The performance of a mobile working machine is strongly dependent on the skills of the operator. It is commonly accepted that among operators, productivity may vary tens of percents. This research considers the human skill evaluation in working machines during normal work. A general framework for skill evaluation via task sequence recognition by a hidden Markov model (HMM) is described. The definitions of skill metrics, based on task resource consumption, task completion frequency, task difficulty, and ability to plan and make decisions, are given and justified through an example. The skill evaluation methods are illustrated by utilizing them in two industrial applications.

Hybrid microassembly combining robotic microhandling and self-assembly aims at having the best of the both: good efficiency, reliability, accuracy and capability in creating complex structures. In this paper, a microassembly technique combining robotic tweezer-type microgripper and droplet self-alignment is discussed. The assembly method is evaluated by applying it in different 3D assembly cases, which would pose problems to a solution based on robotics or self-assembly alone. In the first, part rotation is realized by capillary forces. In the second, hierarchical structures are realized. Finally, cantilever structures are created using the method.

Hybrid microhandling is a new approach in microhandling that combines the benefits of two major branches of microhandling: robotic micromanipulation and self-assembly under a unified framework. This paper presents an in depth experimental study of a hybrid microhandling technique that combines robotic micromanipulator and droplet self-alignment. The experimental study is carried out on a home-made microrobotics test platform adapted for the experiments. Three important aspects of the microhandling technique are investigated - the efficiency, reliability, and capability - through a series of tests. With the assistance of a high-speed camera, the duration and the trajectory of the droplet self-alignment process are studied. According to authors’ knowledge, such results have not been previously reported. The experimental results also indicate that such a hybrid microhandling technique has very low requirements on the precision of the handling robot (comparable to the dimensions of the micropart) while being able to achieve micrometer precision. Part sizes ranging from 50 to 300 micrometers can achieve perfect assembly results. The experiments also show, for the first time, that parts having different sizes can be reliably assembled using the proposed hybrid microhandling technique.

Novel objectives such as very low outage, high capacity, and high throughput are major challenging problems in radio resource management of mobile communication systems. More specifically, in radio resource scheduling (RRS), the aim is how to optimize available resources such as transmission power and data rate to achieve certain targeted objectives. Conventional RRS algorithms are based on optimizing one objective while keeping others as constraints. This paper proposes a novel distributed RRS algorithm based on analytic multi-objective optimization. The proposed algorithm relaxes the constraints and jointly optimizes all the required objectives. Infinity set of optimal solutions, called Pareto optimal, is obtained. Each solution in the set is optimal in a specific sense. The decision maker selects the required solution that fulfills the network requirements and conditions. Some of the conventional RRS algorithms are special cases of our multi-objective based algorithm. Detailed mathematical analysis of the proposed algorithm is given. Simulation results show the behavior of the proposed algorithm as well as its advantages over conventional algorithms.

Maintenance of widely distributed sensor networks becomes a big issue in the future with more cheaper sensor nodes. This paper describes and discusses a method including five maintenance variables for characterization of the state of spatially distributed measurement network. The variables are aimed to describe the reliability of the network for end user and to help making automated maintenance decisions. The descriptive concepts are called availability, accuracy, reliability, estimability and influence. All of these are described in numerical form in a case example concerning a weather station network.

In this paper, we apply well-known variable structure systems control theory to power control problem in mobile radio systems. The aim is to make the power control operation insensitive to unknown changes in the link gains. Some of well-known distributed power control algorithms (Foschini and Miljanic algorithm, fixed step, etc) are obtained as special cases of the proposed algorithm. Simulation results suggest that the proposed algorithm is relatively fast and robust. The algorithm is also verified with an advanced dynamic system simulator

The paper studies the fault diagnosis of induction motors using neural network time-series models. The problem has been widely discussed in the literature and neural networks have been used in the fault diagnosis of induction motors. However, the neural network models have been mostly static - dynamical neural networks have been overlooked and have not received enough attention in this context. Here neural network time-series models are created for the normal and faulty motor. A filter bank of the models is formed and a Bayesian classifier is used to determine the correct classification of the motor condition, when tested with different types of FEM simulated data for different degrees of load.

