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The paper describes advances made in a computer method for the detection of paper shrinkage during the drying stages. The technique is based on two-dimensional Fourier transformations used to detect those periodic marks left on the paper by the wire during the formation stage. These marks are obtained from images of paper taken by low-cost CCD cameras for a later matching with those of the wire. Differences in separation of lines for the two cases are due to paper shrinkage. Finally some comments on hardware considerations of the sensor design and potentials of the method for the paper and graphic industry are given

This paper constitutes a review of the research work completed at the Computing Center of the USSR Academy of Sciences dealing with the theory of optimal orbit transfers for space vehicles powered by low-thrust rocket engines.

Recycling of paper is typically performed in a chain of processes. An important process is the peroxide bleaching implementing the means to achieve a high brightness quality. As this process step requires consumption of various chemicals, it is quite cost intensive. This paper presents means to optimize the bleaching stage to achieve an (adaptable) trade-off between cost and brightness: First, the time-varying deadtime of the bleaching stage is estimated using a Kalman filter. Second, the bleaching stage is modelled by Gaussian processes aiming to be used for optimization of the bleaching stage by application of a model predictive controller.

The problem addressed is to construct a feedback regulator that stabilizes an equilibrium of the randomly forced nonlinear dynamic system, and synthesizes a required stochastic sensitivity of this equilibrium. The aim of this paper is to demonstrate how elaborated control theory can be used for the protection of the ecological systems against catastrophic shifts. In present paper, a stochastically forced predator-prey model with Allee effect is studied. A probabilistic mechanism of the noise-induced extinction of both species is shown. Using stochastic sensitivity functions technique, we construct confidence ellipses and estimate the threshold value of the intensity for noise generating a transition from the stable coexistence of both species to the extinction. For the stabilization of this population system, a method of the control for confidence ellipses is applied. It is shown that the regulator provides a low level of stochastic sensitivity and prevents unwanted ecological catastrophes.

Fly-back phase of any Reusable Launch Vehicle (RLV), with wings, encounters ballistic flight in the absence of aerodynamic forces, in rare atmosphere at high re-entry altitude. Lifting vehicles enter the atmosphere at high angle of attack to increase the drag so that the vehicle velocity can be reduced. High angle of attack also increases the lift to balance the weight for equilibrium glide flight. During high altitude flight, angle of attack and side-slip estimations based on information from inertial sensors and modeled wind, are accurate enough to be used for feedback in attitude control. Once RLV descends below certain altitude and Mach number reduces below certain value, wind's contribution in angle of attack and side-slip becomes prominent and inertial navigation based estimates of these angles, no more remain accurate for their use in attitude control. To overcome this problem, normal and lateral accelerations are directly used in place of angle of attack and side-slip angle, respectively. Two aspect of control law design are covered in this paper which are applicable to descent flight of a typical RLV. First aspect is the impact of change in feedback signals from side-slip angle to lateral acceleration, on stability of lateral-directional dynamics. It is seen that the selection of feedback variables or sensors, significantly changes the achievable stability margins. Second aspect is the role of sensor placement w.r.t. CG, especially lateral accelerometer, on the stability of lateral-directional dynamics. This aspect of sensor placement is being discussed for the first time in any paper. It is seen that proper placement of accelerometer results in better stability margins.

Stability and convergence properties of large-scale integrated systems are essential aspects of the development of truly useful and well-designed cyber-physical systems (CPS). Because of the desire for flexible, adaptive and reactive cyber-physical systems, i.e., global operation, nonlinear analyses and tools are especially important in CPS. Hence, Lyapunov methods are at the core of many critical control methodologies for such systems. This paper considers Lyapunov stability for approximately symmetric systems. Many robotic systems, such as swarms and fleets of mobile robots, distributed sensor networks, highly integrated cyber-physical systems, etc. , are comprised many identical interacting agents, and our prior work has developed computationally efficient stability analyses of the control and dynamics of such symmetric systems. This paper extends those results to the important case where all the agents are not identical, which is important for real-world applications where it is not possible to have exactly identical agents. Importantly, these results do not require the components to have small differences. However, the bounds on the nature of the solutions will obviously depend on how different the agents are.

