｆｕｚｚｙ Ｌｏｇｉｃ工具箱实现模糊控制系统的设计、仿真和动态性能分析,并以电站汽温过程控制为例进行了仿真研究,结果表明利用Ｍａｔｌａｂ进行模糊控制系统仿真快速简单精确,可视化强,完全能够满足工程技术人员的需要。     

基于Simulink仿真工具的感应电动机仿真模型研究

在分析感应电动机数学模型的基础之上,利用Ｍａｔｌａｂ软件提供的Ｓｉｍｕｌｉｎｋ仿真工具,构造了感应电动机的通用仿真模型。并设计了人机交互式界面,使得利用Ｍａｔｌａｂ进行交流调速诉仿真过程大大简化。该模型结构简单。可测量多,研究结果表明,能满足一般系统的要求,因而有较强的实用性。     

组件技术在离散动态系统仿真软件体系结构中的应用研究

文章针对仿真系统软件可重用性差的特点,提出了基于二进制ＣＯＭ（Ｃｏｍｐｏｎｅｎｔ Ｏｂｊｅｃｔ Ｍｏｄｅｌ）标准组件技术的离散动态系统仿真工具的设计方法。并利用组件技术开发了离散动态系统计算机仿真工具,实现了类似Ｍａｔｌａｂ拖放式创建模型,通过联线方式建立模型间逻辑联系。该仿真工具已经在某一炼钢连铸离散动态系统中得到了运用。     

海运船舶电力推进系统的鲁棒控制

针对海运船舶电力推进系统的数学模型,采用鲁棒控制方法设计状态反馈控制器。主要是基于李雅普诺夫稳定性理论,推导使系统稳定且满足 H￥性能的控制器存在的条件,利用 MATLAB软件进行仿真求解扰动抑制度γ,并获得相应的单位阶跃响应曲线,表明用鲁棒控制方法对海运船舶电力系统进行控制是可行的。     

用Matlab仿真SPWM波的设计与实现

本文通过采用Ｍａｔｌａｂ中的动态仿真工具Ｓｉｍｕｌｉｎｋ进行正弦波脉冲宽度调制（ＳＰＷＭ）的仿真,介绍了一种简单易行的ＳＰＷＭ波生成技术,并演示了ＳＰＷＭ技术在三相交流电源变频调制中的应用。     

神经模糊控制在船舶自动舵中的应用

针对常规模糊自动舵由于受船舶控制过程的非线性、时变性以及风浪干扰等因素影响,模糊控制规则和隶属函数需要校正,利用神经网络的自学习能力,用神经网络去实现模糊控制,设计自动舵神经模糊控制器,采用BP算法和最小二乘算法的混合学习算法实现对模糊规则和隶属函数的参数训练,提高控制器的自适应能力。仿真实验表明所设计的控制器有效可行,适应船舶在风浪干扰环境下的控制性能要求。     

神经模糊控制在船舶自动舵中的应用

针对常规模糊自动舵由于受船舶控制过程的非线性、时变性以及风浪干扰等因素影响,模糊控制规则和隶属函数需要校正,利用神经网络的自学习能力,用神经网络去实现模糊控制,设计自动舵神经模糊控制器,采用BP算法和最小二乘算法的混合学习算法实现对模糊规则和隶属函数的参数训练,提高控制器的自适应能力.仿真实验表明所设计的控制器有效可行.适应船舶在风浪干扰环境下的控制性能要求.     

飞机操纵系统建模及降阶仿真研究

通过对某型号飞机三轴（纵向、横向、侧向）操纵系统（机构操纵系统、增稳系统、自动加码仪系统）组成、结构及功能分析,建立了完整的数学模型。将系统分布参数分段集中化处理,在Ｍａｔｌａｂ环境下以模块化方式进行系统仿真得到了与实际系统静动态响应相吻合的仿真模型。由于飞机操纵系统模型的高阶非线性,采用“最小二乘参数拟合法”进行等效降阶处理,得到低阶带延迟环节的简化模型。这种以时域响应进行优化、频域响应进行验证     

Matlab与VC++混合编程的研究及其在图象处理中的应用

分析了用Ｍａｔｌａｂ处理的优缺点,指出了其运算速度慢的根源。讨论了Ｍａｔｌａｂ调用Ｍｅｘ文件的参数传递机制,在此基础上提出了一种运用Ｃ语言灵活调用Ｍａｔｌａｂ自带的ＡＰＩ函数来编制Ｍｅｘ文件从而实现快速图象处理的方法,并说明了该方法的具体操作步骤,最后给出了一个典型的图象处理算法的运算速度的对比实验结果及结论。     

鲁棒PID算法在船舶柴油机控制中应用

基于闭环增益成形算法给出鲁棒PID算法,并将之应用于船舶柴油机的控制中。从仿真结果可知,该算法的控制性能和鲁棒稳定性良好,算法简单、实用。     

Robust and adaptive control: fidelity or an open relationship?

Robust and adaptive control are essentially meant to solve the same control problem. Given an uncertain LTI model set with the assumption that the controlled plant slowly drifts or occasionally jumps in the allowed model set, find a controller that satisfies the given servo and disturbance rejection specifications. Specifications on the transient response to a sudden plant change or “plant jump” are easily incorporated into the robust control problem, and if a solution is found, the robust control system does indeed exhibit satisfactory transients to plant jumps. The reason to use adaptive control is its ability, when the plant does not jump, to maintain the given specifications with a lower-gain control action (or to achieve tighter specifications), and also to solve the control problem for a larger uncertainty set than a robust controller. Certainly equivalence-based adaptive controllers, however, often exhibit insufficient robustness and unsatisfactory transients to plant jumps. It is therefore suggested in this paper that adaptive control always be built on top of a robust controller in order to marry the advantages of robust and adaptive control. The concept is called adaptive robust control. It may be compared with gain scheduling, two-time scale adaptive control, intermittent adaptive control, repeated auto-tuning, or switched adaptive control, with the important difference that the control is switched between robust controllers that are based on plant uncertainty sets that take into account not only the currently estimated plant model set but also the possible jumps and drifts that may occur until the earliest next time the controller can be updated.     

