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The iterative learning control (ILC) is attractive for its simple structure, easy implementation. So the ILC is applied to various fields. But the unexpected huge overshoot can be observed as iteration repeat when we use the ILC to the real world applications. Such bad transient becomes an obstacle for using the ILC in the real field. Designers use a projection method to avoid the bad transient usually. However, the projection method does not show a good error performance enough. Therefore we propose a new learning rule to reduce such a bad transient effectively. The simple normalized learning rules for P-type and PD-type are presented and we prove their convergence. Numerical examples are given to show the effectiveness of the proposed learning control algorithms. [ABSTRACT FROM AUTHOR]

Ａｉｍｉｎｇ ａｔ ｔｈｅ ｐａｒａｍｅｔｅｒ ｉｄｅｎｔｉｆｉｃａｔｉｏｎ ｏｆ ｔｈｅ ｐｅｒｍａｎｅｎｔ ｍａｇｎｅｔ ｌｉｎｅａｒ ｓｙｎｃｈｒｏｎｏｕｓ ｍｏｔｏｒ(ＰＭＳＬＭ), ｔｈｅ ｍｏｄｅｌ ｐａｒａｍｅｔｅｒｓ ｏｆ ｓｅｒｖｏ ｓｙｓｔｅｍ ｃａｎ ｂｅ ｓｕｃｃｅｓｓｆｕｌｌｙ ｉｄｅｎｔｉｆｉｅｄ ｂｙ ｔｈｅ ｂｌｏｃｋ ｌｅａｓｔ ｓｑｕａｒｅｓ ａｌｇｏｒｉｔｈｍｏｎ ｔｈｅ ｂａｓｉｓ ｏｆ ｔｈｅ ｓｔｕｄｙ ｏｆ ｔｈｅ ｌｉｎｅａｒ ｍｏｔｏｒ'ｓｍａｔｈｅｍａｔｉｃａｌ ｍｏｄｅｌ ａｎｄ ｔｈｅ ｌｅａｓｔ ｓｑｕａｒｅ ｉｄｅｎｔｉｆｉｃａｔｉｏｎ ａｌｇｏｒｉｔｈｍ. Ｈｏｗｅｖｅｒ, ｉｔｓ ｍｏｄｅｌ ｉｓ ｎｏｔ ａｃｃｕｒａｔｅ ｅｎｏｕｇｈ, ｅｓｐｅｃｉａｌｌｙ ｆｏｒ ｔｈｅ ｏｓｃｉｌｌａｔｉｎｇ ｅｌｅｍｅｎｔ ｉｄｅｎｔｉｆｉｃａｔｉｏｎ ｉｎ ｔｈｅ ｓｅｒｖｏ ｓｙｓｔｅｍ. Ａｃｃｏｒｄｉｎｇ ｔｏ ｔｈｅ ｓｈｏｒｔｃｏｍｉｎｇｓ ｏｆ ｔｈｅ ｔｒａｄｉｔｉｏｎａｌ ｉｄｅｎｔｉｆｉｃａｔｉｏｎ ｍｏｄｅｌ, ａ ｆｉｌｔｅｒ-ｂａｓｅｄ ｉｔｅｒａｔｉｖｅ ｌｅａｒｎｉｎｇ ｌｅａｓｔ ｓｑｕａｒｅ ｉｄｅｎｔｉｆｉｃａｔｉｏｎ ｍｅｔｈｏｄ ｗａｓ ｐｒｏｐｏｓｅｄ. Ｔｈｅ ｎｏｉｓｅ ｉｎｔｅｒｆｅｒｅｎｃｅ ｗａｓ ｒｅｄｕｃｅｄ ｂｙ ｔｈｅ ｆｉｌｔｅｒ ｉｎ ｔｈｅ ｉｄｅｎｔｉｆｉｃａｔｉｏｎ ｐｒｏｃｅｓｓ, ａｎｄｎｏｎｌｉｎｅａｒ ｐｒｏｂｌｅｍ ｏｆ ｔｈｅ ｉｄｅｎｔｉｆｉｃａｔｉｏｎ ｍｏｄｅｌ ｗａｓ ｓｏｌｖｅｄ ｂｙ ｉｔｅｒａｔｉｎｇ ｉｎ ｔｈｉｓ ａｌｇｏｒｉｔｈｍ. Ｔｈｅ ｓｉｍｕｌａｔｉｏｎ ａｎｄ ｅｘｐｅｒｉｍｅｎｔｒｅｓｕｌｔｓ ｉｎｄｉｃａｔｅ ｔｈａｔ ｔｈｅ ｆｉｌｔｅｒ-ｂａｓｅｄ ｉｔｅｒａｔｉｖｅ ｌｅａｒｎｉｎｇ ｌｅａｓｔ ｓｑｕａｒｅ ｉｄｅｎｔｉｆｉｃａｔｉｏｎ ｍｅｔｈｏｄ ｃａｎ ｅｆｆｅｃｔｉｖｅｌｙ ｉｍｐｒｏｖｅ ｔｈｅ ａｃｃｕｒａｃｙ ｏｆ ｉｄｅｎｔｉｆｉｃａｔｉｏｎｃｏｍｐａｒｅｄ ｗｉｔｈ ｔｈｅ ｂｌｏｃｋ ｌｅａｓｔ ｓｑｕａｒｅｓ ａｌｇｏｒｉｔｈｍ, ａｎｄ ｔｈｅ ｉｄｅｎｔｉｆｉｃａｔｉｏｎ ｒｅｓｕｌｔｓ ｏｆ ｔｈｅ ｏｓｃｉｌｌａｔｉｎｇ ｅｌｅｍｅｎｔ ａｒｅ ｍｏｒｅ ｃｏｎｓｉｓｔｅｎｔ ｗｉｔｈ ｔｈｏｓｅ ｏｂｔａｉｎｅｄ ｂｙ ｐｏｗｅｒ ｓｐｅｃｔｒｕｍ ａｎａｌｙｓｉｓ. [ABSTRACT FROM AUTHOR]