It is well known that in most bilateral teleoperation systems, variable time-delays arise in the communications. Motivated by this fact, the present work reports the extension to the (asymmetric) variable time-delays case of the adaptive controller for bilateral teleoperators with uncertain parameters and constant time-delays, reported in Nuno et al (2010) [Nuno, E., Ortega, R. and Basanez, L. (2010). An adaptive controller for nonlinear teleoperators. Automatica, 46(1), 155–159]. Compared to the previous work, the stability analysis does not rely on the cascade interconnection structure of the local and remote nonlinear dynamics and the linear interconnection map. Instead, the paper employs a different Lyapunov candidate function that incorporates a strictly positive term, the local and remote position error. With the only assumption that a bound of the time-delays is known, the paper also presents a sufficient condition that ensures the asymptotic convergence of position errors and velocities to zero. Some simulations, in free space and interacting with a stiff wall, illustrate the performance of the proposed control scheme in the presence of variable time–delays.

The paper deals with nonlinear control under input constraints of a non isothermal Continuous Stirred Tank Reactor (CSTR) using thermodynamic concepts. More precisely, the paper presents an extension of the previous work (Hoang et al. (2012)) where the jacket temperature is used as the only control input. Constrained input control strategy is based on the observation that the chemical reactor operates at an unique stationary temperature when the lower or upper bound of the input variable is imposed. This control design results in a global asymptotic stabilizing feedback law that provides a locally exponentially stable behaviour of the overall system.

There are two kinds of processes: the deterministic ones, in which the behavior in time is known, and the stochastic ones, which is governed by density probabilities functions. A stochastic process is defined as a Markov Process if the future state depends only on the current state and not on the previous ones. Markov Chains are stochastic processes with Markov properties. They are applicable in several areas, including Logistics. This paper is concerned with the problem of pest behavior for agriculture. It will be presented the basic concepts of Markov Chains, as well as their analysis and properties. After that, it will be assessed some stochastic Markov processes found in literature. Finally this paper presents a proposal for a modeling of a pest behavior for an agriculture production system.

This paper considers the application of a discrete-time LPV controller technique to active noise and vibration control (ANC/AVC) based on dilated LMIs. The goal of the controller design is to reduce the excitations induced by the combustion engine and transmitted through the chassis into the passenger compartment. These vibration disturbances are quite specific and consist of a varying nominal frequency and several multiples of it. The main contribution of this paper focuses in the modeling of the multisine disturbance. A parametrization of the cosine-function is proposed requiring only two parameters for L different frequencies. This reduction is based on a least square approximation. By separating the Lyapunov matrix and the system matrix in the controller design it is possible to use a Lyapunov function with dependent Lyapunov matrices leading to less conservative results. Simulations show the benefit of this method by attenuating six predefined frequencies with only two varying parameters.

The paper gives a PID gain tuning method using a Fictitious Reference Iterative Tuning (FRIT) approaches in the frequency domain. The proposed method obtains the PID gains that optimize a performance index, which evaluates the difference between the closed loop output and the reference model output in the frequency domain. The performance index is calculated using the Fourier transformation of one-shot experimental input-output data. The paper shows that a bandpath filter plays an important role in the Fourier transformation of the one-shot input-output data, and the optimal pre-filter, which well approximates the performance index to the original performance index, can be easily calculated in the frequency domain, and contributes to deriving a better PID gains improving the closed loop response through numerical examples.

