Iterative Learning Control for uncertain systems: Robust monotonic convergence analysis

In this paper, we present a novel Robust Monotonic Convergence (RMC) analysis approach for finite time interval Iterative Learning Control (ILC) for uncertain systems. For that purpose, a finite time interval model for uncertain systems is introduced. This model is subsequently used in an RMC analysis based on μ analysis. As a result, we can handle additive and multiplicative uncertainty models in the RMC problem formulation, analyze RMC of linear time invariant MIMO systems controlled by any linear trial invariant ILC controller, and formulate additional straightforward RMC conditions for ILC controlled systems. To illustrate the derived results, we analyze the RMC properties of linear quadratic (LQ) norm optimal ILC.     

Stochastic closed-loop model predictive control of continuous nonlinear chemical processes

A new predictive control framework for chemical processes is presented, that has a number of fundamental differences to classical MPC. Both future disturbances and future process measurements are explicitly introduced in the model prediction, while back-off prevents violation of the inequality constraints. A feedforward trajectory, used for constraint pushing, is optimized simultaneously with a linear time-varying feedback controller, used to minimize the back-off. No feedback is generated by the receding horizon implementation itself. Via several transformations, the resulting optimization problem is rendered convex. For nonlinear processes, this applies to the sub-problem in a sequential conic optimization approach. A two stage LQG approach reduces the complexity even further for large scale systems. The method is illustrated on a HDPE reactor example and compared to a LTV-MPC.     

Internal-model-based design of repetitive and iterative learning controllers for linear multivariable systems

Repetitive and iterative learning control are two modern control strategies for tracking systems in which the signals are periodic in nature. This paper discusses repetitive and iterative learning control from an internal model principle point of view. This allows the formulation of existence conditions for multivariable implementations of repetitive and learning control. It is shown that repetitive control can be realized by an implementation of a robust servomechanism controller that uses the appropriate internal model for periodic distrubances. The design of such controllers is discussed. Next it is shown that iterative learning control can be implemented in the format of a disturbance observer/compensator. It is shown that the resulting control structure is dual to the repetitive controller, and that both constitute an implementation of the internal model principle. Consequently, the analysis and design of repetitive and iterative learning control can be generalized to the powerful analysis and design procedure of the internal model framework, allowing to trade-off the convergence speed for periodic-disturbance cancellation versus other control objectives, such as stochastic disturbance suppression.     

Development of an ELISA technique for serodiagnosis of bovine paratuberculosis

In Japan, ortho-phenylphenol (OPP), biphenyl (BP), and thiabendazole (2-(4'-thiazolyl)benzimidazole, TBZ) are commonly used as a postharvest treatment to preserve imported citrus fruits during transport and storage. We used a modification of the alkaline single cell gel electrophoresis (SCG) (Comet) assay to test the in vivo genotoxicity of those agents in mouse stomach, liver, kidney, bladder, lung, brain, and bone marrow. CD-1 male mice were sacrificed 3, 8, and 24 h after oral administration of the test compounds. OPP (2000 mg/kg) induced DNA damage in the stomach, liver, kidney, bladder, and lung, BP (2000 mg/kg) and TBZ (200 mg/kg) induced DNA damage in all the organs studied. For OPP, increased DNA damage peaked at 3-8 h and tended to decrease at 24 h. For BP, on the contrary, increased DNA migration peaked at 24 h. That delay may have been due to the fact that OPP is metabolized by cytochrome 450 and prostaglandin H synthase to phenylbenzoquinone (PBQ), a DNA binding metabolite, and BP is metabolized to PBQ via OPP and m-phenylphenol. The positive response to TBZ, an aneugen, supports the in vivo DNA-damaging action of TBZ.     

Fundamental approach to the selection of implementation parameters in the hybrid serial solution of partial differential equations

In this paper a comprehensive theoretical basis is formulated concerning the hybrid computer approximation of partial differential operators. The investigation leads to a theorem that enables the identification of consistency and error properties of general hybrid methods. The theorem is applied to several well-known hybrid methods and the results presented can be directly used as a guide for selecting the optimal implementation.     

A method for noise-robust context-aware pattern discovery and recognition from categorical sequences

An efficient method for weakly supervised pattern discovery and recognition from discrete categorical sequences is introduced. The method utilizes two parallel sources of data: categorical sequences carrying some temporal or spatial information and a set of labeled, but not exactly aligned, contextual events related to the sequences. From these inputs the method builds associative models able to describe systematically co-occurring structures in the input streams. The learned models, based on transitional probabilities of events observed at several different time lags, inherently segment and classify novel sequences into contextual categories. Learning and recognition processes are purely incremental and computationally cheap, making the approach suitable for on-line learning tasks. The capabilities of the algorithm are demonstrated in a keyword learning task from continuous infant-directed speech and a continuous speech recognition task operating at varying noise levels.     

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.     

Estimating the hydrolytic durability of cured wood adhesives by measuring formaldehyde liberation and structural stability

The durability of different commonly used thermosetting wood adhesives (UF, MUF, MUPF) was determined from the hydrolytically induced mass loss and the amount of liberated formaldehyde. Both methods showed high reproducibility. Different methodical aspects were investigated for further improvement. The hydrolytically induced mass loss and formaldehyde liberation depend on the particle size of the cured resins. Additionally, more severe hydrolytic conditions (time, pH, temperature) caused elevated mass losses and formaldehyde liberations. The hydrolytic stability of the cured resins could be well determined by gravimetric analysis. However, a distinct graduation of the adhesive durability according to formaldehyde liberation appeared to be more difficult. These findings were reinforced by correlating the hydrolytic indicators and wet strength properties of particle boards according to different test methods (V70, V100, EN 321, Werzalit cycle test).     

Synthesis of robust multivariable iterative learning controllers with application to a wafer stage motion system

Iterative Learning Control (ILC) is a powerful control concept that iteratively improves the transient behaviour of processes that are repetitive in nature. Although most of the published ILC schemes are heuristic in nature, some initial research has been performed on the formulation of the ILC problem in the H