The use of the haptic channel in multimodal interfaces holds several advantages for communication, one of them being that it allows decreasing the load of the visual and auditory channels. Tactons are abstract messages that can be used to communicate non-visually. In this paper we describe a study in which we tested if a set of four tactons can be used to convey a risk level (four states) through an enactive shoe. To this end, two experiments have been run. In the first experiment with 14 participants, we used a multidimensional scale analysis to identify the six most different tactons from an initial set of 30 tactons. In the second experiment (with 38 participants), we evaluated participants’ ability to recognize four preselected tactons among these six. For each trial, participants had to perform 12 identifications (three times for each tacton) until they reached a score greater than 95%. The number of trials required and the completion time are analyzed. We found that the repetition significantly improves the recognition rate of tactons but does not speed up the completion time. [ABSTRACT FROM AUTHOR]

Deep learning (DL) models achieved a lot of success due to the availability of labeled training data. In contrast, labeling a huge amount of data by a human is a time-consuming and expensive solution. Active Learning (AL) efficiently addresses the issue of labeled data collection at a low cost by picking the most useful samples from a large number of unlabeled datasets. However, current AL techniques largely depend on regular human involvement to annotate the most uncertain/informative samples in the collection. Therefore, we introduce a Deep Learning Based Active Learning (DLBAL) method that may incrementally learn from a small number of annotated training samples to build an effective classifier with optimum feature representation. The proposed DLBAL method improves the contemporary AL approaches in two steps. First, EfficientNet-B0 networks are integrated into AL. The incrementally annotated useful samples may be used to update both the representation of the feature and the classification algorithm concurrently. Second, we provide an effective technique for sample selection that improves the performance of the classifier with fewer manual annotations at a lower cost. In contrast to previous approaches that emphasize only uncertain instances with low prediction confidence, we obtain a significant number of instances with high-confidence from the unannotated set for pattern learning. Particularly, these instances are randomly picked and assigned labels to them continuously. Detailed experiments are conducted to show that the proposed DLBAL system obtained reasonable results on two difficult image classification datasets, namely Cross-Age Celebrity face recognition Dataset (CACD) and Caltech-256. • Proposed an Active Learning technique for data annotation. • Improved the performance of classifier by employing Active Learning technique. • Easily extensible to other image classification related problems. • Minimized the human effort required for annotating the uncertain data. • Obtain high classification accuracy over CACD and Caltech-256 datasets. [ABSTRACT FROM AUTHOR]