This paper reviews some recent results in motion control of marine vehicles using a technique called Interconnection and Damping Assignment Passivity-based Control (IDA-PBC). This approach to motion control exploits the fact that vehicle dynamics can be described in terms of energy storage, distribution, and dissipation, and that the stable equilibrium points of mechanical systems are those at which the potential energy attains a minima. The control forces are used to transform the closed-loop dynamics into a port-controlled Hamiltonian system with dissipation. This is achieved by shaping the energy-storing characteristics of the system, modifying its interconnection structure (how the energy is distributed), and injecting damping. The end result is that the closed-loop system presents a stable equilibrium (hopefully global) at the desired operating point. By forcing the closed-loop dynamics into a Hamiltonian form, the resulting total energy function of the system serves as a Lyapunov function that can be used to demonstrate stability. We consider the tracking and regulation of fully actuated unmanned underwater vehicles, its extension to under-actuated slender vehicles, and also manifold regulation of under-actuated surface vessels. The paper is concluded with an outlook on future research.

In the paper a linear control system structure with modal controllers for multi-input, multioutput (MIMO) nonlinear dynamic processes is presented. The problems under study are exemplified by synthesis of a system for control a position and yaw angle of drillship described by a 3DOF nonlinear mathematical model of its low-frequency (LF) motions over the drilling point. The equations of the ship motions include the impact of the sea current and wind forces on the ship's hull. In the proposed control system structure a set of (stable) linear modal controllers is used that create an adaptive linear controller with variable parameters tuned appropriately to operation conditions chosen on the basis of two measured auxiliary signals. These are: the ship's current forward speed measured with respect to the water and systematically calculated the difference between the yaw angle and the sea current angle. The system synthesis is carried out by means of Diophantine polynomial matrix equations method for system pole placement after having linearized model of low-frequency motions made by the vessel at its nominal “operating points” in steady states. Furthermore, the control system has been extended by an additional integrating path to compensate influences of environmental disturbances. The final part of the paper includes simulation results of system operation with an adaptive controller of (stepwise) varying parameters along with conclusions and final remarks.

This paper addresses the problem of optimal sensor placement for acoustic range-based underwater target positioning. In particular, we focus on the experimental set-up whereby target positioning is performed by measuring the ranges between the underwater target and a number of surface units equipped with acoustic ranging devices and GPS. With the objective of affording the reader a concise overview of the main theoretical challenges involved, this paper starts with a survey of previous work done in the area, including that of the authors. By casting the problem at hand in the form of a classical estimation problem we describe and solve the equivalent mathematical problem of maximizing the determinant of a conveniently defined Fisher Information Matrix (FIM). The latter is related to the Cramer-Rao Bound, which equals the smallest possible position estimation error variance that can possibly be achieved with any unbiased estimator. To further clarify the presentation, the details of Monte Carlo simulations in 2D and 3D with a selected target positioning algorithm are included to confirm the theoretical results numerically.

In this paper the authors present a hexapod robot structure. For the leg has been developed a simple simulation interface to ease the analysis of the motion. The control part is an experimental one and uses Matlab and Arduino Duemilanove development board. The movement of the leg tip along a predefined trajectory was made using a walking algorithm. Moving the leg tip from one point to another is made in two steps: stance phase and swing phase. The communication protocol is also presented. In order to control the robotic leg the direct kinematical model and inverse kinematical model were used. Also the dynamic model using Lagrange method was developed and implemented using SimMechanics toolbox and ode45 function from Matlab. The paper includes some experimental results related to the leg control and some simulations regarding dynamic model.

This paper discusses the trajectory planning problem of a four-degrees-of-freedom (4-DOF) robotic manipulator with similar upper limb structures of human beings. The spatial motions of the end-effector (i.e., wrist) can be desired in terms of controlling four motors of the robotic manipulator; hence, such a configuration results in the redundancy problem. In general, Jacobian solutions perform linear approximations of inverse kinematics problems with redundancy conditions. With human beings, the same wrist position can be found from different limb postures, and these postures depend on different motion scenarios such as writing words, waving hands and shaking hands. Nevertheless, Jacobian solutions are hardly to realize specific limb motion scenarios of human beings. Therefore, this paper proposes a supervised neural network based robotic manipulator trajectory planner which constructs limb motion characteristic models according to relative joint posture features with respect to different motion scenarios. The motion features are further used to provide an auxiliary condition for eliminating the redundancy problem of the inverse kinematics as well as to meet specific motion scenarios. Finally, several trajectory planning results were evaluated in terms of the Jacobian and neural network approaches respectively by using an optical motion capture system. Experiment results demonstrated that the proposed neural network based control system performs similar motion behaviors when compared to Jacobian approaches based on the same test trajectory and motion scenario.

