基于20-SIM软件的控制系统建模与仿真

简要地介绍了由荷兰Twente大学控制工程系研发的商业软件20-SIM的主要功能、编程环境和仿真方法.在讨论控制系统涉及领域广的基础上,提出利用20-SIM软件进行建模与仿真.在实例模型中,首先利用20-SIM建立了控制系统图标化模型.在此基础上,应用了自动控制技术中的最优状态反馈控制和卡尔曼滤波器技术,仿真结果理想地显示了控制效果.由此可见,基于20-SIM建模与仿真方法直观简便,为控制系统的建模与仿真提供了新的思路.     

模型参考自适应控制的20-sim软件实现

简要地介绍了李雅普诺夫稳定意义上模型参考自适应控制（MRAC）的基本原理,给出了设计MRAC控制系统的基本步骤。基于荷兰Twente大学控制工程系研发的机电一体化设计软件20-sim,以Twente大学先进控制演示仪为研究对象,在20-sim下建立了系统的图标化数学模型,编制了模型参考自适应和Lyapunov求解模块。在此基础上,构成了完整的MRAC控制系统。仿真结果显示基于20-sim建模与仿真方法直观简便,所设计的模块可移植性强,为控制系统的建模与仿真提供了新的思路。     

基于键合图法的活塞泵建模与仿真

基于由荷兰Twente大学控制工程系研发的机电一体化建模软件20-sim,提出利用键合图法实现活塞泵的建模与仿真。在实例模型中,首先实现了单向阀的20-sim模块构造,继而在20-sim环境中建立了活塞泵的键合图模型,仿真结果理想地显示了概念设计阶段的控制效果。     

20-sim与Matlab交互实现倒立摆系统的稳定性分析

本文简要介绍了20-sim软件的主要功能、编程环境和仿真方法。在讨论倒立摆系统是自动控制理论专业的理想实验对象的基础上,介绍了一阶倒立摆系统的构成和数学模型,提出利用20-sim软件对倒立摆系统的稳定性进行判断,在此基础上,应用自动控制技术中的最优状态反馈控制,利用SIDOPS＋仿真语言将20-sim与Matlab相结合进行仿真,理想地显示了控制效果。     

基于系统辨识的模型参考自适应控制

介绍了荷兰Twente大学控制工程系研发的先进控制实验平台的基本组成、功能和基于机电一体化设计软件20-sim。以先进控制演示仪为研究对象,在20-sim环境下建立了系统的图标化数学模型,并从理论上对模型作了降阶分析。根据系统辨识的模型参考自适应控制（MRAC）原理,编制了模型参考自适应和Lyapunov求解模块。采用线性二次型调节器为控制模块,构成了完整的先进控制演示仪的MRAC控制系统。该系统仿真结果显示所建立的模型针对性强,可用于各种先进控制策略的仿真研究。     

基于卡尔曼滤波的自主式水下航行器大尺度编队控制

针对网络环境下环境噪声对自主式水下航行器编队控制的影响,提出一种利用卡尔曼滤波实时估计AUV最优运动状态的编队控制方法.将空间间隔较远的多AUV系统建模为多智能体系统,从大尺度上研究其编队控制问题.为了得到每个AUV速度状态的最优估计值,每个AUV都嵌入一个全局卡尔曼滤波器,利用该全局滤波器进行最优估计从而计算出噪声环境下其自身的最优位置.仿真结果验证了所给出的控制策略的有效性.     

改进粒子群算法优化扩展卡尔曼滤波器电机转速估计

针对感应电机扩展卡尔曼滤波器转速估计中难以取得卡尔曼滤波器系统噪声矩阵和测量噪声矩阵最优值的问题, 提出了一种基于改进粒子群算法优化的扩展卡尔曼滤波器转速估计方法。算法通过融合遗传算法和粒子群算法的优点, 采用可调整的算法模型对粒子群算法进行改进, 将改进的粒子群算法对扩展卡尔曼滤波器中的系统噪声矩阵和测量噪声矩阵进行优化处理, 将优化后的卡尔曼滤波器应用于感应电机转速估计。仿真实验表明, 与试探法、标准粒子群算法及遗传算法比较, 改进粒子群算法优化的扩展卡尔曼滤波器能够有效提高转速估计的精度, 从而提高无速度传感器矢量控制系统的控制性能。     