A novel lane-changing trajectory control strategy in an iterative learning framework is proposed and its impact on fuel consumption of the off-ramp traffic system is analyzed in this paper. The iterative learning framework includes three layers. In the calibration layer, the lane-changing decision process is modelled by probabilistic neural network while a back-propagation neural network is designed to imitate the lane-changing trajectory. Moreover, we use a cost function and numerical simulation in the optimization layer to optimize the trajectory database and calibrate the proposed framework, respectively. In the application layer, simulation experiments are conducted to examine the fuel consumption of individuals and systems. The results indicate that connected automated vehicles (CAVs) can dissipate quickly after congestion is formed and complete lane-change and avoidance behavior in a more stable state after iterative learning. CAVs can reduce fuel consumption by 35% compared with human-driven vehicles. [ABSTRACT FROM AUTHOR]

• Variable-length motif discovery is used to detect power waveform motifs (PWMs) for the first time in unsupervised NILM. • An adaptive similarity threshold setting method is proposed for establishing an unsupervised motif discovery method. • Frequent sequence pattern mining is employed to learn the complete power patterns in appliance working cycles. • The proposed method is integrated into the incremental learning framework, which can gradually discover "unknown" appliances. • Comparison results on public and private datasets demonstrate our work performs better on complex appliance identification. Due to the diversity of appliances and users' power consumption behaviors, it is challenging to accurately extract load signature samples for non-intrusive load monitoring (NILM) in unseen scenarios. To this end, this paper proposes an enhanced NILM load pattern extraction method via variable-length motif discovery. Firstly, the variable-length motif discovery method is used for the first time to discover similar subsequences from the aggregated power time series to extract power waveform signature (PWS) samples for appliances and obtain the power waveform motifs (PWMs). Furthermore, the appliance PWM sequence pattern mining method is proposed to detect complete power consumption patterns of appliance working cycles, and eventually construct the load signature library. Finally, the similarity between the original PWS samples and the templates in the signature library is measured to realize load identification. In practice, the proposed method is intended to integrate into the incremental learning framework, so that the unknown appliances can be identified gradually by continuous iterative learning. The comparison testing results on the public and private datasets demonstrate that the proposed method can effectively discover various PWMs of different appliances, and accurately model their power consumption patterns, thus exhibiting better performance on the unsupervised load identification compared with the existing methods, especially for the complex appliances. [ABSTRACT FROM AUTHOR]

To improve the processing quality and efficiency of robotic belt grinding, an adaptive sliding-mode iterative constant-force control method for a 6-DOF robotic belt grinding platform is proposed based on a one-dimension force sensor. In the investigation, first, the relationship between the normal and the tangential forces of the grinding contact force is revealed, and a simplified grinding force mapping relationship is presented for the application to one-dimension force sensors. Next, the relationship between the deformation and the grinding depth during the grinding is discussed, and a deformation-based dynamic model describing robotic belt grinding is established. Then, aiming at an application scene of robot belt grinding, an adaptive iterative learning method is put forward, which is combined with sliding mode control to overcome the uncertainty of the grinding force and improve the stability of the control system. Finally, some experiments were carried out and the results show that, after ten times iterations, the grinding force fluctuation becomes less than 2N, the mean value, standard deviation and variance of the grinding force error’s absolute value all significantly decrease, and that the surface quality of the machined parts significantly improves. All these demonstrate that the proposed force control method is effective and that the proposed algorithm is fast in convergence and strong in adaptability.