This paper presents a thermodynamically consistent model of MSMA (Magnetic Shape Memory Alloys) under port Hamiltonian framework. It is based on previous works on MSMA proposed in (Gauthier et al., 2008; Calchand et al., 2011). The main difference lies in the choice of the state variables and manipulated thermodynamic forces. Furthermore in (Gauthier et al., 2008), subsequent experiments revealed a highly hysteretic behavior of these materials. Here, the simplified hysteretic behavior is incorporated into the port-hamiltonian model to obtain a finer and more precise model. Such modeling will allow the use of a wide range of energy based methods to design the associated control system. The paper ends with some extensions to more complex hysterestic phenomena by using Preisach like model. First ideas are proposed to extend the previous physical model to systems with internal hysteretic loops.

In the paper an adaptive multi-controller control system utilizing a TSK fuzzy rules for a MIMO nonlinear dynamic plant is presented. The problems under study are exemplified by synthesis of a position and yaw angle control system for a drillship described by a 3DOF nonlinear mathematical model of low-frequency motions made by the drillship over the drilling point. In the proposed control system use is made of a set of linear modal controllers that create a multi-controller structure from which a group of controllers appropriate to given operation conditions is chosen and used to calculate, by using TSK fuzzy rules, control signals. The final part of the paper includes simulation results of system operation with an adaptive controller of (stepwise) varying parameters along with conclusions and final remarks.

The paper addresses the problem to recognize a level of robust controller design in our Institute and show the difficulties of implementation to student courses. In the first part of paper we introduce the survey of robust controller design for SISO systems with generalization design procedure for structured and unstructured uncertainties. The second part of paper is devoted to MIMO systems. In the frequency domain robust controller design procedure we reduce to independent design of SISO subsystems and in time domain the LMI or BMI approaches with polytopic system description are favorable.

The paper describes a novel method to group data points into clusters. Our method is derived from the assumption of Gaussian distribution of the points. We apply the clustering method to the well-known Iris flower data set and we suggest a nonlinear discriminant analysis of the data for a more sophisticated classification. To the best knowledge of the authors this paper shows the best classification result for the Iris data so far.

Using a model of the turtle retina, in this paper we show how the model can be used to discriminate between targets that are moving, with a constant speed, along several different directions. Model of retinal cells are chosen from an earlier work of the authors, described in the PhD thesis of the first author, and a patch has been constructed on the visual streak of the retina. Distributions of intensity sensitive ‘A Cells’ and direction sensitive ‘B Cells’ are chosen from data already reported in the literature and our model of the retinal patch follows this distribution. The model patch is exposed to point targets moving at constant speed but along several directions that are separated by 2°. The obtained patch response is analyzed by two different methods discussed in this paper. In the first method the patch responses are low pass filtered and projected onto a vector on the principal component space. Detection is performed using maximum likelihood method assuming that the vector is distributed as a Gaussian random process conditioned on the direction of the target. In the second method, the spike rate is computed as an intensity function of a vector Poisson process. The detection algorithm is based on minimizing the distance between the intensity function of an unknown input to the intensity functions of inputs computed for an array of targets. The detection algorithms presented in this paper have been implemented using ‘a moving time window of detection’ and a window of ‘20 ms’ is presented for the purpose of illustration. Detection results are presented using a root mean square error criterion.