Relations between uncertainty structures in identification for robust control

Various techniques of system identification exist that provide a nominal model and an uncertainty bound. An important question is what the implications are for the particular choice of the structure in which the uncertainty is described when dealing with robust stability/performance analysis of a given controller and when dealing with robust synthesis. It is shown that an amplitude-bounded (circular) uncertainty set can equivalently be described in terms of an additive, Youla parameter and ν-gap uncertainty. As a result, the choice of structure does not matter provided that the identification methods deliver optimal uncertainty sets rather than an uncertainty bound around a prefixed nominal model. Frequency-dependent closed-loop performance functions based on the uncertainty sets are again bounded by circles in the frequency domain, allowing for analytical expressions for worst-case performance and for the evaluation of the consequences of uncertainty for robust design. The results can be used to tune optimal experimental conditions in view of robust control design and in the further development of experiment-based robust control design methods.     

Controller tuning freedom under plant identification uncertainty: double Youla beats gap in robust stability

In iterative schemes of identification and control one of the particular and important choices to make is the choice for a model uncertainty structure, capturing the uncertainty concerning the estimated plant model. Structures that are used in the recent literature encompass e.g. gap metric uncertainty, coprime factor uncertainty, and the Vinnicombe gap metric uncertainty. In this paper, we study the effect of these choices by comparing the sets of controllers that guarantee robust stability for the different model uncertainty bounds. In general these controller sets intersect. However in particular cases the controller sets are embedded, leading to uncertainty structures that are favourable over others. In particular, when restricting the controller set to be constructed as metric-bounded perturbations around the present controller, the so-called double Youla parametrization provides a set of robustly stabilizing controllers that is larger than corresponding sets that are achieved by using any of the other uncertainty structures. This is particularly of interest in controller tuning problems.     

On the computation of the gap metric for LTV systems

In this paper, we consider the robust stabilization problem for linear discrete time-varying (LTV) systems using the gap metric. In particular, we show that the time-varying (TV) directed gap reduces to an operator with a TV Hankel plus Toeplitz structure. Computation of the norm of such an operator can be carried out using an iterative scheme involving a TV Hankel operator defined on a space of Hilbert–Schmidt causal operators. The “infimization” in the TV directed gap formula is shown to be, in fact, a minimum by using duality theory. The latter holds as well in the time-invariant case.     

MPC for stable linear systems with model uncertainty

In this paper, we developed a model predictive controller, which is robust to model uncertainty. Systems with stable dynamics are treated. The paper is mainly focused on the output-tracking problem of a system with unknown steady state. The controller is based on a state-space model in which the output is represented as a continuous function of time. Taking advantage of this particular model form, the cost functions is defined in terms of the integral of the output error along an infinite prediction horizon. The model states are assumed perfectly known at each sampling instant (state feedback). The controller is robust for two classes of model uncertainty: the multi-model plant and polytopic input matrix. Simulations examples demonstrate that the approach can be useful for practical application.     

Performance improvement of robust controllers for polynomially uncertain systems

This paper is concerned with the high-performance robust control of discrete-time linear time-invariant (LTI) systems with semi-algebraic uncertainty regions. It is assumed that a robustly stabilizing static controller is given whose gain depends polynomially on the uncertain variables. The problem of tuning this parameter-dependent gain with respect to a prescribed quadratic cost function is formulated as a sum-of-squares (SOS) optimization. This method leads to a near-optimal controller whose performance is better than that of the initial controller. It is shown that the results derived in the present work encompass the ones obtained in a recent paper. The efficacy of the results is elucidated by an example.     

Optimization over state feedback policies for robust control with constraints

This paper is concerned with the optimal control of linear discrete-time systems subject to unknown but bounded state disturbances and mixed polytopic constraints on the state and input. It is shown that the class of admissible affine state feedback control policies with knowledge of prior states is equivalent to the class of admissible feedback policies that are affine functions of the past disturbance sequence. This implies that a broad class of constrained finite horizon robust and optimal control problems, where the optimization is over affine state feedback policies, can be solved in a computationally efficient fashion using convex optimization methods. This equivalence result is used to design a robust receding horizon control (RHC) state feedback policy such that the closed-loop system is input-to-state stable (ISS) and the constraints are satisfied for all time and all allowable disturbance sequences. The cost to be minimized in the associated finite horizon optimal control problem is quadratic in the disturbance-free state and input sequences. The value of the receding horizon control law can be calculated at each sample instant using a single, tractable and convex quadratic program (QP) if the disturbance set is polytopic, or a tractable second-order cone program (SOCP) if the disturbance set is given by a 2-norm bound.     

一种变形的回路成形控制器及其应用

给出一种变形的回路成形控制器,并从理论上分析了将常规的回路成形控制器变形后能够改善系统动态性能所需的条件。将其应用船舶自动舵的设计,获得了满意的控制结果。     

SISO系统的鲁棒性能设计

本文讨论了ＳＩＳＯ系统的鲁棒性能设计问题。根据具有乘性不确定性ＳＩＳＯ系统鲁棒性能指标μ的显式表达式,分析了μ综合中权函数的选择,文中还配有例题进行说明。