At submicrometer manufacturing technology nodes, process variations affect circuit performance significantly. To counter these variations, engineers are reserving more timing margin to maintain yield, leading to an unaffordable overdesign. Most of these margins, however, are wasted after manufacturing, because process variations cause only some chips to be really slow, while other chips can easily meet given timing specifications. To reduce this pessimism, we can reserve less timing margin and tune failed chips after manufacturing with clock buffers to make them meet timing specifications. With this post-silicon clock tuning, critical paths can be balanced with neighboring paths in each chip specifically to counter the effect of process variations. Consequently, chips with timing failures can be rescued and the yield can thus be improved. This is specially useful in high-performance designs, e.g., high-end CPUs, where clock binning makes chips with higher performance much more profitable. In this paper, we propose a method to determine where to insert post-silicon tuning buffers during the design phase to improve the overall profit with clock binning. This method learns the buffer locations with a Sobol sequence iteratively and reduces the buffer ranges afterward with tuning concentration and buffer grouping. Experimental results demonstrate that the proposed method can achieve a profit improvement of about 14% on average and up to 26%, with only a small number of tuning buffers inserted into the circuit. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We introduce and study a model for learning in the limit by finite automata from positive data and negative counterexamples. The focus is on learning classes of languages with the membership problem computable by finite automata (so-called automatic classes). We show that, within the framework of our model, finite automata (automatic learners) can learn all automatic classes when memory of a learner is restricted by the size of the longest datum seen so far. We also study capabilities of automatic learners in our model with other restrictions on the memory and how the choice of negative counterexamples (arbitrary, or least, or the ones which are bounded by the largest positive datum seen so far) can impact automatic learnability. [ABSTRACT FROM AUTHOR]

With an increasing number of multirotor unmanned aerial vehicles (UAVs), solutions supporting the improvement in their precision of operation and safety of autonomous flights are gaining importance. They are particularly crucial in transportation tasks, where control systems are required to provide a stable and controllable flight in various environmental conditions, especially after changing the total mass of the UAV (by adding extra load). In the paper, the problem of using only available basic sensory information for fast, locally best, iterative real-time auto-tuning of parameters of fixed-gain altitude controllers is considered. The machine learning method proposed for this purpose is based on a modified zero-order optimization algorithm (golden-search algorithm) and bootstrapping technique. It has been validated in numerous simulations and real-world experiments in terms of its effectiveness in such aspects as: the impact of environmental disturbances (wind gusts); flight with change in mass; and change of sensory information sources in the auto-tuning procedure. The main advantage of the proposed method is that for the trajectory primitives repeatedly followed by an UAV (for programmed controller gains), the method effectively minimizes the selected performance index (cost function). Such a performance index might, e.g., express indirect requirements about tracking quality and energy expenditure. In the paper, a comprehensive description of the method, as well as a wide discussion of the results obtained from experiments conducted in the AeroLab for a low-cost UAV (Bebop 2), are included. The results have confirmed high efficiency of the method at the expected, low computational complexity.

The paper presents a novel autotuning approach for finding locally-best parameters of controllers on board of unmanned aerial vehicles (UAVs). The controller tuning is performed fully autonomously during flight on the basis of predefined ranges of controller parameters. Required controller properties may be simply interpreted by a cost function, which is involved in the optimization process. For example, the sum of absolute values of the tracking error samples or performance indices, including weighed functions of control signal samples, can be penalized to achieve very precise position control, if required. The proposed method relies on an optimization procedure using Fibonacci-search technique fitted into bootstrap sequences, enabling one to obtain a global minimizer for a unimodal cost function. The approach is characterized by low computational complexity and does not require any UAV dynamics model (just periodical measurements from basic onboard sensors) to obtain proper tuning of a controller. In addition to the theoretical background of the method, an experimental verification in real-world outdoor conditions is provided. The experiments have demonstrated a high robustness of the method to in-environment disturbances, such as wind, and its easy deployability.