This paper presents a robust nonlinear state feedback linearization approach for power systems with uncertainties. The uncertainties considered here are a class of bounded perturbations to the state model of power systems. The control law is applied through the excitation system of the synchronous generators connected to the power systems to enhance the stability of the system. In this paper, uncertainties are considered for single machine infinite bus (SMIB) system. A Lyapunov-based concept is used to guarantee uniform ultimate boundedness.

Performance evaluation models are used by companies to design, adapt, manage and control their production systems. In the literature, most of the effort has been dedicated to the development of efficient methodologies to estimate the first moment performance measures of production systems, such as the expected production rate, the buffer levels and the mean completion time. However, there is industrial evidence that the variability of the production output may drastically impact on the capability of managing the system operations, causing the observed system performance to be highly different from what expected. This paper presents a general theory and a methodology to analyze the cumulated output and the lot completion time variability of unreliable machines and systems characterized by general Markovian models. Both discrete models and continuous reward models are considered. We then discuss two simple examples that show how the theory developed in this paper can be applied to analyse the dependency of the output variability on the system parameters.

This paper discusses the identification of linear and non-linear black-box models for describing a diesel engine selective catalytic reduction (SCR) system. SCR aftertreatment systems form an important technology for reducing the NO x produced by diesel engines, and therefore good models are essential for the control of these systems. This paper compares a linear and a non-linear model for identification of the system. The output signals of the SCR were generated from 4 measured input signals, using a simulated 18 state model. The experiments with a recursive prediction error method, RPEM, with only 2 states show that the system can be accurately approximated with a much simpler model. The RPEM estimates 16 unknown parameters while the linear model uses 9 parameters. The results were compared based on the model fit and it was clear from the validation data set that the non-linear model gives better results and captures more of the system dynamics as compared to the linear model. A comparison of the RPEM using the midpoint integration method and the Euler method for discretisation was also made for the models. The results clearly show that the more accurate discretisation algorithm results in a better model fit.

In this paper, we provide a consolidated summary of the work reported in [1], [2], [3], [4] which is based on probe based high density storage and nano-imaging using atomic force microscopy (AFM) setup. In probe based high density storage, we consider a data storage system that operates by encoding information as topographic profiles on a polymer medium. A cantilever probe with a sharp tip (few nm radius) is used to create and sense the presence of topographic profiles, resulting in a density of few Tb per in.2. The high quality factor dynamic mode operation, that is less harsh on the media and the probe, is analyzed. The read operation is modeled as a communication channel which incorporates system memory due to inter-symbol interference and the cantilever state. Next, the solutions to the maximum likelihood sequence detection problem and maximum a posterior (MAP) symbol detection problem based on the Viterbi algorithm and BCJR algorithm respectively are devised. Experimental results demonstrate that the performance of the Viterbi and BCJR detectors are several orders of magnitude better than the performance of other existing schemes. Nano-imaging has played a vital role in basic sciences as it enables interrogation of material with sub-nanometer resolution. In this paper, we also present a high speed one-bit imaging technique using dynamic mode AFM with a high quality factor cantilever. Experimental results demonstrate that our proposed algorithm provides significantly better image resolution compared to current nano-imaging techniques at high scanning speed.

This paper proposes a fault detection method for hysteretic base-isolation systems. One major difficulty in the context of fault detection is to obtain a scheme that is robust with respect to disturbances (in this case, the seismic excitation) that are not faults. One of the key contribution of this paper is a Lyapunov-based disturbance (seismic) observer that leads to the design of a robust fault detection scheme. The different fault types considered in this paper are stiffness and damping variations in the system. The proposed fault estimation method provides a direct estimate of the size and severity of the fault, which can be important in many civil engineering applications. A design procedure is described, and nonlinear simulation results are presented to demonstrate the applicability of the proposed method.

This paper addresses the Linear Parameter Varying (LPV) control of robot manipulators. The dynamical model of such mechanical systems, obtained by writing the Euler-Lagrange equations, is nonlinear. Based on the measured signals and some change of variables, the nonlinear model can lead to an equivalent LPV state-space representation of the system. In this paper, we present a design method of state-feedback and output-feedback LPV controllers for robot manipulators. This method uses structured parameter-dependent Lyapunov matrices and proposes augmented Linear Matrix Inequalities (LMI) conditions with structural constraints. These design conditions are given as a finite number of LMIs. The validity of the proposed approach is demonstrated through simulations.

