Iterative Learning Controller Design for CNC Machine Tools

This paper aims at iterative learning control (ILC) design and analysis for a class of linear distributed parameter systems (DPSs) that may be hyperbolic, parabolic, or elliptic, and include many important physical processes such as diffusion, vibration, heat conduction and wave propagation as special cases. Owing to the linear characteristic of systems, the system equations are first cast into a matrix form in the Laplace transform domain. Then, through determination of a fundamental matrix, the system transfer function is precisely evaluated in a closed form. The derived transfer function clearly demonstrates the input-output relationship of system, and thus facilitates the consequent ILC design and convergence analysis in the frequency domain. The proposed control design scheme is able to deal with parametric and non-parametric uncertainties and makes full use of the process repetition, while avoids any simplification or discretization for the 3D dynamics of distributed parameter systems in the time, space, and iteration domains. In the end, an illustrative example is presented to demonstrate the efficacy of the proposed ILC scheme.

Iteration dependent interval based open‐closed‐loop iterative learning control for time varying systems with vector relative degree

Iterative learning control design for linear discrete-time systems with multiple high-order internal models

An iterative learning control algorithm within prescribed input-output subspace

In this paper, a new iterative learning algorithm is proposed for repetitive nonlinear systems. The control system employs a combination of state feedback and iterative learning control (ILC) in which the coefficients of states are learned similar to ILC methods. The control system is in a closed loop format both in iteration domain (because of ILC) and in time domain (because of feedback control) which improves the robustness of the conventional ILC. The convergence of the control algorithm is also proved. Finally, simulation results for a 5-DOF manipulator have been presented to illustrate that the proposed algorithm is more robust than a first order P type ILC method at the presence of white Gaussian noise as a nonrepeating disturbance.

A novel design strategy for iterative learning and repetitive controllers of systems with a high modal density: Theoretical background

Iterative learning control (ILC) is one of the most popular tracking control methods for systems that repeatedly execute the same task. A system model is usually used in the analysis and design of ILC. Model‐based ILC results in general in fast convergence and good performance. However, the model uncertainties and nonrepetitive disturbances hamper its practical applications. One of the commonly used solutions is the introduction of a low‐pass filter, namely, the Q‐filter. However, it is indicated in this paper that the existing Q‐filter configurations compromise the servo performance, although improving the robustness. Motivated by the combination of performance and robustness, a novel Q‐filter configuration in ILC is presented in this paper. Some practical considerations, such as the configuration of ILC in a feedback control system, the time delay compensation, and the learning coefficient, are provided in the implementation of the proposed ILC algorithm. The effectiveness and superiority of the proposed ILC versus existing Q‐filter ILC are demonstrated by both theoretical analysis and experimental verification on a wafer stage.

In this study, a two‐dimensional (2D) H ∞ ‐based method is presented for the iterative learning control (ILC) design problem of a class of linear discrete‐time systems with iteratively periodic factors, including initial states, parametric uncertainties, disturbances, measurement noises, and reference trajectories. First, the ILC design problem of the linear systems is described as a controller design problem of 2D uncertain Roesser models. Second, the H ∞ performance of 2D Roesser models is studied under a general boundary condition. Third, an ILC design criterion is presented to achieve the perfect tracking and specified H ∞ performance by using linear matrix inequality approaches. Finally, a numerical example is given to illustrate the efficiency of the proposed ILC design method.

This work focuses on the model-free iterative learning control (ILC) design with good transient performance and fast convergence rate that is employed for precision control of a wafter stage. First, the experimental setup, the mathematical model, and the desired reference are introduced. Notice that the desired reference and its internal model (IM) are known in advance. Then, based on the internal model principle (IMP), a new ILC scheme, named as 1M-based ILC, is proposed by incorporating the 1M of the reference. The proposed 1M-based ILC is model-free, that is, independent on the system model of the controlled plant. The 1M -based ILC is carefully constructed to track the reference along the time axis while tracking/rejecting repetitive variations along the iteration axis. Precisely because of the tracking capability along the time axis in every iteration, the 1M-based ILC can achieve satisfactory performance even in the 0th iteration and improve the transient performance and the convergence speed. In addition, the 2-D $H_{\infty}$ theory is used to establish the design criterion for the 1M-based ILC. Finally, in the experiments, the efficiency of the 1M-based ILC is illustrated by comparing with a P-type ILC and a model-based one.

In recent years, discriminative learning methods have widely been used in various areas of Natural Language Processing (NLP). These methods achieve the best performance, when the set of training and testing samples have the same distribution. However, in many applications of NLP, the lack of labeled datasets for some domains is a serious challenge. In such conditions, we need to develop a model based on domains with rich labeled instances and apply it to the domain with no labeled instances. In this research, a method for sentiment classification of opinions into positive and negative groups, which represent the users' feelings, is offered based on multi-source transfer learning. The proposed method here employs Spectral Feature Alignment algorithm to adapt different domains. Furthermore, according to the Majority Voting, accuracy is assigned to classifications trained on different domains based on the Majority Voting Error. Ultimately, decisions are made for each classification based on the calculated error. The Amazon datasets for four different categories, each of which contains 1000 positive and 1000 negative samples, are exploited to train the proposed model. Meanwhile, each category includes unlabeled samples that are used to select pivot features. The accuracy values of 85.5%, 86.4%, 83.5% and 90.1% obtained for Electronics, DVD, Books and Kitchen domains respectively, show the effectiveness of the proposed method compared with similar methods.