基于卡尔曼滤波CMAC-PID的视力检查距离控制系统

为提高视力检查的精确性和灵活性,设计了视力检查距离控制系统对检查距离进行有效控制,并建立了数学模型。针对该系统的非线性、时变性和多干扰性,提出基于卡尔曼滤波器的小脑神经网络与比例—积分—微分复合控制(CMAC-PID)方法,利用卡尔曼滤波器抑制测量噪声和控制干扰的影响。仿真结果表明,此控制方法在抗干扰方面优于CMAC-PID控制,可以更好地改善距离控制系统的性能。     

基于模糊卡尔曼滤波器的网络控制系统状态估计

针对基于T S模糊模型的网络控制系统提出了一种卡尔曼滤波器的设计方法．先运用卡尔曼滤波理论给每个子系统设计出子滤波器，然后通过这些子滤波器的模糊融合得出全局滤波器．本文证明此全局滤波器可实现无偏状态估计，并给出了其稳定的条件．最后用仿真实例验证了所提出的卡尔曼滤波器的有效性．     

基于FPGA的卡尔曼滤波器的设计与实现

采用 FPGA 硬件实现卡尔曼滤波器,解决了采用 DSP 软件方法实现存在的并行性和速度问题。以基于 FPGA 的数据采集系统为硬件平台,根据模块化设计思想,采用 VHDL 编程实现ADS8364芯片控制模块,利用 FPGA 的系统级设计工具 DSP Builder 设计卡尔曼滤波器模块,给出模块的软件仿真结果并完成整个系统的硬件验证。结果证明了设计的正确性,同时表明采用 DSP Builder使卡尔曼滤波器的 FPGA 硬件实现更加简单,速度更快。     

Stability robustness bounds for discrete-time linear regulators

Stability robustness analysis and design for linear multivariable discrete-time systems with bounded uncertainties are discussed. Robust stability of the full-state feedback linear quadratic (LQ) regulator in the presence of perturbations (modelling errors) of the system matrices is investigated. These results are based on a recently developed bound on elemental (structured) time-varying perturbations of an asymptotically stable linear time-invariant discrete-time system. Lyapunov theory and singular value decomposition techniques are employed in deriving these bounds. Extensions of these results to linear stochastic systems with the Kalman filter as the stale estimator (LQG regulators) and to reduced-order dynamic compensator feedback are described. A state feedback control design method is presented for LQ regulators, using a quantitative measure called the Stability Robustness Index. Simple examples illustrate these new results.     

Optimal signal tracking algorithm for GNSS signal using moving set-point LQG system

This paper proposes moving set-point state feedback for a LQG control system. In contrast to the independent code and carrier tracking loop of a conventional receiver algorithm, the code and carrier tracking variables are correlated in the LQG system. Not only are the multiple states correlated with each other, but also the control inputs are formulated from a combination of the states and the optimal LQG controller gain. In addition, this paper develops a moving set point to estimate the GNSS signal more accurately. To analyze the advantage of the proposed method, a signal tracking simulation using hardware GNSS simulator is performed and the simulation results show that the tracking performance of the moving set-point LQG system is better than that of a conventional loop filter, especially in terms of the code tracking performance.     

基于卡尔曼滤波的液压主动悬架控制研究

汽车的液压主动悬架系统在控制过程中不可避免的受到噪声的影响。应用卡尔曼滤波对系统的状态向量做最优估计,并应用到系统的全状态反馈控制中,可以有效的提高系统的鲁棒性。文章给出了卡尔曼滤波的应用条件,对单一LQR调节器系统和基于LQG理论的调节器系统做了仿真对比。在LQR调节器系统中前置卡尔曼滤波,能得到更好的振动控制效果。     

A feedback control synthesis for the Rayleigh-Bénard convection by means of mode reduction

A state feedback controller is derived to suppress the Rayleigh-Bénard convection in a finite domain by adjusting heat flux profile at the bottom of the system under the constraint of constant heat input. The Karhunen-Loe've Galerkin procedure is employed to convert the Boussinesq equation to a reduced order model, from which an extended Kalman filter and the state optimal feedback controller are constructed. The number of measurement locations employed in the Kalman filter determines the number of reliable eigenmodes to be adopted in the feedback controller. With the feedback controller constructed by employing only the reliable dominant eigenmodes, it is found that there exists a threshold Rayleigh number beyond which the suppressed state becomes unstable to evolve to a new stationary convection state. The linear stability analysis is used to investigate the effect of a number of eigenmodes employed in the feedback controller on the threshold Rayleigh number.     