In order to improve apples quality, many methods have been done on planting, harvesting and storing. However, mould core apples are caused by fungi or/and humid storage condition. Therefore, mould core apples should be selected as the last step to improve the apples quality before selling. This paper describes a detection system depending on X-ray technology, which can get the internal image of the detection objection, to detect the mould core apple. Nevertheless, X-ray is always avoided in the fruit detection because of the radiation of X-ray and the food safety. This paper solves this problem by using a low-level radiation of X-ray radiant. However, the low-level radiation of X-ray radiant generates a low contrast image, which raises a problem in X-ray image processing because it is difficult to distinguish the background and foreground in the low contrast image. In order to perform successful detection of mould core apple, in this paper, firstly, the Wiener filter is utilized for decreasing the image noise from the camera or/and X-ray machine imaging system. Then, the X-ray image is enhanced using contrast enhancement method to solve the problem of low contrast X-ray image. Thirdly, the mould core area is extracted by region recognition and edge detection method after using Lifting Wavelet Transform (LWT). Finally, support vector machine (SVM) is utilized to classify the mould core apple. The experiment results show that the image processing and classification method could detect mould core apple with a success rate of 95%.

This paper describes a methodology based on Gaussian distribution approach for generating capacity reliability evaluation of a power system. The presented methodology has developed using continuous probability distribution function. The failure probability evaluation of generating system accounts the load models as load duration curve and is based on maximum average generation capacity. The probability of load exceeding the generating capacity has also been considered in LOLP evaluation. The described methodology in this paper is based on safety factor concept and peak load considerations. The test results using proposed methodology and various discussions have been presented for various generating capacity and load data available at Vijay Wada super thermal power plant, A. P.; India. Obtained results from extensive testing of the proposed method at different generating capacity and load demand considerations of Indian power system confirm the validity of the developed approach.

This paper presents a new controller for locking a moving object in 3-D space at a particular position (for example the center) on the image plane of a camera mounted on a robot by actively moving the camera. The controller is designed to cope with both the highly nonlinear robot dynamics and unknown motion of the object. Based on the fact that the unknown position of the moving object appears linearly in the closed-loop dynamics of the system if the depth-independent image Jacobian is used, we developed a nonlinear observer to estimate the 3-D motion of the object on-line. With a full consideration of dynamic responses of the robot manipulator, we employed the Lyapunov method to prove asymptotic convergence of the image errors. Experimental results are presented to support the approach in this paper.

Abstract The overall task of a supermarket refrigeration system is to maintain the quality of the foodstuff. This is done by making use of a refrigeration cycle in which a refrigerant transport heat from the refrigerated display-cases to the outdoor surroundings. Typically the system is equipped with a decentralized control system neglecting interactions between subsystems. Though these interactions are minor they from time to time lead to a synchronization of the operation of the display-cases which causes an inferior control performance and increased energy consumption. In this paper we will analyze the synchronization using bifurcation theory and show that the system has a chaos-like behavior when it is not synchronized. Therefore, it is a good choice to de-synchronize the system by making the system chaotic. The positive maximal Lyapunov exponent is usually taken as an indication that the system is chaotic, it is used in this paper as a measure of performance for the tendency of the system to synchronize.

In this paper the existence of a common Lyapunov function for stability is guaranteed in a switching network of agents. The objective of the team is to achieve consensus in a modified-leader follower team while the team structure is changing during the mission. The stability of the team dynamics is guaranteed for networks with both balanced or unbalanced describing graphs with directional communication links. Although the original design strategy is based on an optimal control approach, for determining a common Lyapunov function the optimal gains have to be reselected. However, by introducing a criterion for control gains selection, a desirable performance can still be achieved. In this paper, we concentrate on one of the many possible criteria, namely performance-control effort criterion in details. Simulation results are presented to illustrate the performance and capabilities of the team in presence of a switching structure and switching leader scenarios.