Police patrols are used ubiquitously to deter crimes in urban areas. A distinctive feature of urban crimes is that criminals react opportunistically to patrol officers assignments. Compared to strategic attackers (such as terrorists) with a well-laid out plan, opportunistic criminals are less strategic in planning attacks and more flexible in executing them. In this paper, our goal is to recommend optimal police patrolling strategy against such opportunistic criminals. We first build a game-theoretic model that captures the interaction between officers and opportunistic criminals. However, while different models of adversary behavior have been proposed, their exact form remains uncertain. Rather than simply hypothesizing a model as done in previous work, one key contribution of this paper is to learn the model from real-world criminal activity data. To that end, we represent the criminal behavior and the interaction with the patrol officers as parameters of a Dynamic Bayesian Network (DBN), enabling application of standard algorithms such as EM to learn the parameters. Our second contribution is a sequence of modifications to the DBN representation, that allows for a compact representation of the model resulting in better learning accuracy and increased speed of learning of the EM algorithm when used for the modified DBN. These modifications use marginalization approaches and exploit the structure of this problem. Finally, our third contribution is an iterative learning and planning mechanism that keeps updating the adversary model periodically. We demonstrate the efficiency of our learning algorithm by applying it to a real data set of criminal activity obtained from the police department of University of Southern California (USC) situated in Los Angeles, USA. We project a significant reduction in crime rate using our planning strategy as opposed to the actual strategy deployed by the police department. We also demonstrate the improvement in crime prevention in simulations when we use our iterative planning and learning mechanism compared to just learning once and planing. This work was done in collaboration with the police department of USC.

This paper introduces ROFALT, an open-source, model-based iterative learning control (ILC) tool for nonlinear systems, that aims at closing the gap between the theory of nonlinear ILC and successful applications. Providing a simple yet powerful syntax in MATLAB, ROFALT supports all phases of the design of a nonlinear ILC—from modeling, tuning and execution, to analysis. The theoretical basis is an optimization-based two-step approach that allows an easy trade-off between fast convergence and robustness for generic nonlinear systems. To demonstrate the efficiency of the developed tool, a simulation study on an overhead crane is performed, where a model with introduced parameter deviations is used to iteratively learn the open-loop control inputs. Special attention is paid to the comparison of different possible ways to realize the learning effect and the resulting performance. Moreover, the simplicity of considering constraints and their compliance is demonstrated, while fast convergence is observed.

Objective: To verify user satisfaction with the public oral health services in its different dimensions of care and relate them to the socioeconomic and demographic conditions. Material and Methods: Cross-sectional survey, developed with users interviewed of the public oral health services under the Primary Health Care of a Brazilian city (n=461). The associations of satisfaction with the socioeconomic and demographic conditions were analyzed using the chi-square test. Questions concerning satisfaction with the service were grouped into five domains - 'physical structure', 'relation and communication', 'information and support', 'health care' and 'organization of services' - analyzed using the Kruskal-Wallis Test . Results: Elderly individuals with low education and male, tended to be more satisfied with the services. The users reported being satisfied with the service, that were unhappy, referred to the time and the form to schedule a consultation, the time expected to be attended and the resolving capacity of oral health problems, as the causes. The overall satisfaction index was 0.8. The top rated domains were 'physical structure' (0.9) and 'relation and communication' (0.85). The 'service organization' domain received the worst evaluation (0.71) and when compared to the others domain presenting a significant difference. The 'physical structure' domain was significantly highlighted from the domains of 'oral health care' (0.78) and 'information and support' (0.78). The 'relation and communication' domain, also significantly excelled over the areas of 'oral health care'. Conclusion: The users rated the aspects of health care in a very positive way. The areas of highest user satisfaction were 'physical structure' and 'relation and communication'. However, the need for directing production processes in health became evident, especially regarding the organization of services, the domain with the worst rating.

This paper is concerned with the variable initial states problem in iterative learning control (ILC) for linear continuous systems. Firstly, the properties of the trajectory of 2-D continuous-discrete Roesser model are analyzed by using Lyapunov's method. Then, for any variable initial states which absolutely converge to the desired initial state, some sufficient conditions in the form of linear matrix inequalities (LMI) are given to ensure the convergence of the PD-type ILC rules. It implies that the ILC rules can be used to achieve the perfect tacking for variable initial states, even if the system dynamic is unknown. Finally, two numerical examples are given to illustrate the perfect tracking performance with exponentially convergent initial states.

Hydraulic press as a kind of very important press machining equipment has been widely used in national defense and civilian industry. This paper developed a new kind of hydraulic press that synthesizes the advantages of both hydraulic and SRM (Switched Reluctance Motor) driving technology. Aiming at the serious nonlinearity and time-variability existing in electro-hydraulic servo system of pump-controlled cylinder, the ILC (Iterative Learning Control) method was applied to track the shift curve. The fuzzy strategy was utilized to adjust the iterative learning gain adaptively in order to improve the convergence speed and precision of ILC. The simulation and experimental results show that the iterative learning speed is raised enormously by this method, and tracking of the slider shift curve with high response speed and control accuracy is realized.