In this paper we present and analyze a new structure for designing a radial basis function neural network (RBFNN). In the training phase, input layer of RBFNN is augmented with desired output vector. Generalization phase involves the following steps: (1) identify the cluster to which a previously unseen input vector belongs; (2) augment the input layer with an average of the targets of the input vectors in the identified cluster; and (3) use the augmented network to estimate the unknown target. It is shown that, under some reasonable assumptions, the generalization error function admits an upper bound in terms of the quantization errors minimized when determining the centers of the proposed method over the training set and the difference between training samples and generalization samples in a deterministic setting. When the difference between the training and generalization samples goes to zero, the upper bound can be made arbitrarily small by increasing the number of hidden neurons. Computer simulations verified the effectiveness of the proposed method.

This paper describes a numerical method for estimating adhesion forces that are present in different microworld handling operations under a scanning electron microscope (SEM). Emphasis is put on the calculation of van der Waals force, although the results can be generalized in a straightforward manner to cover also electrostatic force. The interacting objects can have arbitrary shapes and arbitrary alignments relative to each other. Using surface formulation instead of volume formulation the accuracy of the estimation is enhanced and the computational complexity is reduced. Surface formulation also enables the division of the surfaces into separate regions. This guarantees better accuracy in the computations and makes it possible to use only part of the surfaces in the force computations.

In this paper, we introduce a new fully distributed algorithm for combining power and rate control in wireless communication systems. The algorithm is based on minimizing a multi-objective definition of an error function. In this paper, we defined three objectives. The objectives are: 1) minimizing the transmitted power, (2) achieving at least the target carrier to interference ratio (CIR) which id defined at the minimum data rate, and 3) achieving the maximum CIR which is defined at maximum data rate. An optimized solution can be obtained by using a multi-objective optimization technique. The suggested algorithm is simple to implement and very efficient. The simulations carried out on the UMTS specifications indicate that our algorithm give a very good performance in terms of data rate, outage probability, convergence speed and transmission power consumption.

This paper introduces a feedforward charge control method, which controls the displacement by the amount of current fed to the actuator. The method includes estimation and compensation of the power losses occurring in the actuator. Power losses are estimated with an experimentally created dynamic model, that does not include the load and self heating effects. Even though the method is based on feedforward control, the amount of current is controlled in closed loop, using a precise current measurement and a PID controller. Experiments with a piezo bender show promising results; the hysteresis was nearly reduced to one part in twenty and drift to one part in ten, in comparison to open-loop voltage control. The proposed method can predict the power losses quite accurately and can therefore be utilized not only for the control but also for power estimation in applications where power consumption is critical.

Environmental conditions, such as temperature and humidity can influence the adhesion forces and microtribological properties of microparts. To have a good understanding of microassembly, it is essential to study the influences of environmental conditions to microassembly process and system. This paper presents our research in environment controlled microassembly, including an environment controlled microassembly system and early results of the influences of environmental parameters on microassembly and pick-and-place operations. The environment controlled microassembly station can control the environmental conditions including temperature and humidity in a closed chamber where microassembly is carried out. The temperature can be controlled in the range of 10-40/spl deg/C and relative humidity of 5-80% RH. Experimental results of the influences of environmental conditions on a piezoelectric bender and piezo-ultrasonic stage are reported, as well as errors of pick-and-place operation. The experimental results show that both temperature and humidity can influence the performance and errors.

In cellular radio systems, the convergence rate of the power control is one of the most important criteria that determines the practical usability of a given algorithm. A good convergence rate guarantees fast reduction of the effects caused by rapidly changing propagation and traffic conditions. A power control algorithm is proposed based on the ideas of the multi-input-multi-output (MIMO) proportional-integral (PI)-controller and block power control (BPC). As in the work of Z. Uykan et al. (see ISSSTA 2000, IEEE, vol.2, p.782-5, 2000), the PI-approach of deriving the power level is taken, however, now in the MIMO form. Here it is formulated with the idea of BPC, which is that there is centralized control within the blocks without any information chance between the blocks. Computational experiments in CDMA system are carried out.

