Self-tuning distributed measurement fusion Kalman estimator for the multi-channel ARMA signal

For the multisensor multi-channel autoregressive moving average (ARMA) signal with white measurement noises and a common disturbance measurement white noise, when the model parameters and the noise variances are all unknown, a multi-stage information fusion identification method is presented, where the consistent fused estimates of the model parameters and noise variances are obtained by the multi-dimension recursive instrumental variable (RIV) algorithm, correlation method and Gevers-Wouters algorithm with a dead band. Substituting these estimates into the optimal distributed measurement fusion Kalman signal estimator, a self-tuning distributed measurement fusion Kalman signal estimator is presented. Its convergence is proved by the dynamic error system analysis (DESA) method, so that it has asymptotical global optimality. In order to reduce computational load, a fast recursive inversion algorithm for a high-dimension matrix is presented by the inversion formula of partitioned matrix. Especially, when the process and measurement noise variance matrices are all diagonal matrices, the inversion formula of a high-dimension matrix is presented, which extends the formula of the inverse of Pei-Radman matrix. Applying the proposed inversion algorithm, the computation of the fused measurement and fused noise variance is simplified and their computational burden is reduced. A simulation example shows effectiveness of the proposed method.     

Implementation of a novel large moving range submicrometer positioner

A novel, compact and two degree-of-freedoms (DOF) submicrometer positioner with a large moving range is presented in this paper. The design of the positioner utilizes a monolithic parallel flexure mechanism with built-in electromagnetic actuators and eddy current sensors to achieve the precision 2-DOF motion. The travel range of the positioner is 1000 μm/5 mrad and which depend on the eddy current sensor’s range of measurement (ROM) and electromagnetic actuator’s effective length. The analytical model and its dynamics were analyzed and derived. The whole control architecture takes the measured configuration parameters and endeavors to control the positioner motion by regulating the currents in the electromagnetic actuators. To increase the compactness and stability of the positioner, a self-tuning adaptive (STA) controller was analyzed and proposed. From the experimental results, satisfactory performances of the system, including stiffness and precision, have been successfully demonstrated.     

基于RBF神经网络电液恒功率调速自整定PID控制

采煤机电液调速系统对保证煤矿生产具有重要的作用。在分析恒功率自动调速的电液控制系统原理的基础上,建立了基于RBF神经网络自整定的PID控制的数学模型。通过在MATLAB/GUI仿真平台上计算得到系统的实际输出和模型输出的误差不超过5%,设计的RBF神经网络的电液恒功率调速自整定PID控制系统具有更高的跟踪精度和响应特性,完全可以实现系统的高精度与快速调速控制。     

Self-tuning dynamic models of HVAC system components

A great majority of modern buildings are equipped with Energy Management and Control Systems (EMCS) which monitor and collect operating data from different components of heating ventilating and air conditioning (HVAC) systems. Models derived and tuned by using the collected data can be incorporated into the EMCS for online prediction of the system performance. To that end, HVAC component models with self-tuning parameters were developed and validated in this paper. The model parameters were tuned online by using a genetic algorithm which minimizes the error between measured and estimated performance data. The developed models included: a zone temperature model, return air enthalpy/humidity and CO₂ concentration models, a cooling and heating coil model, and a fan model. The study also includes tools for estimating the thermal and ventilation loads. The models were validated against real data gathered from an existing HVAC system. The validation results show that the component models augmented with an online parameter tuner, significantly improved the accuracy of predicted outputs. The use of such models offers several advantages such as designing better real-time control, optimization of overall system performance, and online fault detection.     

预报辨识、映照模型及自校正极点配置鲁棒控制系统

本文以反馈通道存在延时的实际系统为背景,提出了一种简单的参数预报辨识算法,分析证明了该算法的区域收敛性。此外,文中还引入了映照模型的概念,并以此为基础,设计了一种算法简单的自校正极点配置鲁棒控制器,证明了映照模型的跟踪性质和整个闭环系统的稳定性。理论分析及仿真的结果表明,本文给出的方法是可行的。     

伺服-调节组合最优自适应控制

建立起最小方差和动态最优的组合自适应控制,对随机伺服系统同时实现随机调节和伺服跟踪的最优控制,整个控制算法呈递推形式,计算量较小。算法的收敛性讨论说明其具有良好的鲁棒性。     

Self-tuning control based on generalized minimum variance criterion for auto-regressive models

Theoretical problems on self-tuning control include stability, performance and convergence of the recursive algorithm involved. In this paper, the problem of controlling minimum or non-minimum phase auto-regressive models with constant but unknown parameters is considered. The stability of an algorithm obtained by combining a recursive estimator for the controller parameters and a generalized minimum variance criterion is proved. The main result is the theorem which assures the overall stability for the closed-loop system in presence of white noise in the input-output relation, where the estimated parameters do not necessarily converge to the true values. The algorithm is proved by the Lyapunov theory.     

具有强相容参数估计的多变量自校正前馈控制器

本文提出了一种新的自校正前馈控制器.该控制器具有如下优点:1)可以控制具有任意 传输延时结构的随机多变量系统;2)可以控制开环不稳定或非最小相位系统;3)不加积分作用 可以自适应消除偏差和可测干扰对输出的影响,消除跟踪误差;4)具有全局稳定特性;5)参数 估计具有强相容性.     

A modified NARMAX model-based self-tuner with fault tolerance for unknown nonlinear stochastic hybrid systems with an input–output direct feed-through term

A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this paper for the unknown nonlinear stochastic hybrid system with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input–output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the fault detection.     

自校正DMC-PID过热汽温控制系统仿真

为了克服在过热汽温控制中对象参数变化对系统控制带来的困难,提出了自校正DMC-PID控制方法.保留了通常过热汽温控制采用的串级控制结构,能够快速有效地克服干扰.同时通过参数辨识得到非参数模型进行动态矩阵控制(DMC),即自校正DMC,无需从广义对象的阶跃模型的上下界中选取一条标称的阶跃响应曲线去实现DMC控制,实际应用起来更简单.仿真结果表明,控制方法有良好的控制效果,改善了模型失配时的鲁棒性,在过热汽温控制中是十分有效的.