Adaptive predictive control algorithm based on Laguerre Functional Model

Laguerre Functional Model has many advantages such as good approximation capability for the variances of system time‐delay, order and other structural parameters, low computational complexity, and the facility of online parameter identification, etc., so this model is suitable for complex industrial process control. A series of successful applications have been gained in linear and non‐linear predictive control fields by the control algorithm based on Laguerre Functional Model, however, former researchers have not systemically brought forward the theoretical analyses of the stability, robustness, and steady‐state performance of this algorithm, which are the keys to guarantee the feasibility of the control algorithm fundamentally. Aimed at this problem, we introduce the principles of the Incremental Mode Linear Laguerre Predictive Control (IMLLPC) algorithm, and then systemically propose the theoretical analyses and proofs of the stability and robustness of the algorithm, in addition, we also put forward the steady‐state performance analysis. At last, the control performances of this algorithm on two different physical industrial plants are presented in detail, and a number of experimental results validate the feasibility and superiority of IMLLPC algorithm. Copyright © 2005 John Wiley & Sons, Ltd.     

Ecophysiology of ochratoxigenic Aspergillus ochraceus and Penicillium verrucosum isolates. Predictive models for fungal spoilage prevention - a review

Ochratoxin A (OTA) is a secondary metabolite produced by several species of Aspergillus and Penicillium; among them Aspergillus ochraceus and Penicillium verrucosum are two ochratoxigenic species capable of growing in different climates and thus contamination of food crops with OTA can occur worldwide. OTA can be found in a wide range of foods such as cereals, coffee, cocoa, spices, beer, wine, dried vine fruit, grapes and meat products. OTA is toxic to animals, it presents neurotoxic, immunotoxic and nephrotoxic effects. It has been implicated in a human kidney disorder known as Balkan Endemic Nephropathy. This review focuses on the ecophysiology of ochratoxin-producing Aspergillus ochraceus and Penicillium verrucosum, the effect of environmental factors on their germination, mycelial growth, and OTA production. Knowledge of environmental conditions required for sucessive stages of fungal development represent the first step towards preventing mycotoxin formation. Predictive models for different stages of fungal development are presented, which allow prediction of the time before spoilage as a function of the abiotic factors. Finally, the implications of these studies in management of barley, coffee and grapes are described. This can help to identify the critical control points in their production, storage and distribution processes.     

Analysis and tuning of adaptive generalized predictive control

The adaptive Generalized Predictive Controller (Clarke et al., 1987a, b) is capable of controlling plants with variable dead-time, unknown model orders and unstable poles and zeros. This paper shows how the GPC control law can be written in an equivalent general linear transfer function form which simplifies closed-loop (eg. root locus) analysis. Three recommended strategies for selecting the design parameters during the commissioning stage allow the user to adjust the closed-loop speed of response on-line using only a single active tuning parameter. Experimental runs confirm the ability of adaptive GPC to provide a consistent closed-loop response in spite of large process changes.     

工业酚水稀释的RBF神经网络预测控制

针对酚水稀释过程中酚水流量不便测量的工程情况,提出了一种基于RBF（径向基）神经网络的预测控制方法.在酚水罐每个液位变化周期内使用计算机定时测量液位高度,并将这些高度值作为目标输出,在每个循环结束时训练RBF网络,利用RBF网络的逼近能力来实时预测酚水流量,从而控制清水的配比,实现酚水质量浓度的稳定.该方法可避免测量误差带来的干扰,提高控制精度.     

注塑机注射速度的模型预测迭代学习控制

针对注射过程具有重复运行和非线性的特性,在对预测控制与迭代学习控制进行综合应用并加以改进的基础上,给出一种模型预测迭代学习复合控制新算法,研究了控制器的设计方案.同时,将迭代学习思想引入到预测步长的在线调整,提出了预测步长的迭代学习方法.仿真结果表明,该方法是有效的,其控制性能优于PID迭代学习控制系统.     

不确定系统的稳定广义预测控制

针对一类有界不确定线性离散被控对象,采用Min-Max优化方法,提出一种新的稳定广义预测控制(MMSGPC)算法.引入内模控制结构,将干扰和不确定性从被控对象中分离出来,并利用局部反环节对其进行补偿;采用Min-Max优化方法,将终端约束条件转化为有界不确定性最差情况时应的线性方程;通过引入矩阵的Moore-Penrose逆,得到了终端约束线性方程的通解,并结合性能指标函数求得了最优控制律.通过仿真实例验证了该方法的稳定效果.     

Output feedback robust MPC for LPV system with polytopic model parametric uncertainty and bounded disturbance

The output feedback robust model predictive control (MPC), for the linear parameter varying (LPV) system with norm-bounded disturbance, is addressed, where the model parametric matrices are only known to be bounded within a polytope. The previous techniques of norm-bounding technique, quadratic boundedness (QB), dynamic output feedback, and ellipsoid (true-state bound; TSB) refreshment formula for guaranteeing recursive feasibility, are fused into the newly proposed approaches. In the notion of QB, the full Lyapunov matrix is applied for the first time in this context. The single-step dynamic output feedback robust MPC, where the infinite-horizon control moves are parameterised as a dynamic output feedback law, is the main topic of this paper, while the multi-step method is also suggested. In order to strictly guarantee the physical constraints, the outer bound of the true state replaces the true state itself, so tightness of this bound has a major effect on the control performance. In order to tighten the TSB, a procedure for refreshing the real-time ellipsoid based on that of the last sampling instant is given. This paper is conclusive for the past results and far-reaching for the future researches. Two benchmark examples are given to show the effectiveness of the novel results.     

Iterative ultrasonic signal and image deconvolution for estimation of the complex medium response

The ill‐conditioned inverse problem of estimating ultrasonic medium responses by deconvolution of RF signals is investigated. The primary difference between the proposed method and others is that the medium response function is assumed to be complex‐valued rather than restricted to being real‐valued. Derived from the complex medium model, complex Wiener filtering is presented, and a Hilbert transform related limitation to inverse filtering type methods is discussed. We introduce a nonparametric iterative algorithm, the least squares method with point count regularization (LSPC). The algorithm is successfully applied to simulated and experimental data and demonstrates the capability of recovering both the real and imaginary parts of the medium response. The simulation results indicate that the LSPC method can outperform Wiener filters and improve the resolution of the ultrasound system by factors as high as 3.7. Experimental results using a single element transducer and a conventional medical ultrasound system with a linear array transducer show that despite the errors in pulse estimation and the noise in the RF signals, excellent results can be obtained, demonstrating the stability and robustness of the algorithm. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 266–277, 2005