This paper focuses on the controller design for a wheeled mobile robot to track a moving target when its control inputs (linear and angular velocities) are limited. This issue is significant in practice, as the unpredicted target motions may cause the control actions beyond the robot's capability. In the paper, after the system model is formulated in a form suitable for the controller design, a Lyapunov function considering the limits of the robot's control efforts into consideration is proposed. A control law setting the robot's linear and angular velocities is then obtained. The conditions for asymptotic target tracking by the robot are also established. Simulation results are provided to verify the effectiveness of the approach.

This paper investigated the tracking control of nonholonomic mobile robots with uncertainties. Nonholonomic kinematic systems with visual feedback are uncertain and more involved in comparison with common kinematic systems. Barbalat theorem and Lyapunov techniques were exploited to craft a dynamic feedback robust controller that enables the mobile robot configuration tracking despite the lack of depth information and the lack of precise visual parameters. The most interesting feature of this paper is that the problem was discussed in the image frame and the inertial frame, which made the problem easy and useful. The convergence of the error system by using the proposed method was rigorously proved. The simulation was given to show the effectiveness of the presented controllers.

This paper proposes an adaptive control method for an underactuated overhead crane system. To improve the transferring efficiency and enhance the security of the crane system, the trolley is required to reach the desired position as fast as possible, while the swing of payload needs to be within an acceptable domain. To achieve these objectives, a novel two-step design strategy consisting of a trajectory planning stage and an adaptive tracking control design stage, is proposed to attack such an underactuated system as overhead crane. In the first step this paper proposes a new S curve as the desired trajectory for trolley tracking, and in the second step, it constructs an adaptive control law to make the trolley track the planned trajectory. As shown by Lyapunov Techniques, the proposed adaptive controller guarantees an asymptotical tracking result even in the presence of uncertainties including system parameters and various disturbances. Simulation results demonstrate that the new S trajectory and the tracking controller achieves a superior performance for the underactuated cranes.

This paper concerns a swing-up control problem for an n -link revolute robot in a vertical plane with its first joint being passive and the rest being active. The objectives of this paper are: 1) to design a controller under which the robot can be brought into any arbitrarily small neighborhood of the upright equilibrium point, where all links of the robot remain in their upright positions; 2) to attain a global analysis of the motion of the robot under the controller. To achieve the above challenging objectives, first, this paper addresses how to devise iteratively a series of virtually composite links for designing a coordinate transformation on the angles of all active joints. Second, this paper constructs a novel Lyapunov function based on the transformation, and proposes an energy based swing-up controller. Third, this paper carries out a global analysis of the motion of the robot under the controller, and establishes some conditions on control parameters for achieving the swing-up control objective. This paper provides insight into the energy or passivity based control to underactuated multi-degree-of-freedom systems.

This paper deals with the robust stability of soft variable-structure controls. More precisely, the control of linear plants subject to parametric uncertainty and actuator saturation is considered. Earlier works are summarized and new results are presented in this paper. It is shown that for all considered types of soft variable-structure controls, the robustness analysis leads to parameter-dependent Lyapunov inequalities. An overhead crane control is given as an illustrating example.

This paper deals with the stabilization of a class of discrete nonlinear models. The considered models are taken in the Takagi-Sugeno's form with periodic parameters. The main goal of this paper is to reduce the conservatism of the stabilization conditions using a special class of candidate Lyapunov functions.

In this paper, the nonlinear scheduled anti-windup approach of Zaccarian and Teel (2004) is revisited in light of a new sector condition which allows for more general results. The main contribution of the paper is to transform the hysteresis switching design into a Lipschitz (continuous) nonlinear control law which allows to guarantee global results with non exponentially unstable plants (this is a necessary condition for global results) and regional results with large stability regions with exponentially unstable plants. The main tool exploited here is a continuously parameterized Lyapunov function that leads to a nonlinear anti-windup compensator. The effectiveness of the approach is illustrated on simulation examples where it is compared to the switching approach.