In the trolley traveling, load swing may influence work efficiency and cause accidents easily, losing efficacy of optimal control law of a bridge crane. Iterative learning control was put forward to research the position and anti-swing controls of bridge crane. The mathematical model of bridge crane was built with Lagrange equation, positioning anti-swing simulation for bridge crane were carried out. The results show that the iterative learning control can implement highly accurate tracking and effectively restrain the swing angle by using the character of repetitive motion of bridge crane. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The consensus seeking problems for both discrete and continuous multi-agent networks are discussed from an iterative learning perspective. It is shown that the consensus seeking process can be viewed as an iterative learning process for agents under directed networks to improve their performances from time to time in order to achieve consensus. If a desired consensus state is specified, then the multi-agent system can be guaranteed to reach consensus through reducing the tracking error between each agent's state and the desired consensus state monotonically to zero with respect to the increasing of time. If there is no desired consensus state, then the agents can achieve consensus through reducing their states monotonically to the minimum quantity with increasing time. Simulations illustrate the observed results. [ABSTRACT FROM AUTHOR]

This paper studies the learning and generalization performances of pseudo-inverse linear discriminant (PILDs) based on the processing minimum sum-of-squared error (MS 2 E) and the targeting overall classification accuracy (OCA) criterion functions. There is little practicable significance to prove the equivalency between a PILD with the desired outputs in reverse proportion to the number of class samples and an FLD with the totally projected mean thresholds. When the desired outputs of each class are assigned a fixed value, a PILD is partly equal to an FLD. With the customarily desired outputs {1, -1}, a practicable threshold is acquired, which is only related to sample sizes. If the desired outputs of each sample are changeable, a PILD has nothing in common with an FLD. The optimal threshold may thus be singled out from multiple empirical ones related to sizes and distributed regions. Depending upon the processing MS 2 E criteria and the actually algebraic distances, an iterative learning strategy of PILD is proposed, the outstanding advantages of which are with limited epoch, without learning rate and divergent risk. Enormous experimental results for the benchmark datasets have verified that the iterative PILDs with optimal thresholds have good learning and generalization performances, and even reach the top OCAs for some datasets among the existing classifiers. [ABSTRACT FROM AUTHOR]

Magnetically levitated planar motor is a new-generation motion device in modern precision industry, while its advanced motion controller design is still of main concern. In this paper, a learning adaptive robust control (LARC) motion controller is proposed for a magnetically levitated planar motor developed in our laboratory to achieve good tracking performance. The planar motor consists of a Halbach permanent magnetic array as the stator, and a levitated platen containing four groups of three-phase windings as the mover. Based on the Lorentz force law, the mover placed in the magnetic field is subjected to vertical force for levitation and horizonal force for planar motion through dynamics decoupling and current allocation. An LARC control scheme containing adaptive robust control (ARC) term and iterative learning control (ILC) term in a serial structure is then proposed for the magnetically levitated planar motor to achieve high-performance tracking even there exist parametric variations and uncertain disturbances. Comparative experiments between traditional lead, ARC, ILC, and the proposed LARC are carried out on the planar motor to track sinusoidal, point-to-point, and planar circular motions, respectively. The experimental results consistently validate that the proposed LARC control strategy outperforms other controllers much, and possesses not only good transient/steady-state tracking performance but also parametric adaptation ability and uncertain disturbance robustness. The proposed scheme actually provides a practically effective technique for motion control of magnetically levitated planar motors in industrial applications. [ABSTRACT FROM PUBLISHER]