Large variations of signal strength due to Rayleigh fading radio channel are observed in cellular radio communication systems. Power control (PC) is needed to mitigate this effect. It is essential in WCDMA, where many users share the same transmission medium simultaneously. Difficulties arise in power control because the conditions of radio channels change very rapidly. In this paper we propose a new adaptive robust algorithm for closed-loop power control for WCDMA systems, suitable for real implementation. The scheme relies on the generalized predictive controller (GPC), which is known to be able to control very difficult processes, and an adaptive self-tuning control strategy to automatically adjust the controller parameters. The approach was shown in simulations to outperform the conventional bang-bang controller.

A fuzzy system is proposed to compute new target values for low level controllers during grade changes in a paper machine. The fuzzy system is designed and tuned in co-operation with human operators of the paper machine. The design method utilizes both heuristic knowledge and estimation techniques based on input-output data. The input fuzzy sets and the rule base are initially designed verbally and thereafter fine-tuned by a least squares algorithm. An industrial example illustrates the iterative design process. It shows that transparency and a priori knowledge included in the model are preserved also during optimization of the fuzzy model.

A previous paper has derived a clustering-based upper bound on mean squared output error of radial basis function networks that explicitly depends on the network parameters. In this study we focus on single-hidden-layer-sigmoid perceptron. Using the analysis of the previous paper, this paper (i) presents a similar upper bound on output error of the sigmoid perceptron and the upper bound can be made arbitrarily small by increasing the number of sigmoid units, and (ii) proposes unsupervised type learning of input-layer (synaptic) weights in contrast to traditional gradient-descent type supervised learning, i.e., the proposed method minimizes the upper bound by a clustering algorithm for determining the input-layer weights in contrast to the gradient-descent type algorithm minimizing the output error, which is traditionally used in the design of the perceptron. The simulation results show that (i) the proposed hierarchical method requires less time for learning when compared to gradient-descent-type supervised algorithm, (ii) it yields comparable performance in comparison with radial basis function network, and (iii) the upper bounds minimized during the clustering are quite tight to the output error function.

The continuous increase of cellular phones shapes up the appearance of new communication systems and new technologies. In real life the radio signals are subjected to Rayleigh fading (fast fading) and interference. These result in large variations in signal strength. Power control (PC) is needed to compensate for these effects. Power control is one of the critical radio resource control functions in a WCDMA-based system. This paper describes a contribution of applying an adaptive (self-tuning) controller in the fast power control loop of WCDMA systems. Replacing the conventional controller with a controller that decides the PC command based on the history of the PC commands and errors improves the quality of the transmission significantly.

The key point in design of radial basis function networks is to specify the number and the locations of the centers. Several heuristic hybrid learning methods, which apply a clustering algorithm for locating the centers and subsequently a linear leastsquares method for the linear weights, have been previously suggested. These hybrid methods can be put into two groups, which will be called as input clustering (IC) and input-output clustering (IOC), depending on whether the output vector is also involved in the clustering process. The idea of concatenating the output vector to the input vector in the clustering process has independently been proposed by several papers in the literature although none of them presented a theoretical analysis on such procedures, but rather demonstrated their effectiveness in several applications. The main contribution of this paper is to present an approach for investigating the relationship between clustering process on input-output training samples and the mean squared output error in the context of a radial basis function netowork (RBFN). We may summarize our investigations in that matter as follows: 1) A weighted mean squared input-output quantization error, which is to be minimized by IOC, yields an upper bound to the mean squared output error. 2) This upper bound and consequently the output error can be made arbitrarily small (zero in the limit case) by decreasing the quantization error which can be accomplished through increasing the number of hidden units.

Piezoelectric actuators have such disadvantageous properties as hysteresis nonlinearity and drift. This paper discusses a hybrid model to describe those properties. The model consists of a dynamic linear part and a static nonlinear part. The dynamic part uses two poles to describe the dynamic response and the drift property. The static nonlinear part modifies the parameters of the linear dynamic model to adapt the model to hysteresis nonlinearity and gain nonlinearity. The model is generated for a piezohydraulic actuator based on a multi-stage step signal. Simulation and experimental results show that the model is able to predict the drift and the nonlinear properties of the actuator.