Constrained output feedback model predictive control for nonlinear systems

A constrained output feedback model predictive control approach for nonlinear systems is presented in this paper. The state variables are observed using an unscented Kalman filter, which offers some advantages over an extended Kalman filter. A nonlinear dynamic model of the system, considered in this investigation, is developed considering all possible effective elements. The model is then adaptively linearized along the prediction horizon using a state-dependent state space representation. In order to improve the performance of the control system as many linearized models as the number of prediction horizons are obtained at each sample time. The optimum results of the previous sample time are utilized for linearization at the current sample time. Subsequently, a linear quadratic objective function with constraints is formulated using the developed governing equations of the plant. The performance and effectiveness of the proposed control approach is validated both in simulation and through real-time experimentation using a constrained highly nonlinear aerodynamic test rig, a twin rotor MIMO system (TRMS).     

Certain nonlinear partially observable stochastic optimal controlproblems with explicit control laws equivalent to LEQG/LQG problems

This paper is concerned with partially observed stochastic optimal control problems when nonlinearities enter the dynamics of the unobservable state and the observations as gradients of potential functions. Explicit representations for the information state are derived in terms of a finite number of sufficient statistics. Consequently, the partially observed problem is recast as one of complete information with a new state generated by a modified version of the Kalman filter. When the terminal cost is quadratic in the unobservable state and includes the integral of the nonlinearities, the optimal control laws are explicitly computed, similar to linear-exponential-quadratic-Gaussian (LEQG) and linear-quadratic-Gaussian (LQG) tracking problems. The results are applicable to filtering and control of Hamiltonian systems     

Optimal control strategy by tracking and compensation for ICPS under the DoS attack

To solve the security problem of industrial cyber-physical systems (ICPS) suffering from the Denial of Service (DoS) attack, this paper proposes an optimal control strategy with functions of tracking and compensation control. The system model, being depicted as a discrete linear time-invariant system combined with an improved Bernoulli model, is established to describe the system under DoS attacks by using the feedback control theory. The optimal control strategy is completed by designing linear quadratic Gaussian (LQG) controller consisting of a tracking regulator and a Kalman filter to achieve tracking control of ICPS whose equilibrium state is neither zero state nor zero output, where a periodic event-triggered mechanism is adopted for ensuring the system's stability. The compensation control strategy is completed by designing a compensator to compensate the system to a certain extent through the multistep state prediction compensation strategy after a DoS attack being detected, where the ${\chi }^2$ detector is adopted to detect the DoS attack based on the measurement residual obtained by the Kalman filter. The simulation test is conducted by using Simulink/True Time tool with a ball-beam system as the physical object, whose results show that the tracking regulator can achieve the tracking control of ICPS; compensation strategy can reduce the impact of the attack on the system to a certain extent.     

基于微型编码器的电机LQG控制器设计方法

针对航空航天领域对电机伺服控制系统高精度和小体积的需求,提出并实现了一种基于微型编码器的电机伺服跟踪系统线性二次高斯控制器设计方法.分析了实际电机系统结构和编码器的典型误差并建立系统的噪声状态方程;设计了平稳卡尔曼滤波器,可以实现角位置、角速率和角加速度的最优估计;根据总体指标完成了线性二次高斯控制器设计并在Matlab/Simulink环境下建立了仿真模型并对其控制效果进行了预测;模拟实际工作环境搭建了实验测试系统对其跟踪性能进行了实测评估.仿真和实验结果表明:结合卡尔曼滤波的电机线性二次高斯控制器不但具有良好的跟踪性能,带宽可达24Hz,还能够有效地抑制噪声影响.