The paper presents a new parallel micromanipulator that is composed of three piezohydraulic actuation systems. The basic elements of the actuation system are a piezoelectric actuator, a bellows and hydraulic oil. The use of the flexible bellows results in a new type of parallel structure, where the joints are integrated into actuator links. The joint-free tripod-like micromanipulator is controlled using a real-time control software that is based on a multi-level architecture. Control is organised in four levels for nonlinearity compensation, position feedback control, supervision and tackling of automatic operations. The micromanipulator presented provides an exceptional combination of submicrometer resolution, large work space, miniature size and advanced control.

This paper focuses on the open-loop and closed-loop position control of a tripod-like joint-free parallel micromanipulator that is composed of three piezohydraulic actuation systems. The micromanipulator is controlled in open-loop using a general inverse kinematics model. Open-loop control is sufficient in many applications but when high accuracy and high speed are required, closed-loop control must be applied. The closed-loop control of the micromanipulator is organized in multiple levels. The first level compensates nonlinearities of the actuation systems and the second level controls the position of the end-effector. Level three is a supervisory level and level four tackles automatic operations. Level two, i.e. the position feedback controller, consists essentially of two single input/single output (SISO) proportional-integral (PI) controllers and an incremental form of the inverse kinematics model. The experimental results show that the position feedback control efficiently eliminates drift and vibration of the end-effector.

This paper describes a new miniaturized actuation system designed especially for parallel link micromanipulators; the system is mechanically designed to form a very compact structure when used in parallel configuration. It consists of a monolithic unimorph type of piezoelectric actuator, an electro-deposited bellows, a tank and hydraulic fluid. The displacement of the piezoelectric element is hydraulically magnified and converted to the displacement of the bellows. The piezohydraulic actuator provides movement from a micrometer level up to a maximum displacement of 500 /spl mu/m.

Fault detection and diagnosis is an important problem in process automation. Both model-based methods and expert systems have been suggested to solve the problem, along with the pattern recognition approach. A number of possible neural network architectures for fault diagnosis are studied. The multilayer perceptron network with a hyperbolic tangent as the nonlinear element seems best suited for the task. As a test case, a realistic heat exchanger-continuous stirred tank reactor system is studied. The system has 14 noisy measurements and 10 faults. The proposed neural network was able to learn the faults in under 3000 training cycles and then to detect and classify the faults correctly. Principal component analysis is used to illustrate the fault diagnosis problem in question.>

A rigid body model for compliant motion of a manipulator is derived. The model is formulated in the joint coordinate frame, and then transformed into the constraint frame to reduce the dimensionality of the model. The proposed model is useful in the simulation of force-controlled manipulators. Examples considering the tasks of a 'peg in a hole' and 'turning a crank' are given. The basic structure of the model is represented in a general framework applicable to many other constrained mechanical systems. A control architecture is suggested, which according to the model leads to exact decoupling of force and position-controlled directions.>

The equations of motion for a constrained mechanical system are derived from D'Alembert's principle utilizing Lagrange multipliers. An explicit expression for the Lagrange multipliers is developed and their physical significance is explained. It is also suggested, how frictional effects can be simply incorporated into the model. The theory is applied to a serial link manipulator, whose tool tip is moving on a frictional surface.

A linear multivariable PI-controller that can be designed based on step responses is presented. Choice of fine tuning parameters for the controller is discussed and solved as an optimization problem. Optimization results suggest that fine tuning can be chosen very simply. The problem of performing step response experiments with manipulators is brought up. Because of the unstability of the open loop system, step responses are best to carry out with manipulator simulators. Also a suggestion of how to deal with the positional variances of the manipulator dynamics is presented.

A constructive solution to the estimation problem of linear hereditary systems with time-varying delay is presented using a least-squares fit to the contaminated data. The utilization of optimum control theory yields a split boundary value problem, which is transformed to an initial-value problem by utilizing the theory of Fredholm resolvents. Applications of the theory to man-machine systems, for example, are indicated.