This paper focuses on visual servoing systems, particularly on image-based visual tracking problem. Basically, in image-based visual tracking, the error signal is computed in the image plane and the command signals are generated with respect to such error by means of a visual Jacobian. The primary controller design challenge of image-based visual tracking system is the robustness with respect to calibration parameter uncertainties. In this paper, a new nonlinear controller design technique is proposed for stable image-based visual tracking. The new controller design takes advantage of the sliding mode control and it yields more robust on calibration parameter uncertainties. The range of admissible uncertainties is enlarged to modify the uncertainty bound matrix. The proposed design technique was applied on a real system and effectiveness of the method was validated through experiments.

This paper presents an algorithm for identifying soil parameters for wheel-terrain interaction dynamics. The soil parameters are useful for traversability prediction, traction control, and performance optimization of a wheeled vehicle traveling on unknown terrain. The Composite Simpson's Rule (CSR) is employed to approximate integrals of the full wheel-terrain interaction dynamic model. This is to facilitate the implementation of soil parameter identification on this model and allow fast identification speed. The 2-stage iterative Newton Raphson (NR) method is used for soil parameter identification. Simulation results show successful identification of a complete set of soil parameters with relatively fast speed. The approach in this paper has great potential to be applied for real off-road wheeled vehicle.

A radome aberration causes degradation of miss distance as well as system stability at high altitude and high maneuver missile with RF seeker. These kinds of effectiveness have been already known well and many counter compensation techniques have been investigated. In this paper, proposed adaptive Particle filter estimates LOS rate excluding the radome induced error. Adaptive concept is very efficient in this problem because the actual radome aberration error is time varied and would not have Gaussian distribution. Particles of this filter are propagated and re-sampled such as conventional Particle filter, however, parameter value is changed as filter step progresses. LOS rate is allocated in particles and radome aberration error is defined to unknown parameter in this paper. Simulation results show that an estimated LOS rate yielded by adaptive Particle filter permits enhanced stability and considerable reduction of miss distance. Moreover, estimated LOS rate rapidly converges for varying radome error with non-constant slope.

This paper first considers the analysis of a spatially distributed controller for a spatially interconnected parameter-varying (SIPV) large scale system using a linear parameter-varying (LPV) framework. The concept of quadratic separators has been extended to compute a measure of worst-case performance for such systems by solving a linear matrix inequality (LMI) problem. The use of quadratic separator helps in using parameter dependent Lyapunov function in a systematic way which leads to less conservative controllers. The problem of synthesizing spatially distributed robust or gain-scheduled controllers for such systems leads to a bilinear matrix inequality and a hybrid evolutionary-LMI approach to solving this problem, based on LMI solvers and genetic algorithms, is proposed in this paper. Main advantage of this approach over other proposed approaches is that it is independent of controller structure. A design example illustrates the efficiency of the proposed method, where two controllers with different structures are synthesized.

This paper aims at showing for a real case the results obtained with the stochastic approach to the analysis of a sequence of alarms produced by the Apache system, a Sachem-like system that is dedicated to monitoring, diagnosing and controlling the zinc baths used by the Arcelor Group. Sachem is the Arcelor's generic extensive large-scale real time knowledge based system designed to monitor, diagnose and control processes. The paper shows how the knowledge of the behavior of a continuous process can be formalized in terms of relations between discrete events that are deduced from a sequence of Apache's alarms by the mean ofa stochastic approach. The stochastic approach is based on the representation of a sequence of alarms under the dual form of a homogeneous Markov chain and its corresponding superposition of Poisson processes. The paper shows how a functional model can be derived from a behavioral model produced according to the stochastic approach.

This paper proposes a variable structure controller (VS) with sliding sector for a hybrid system. At first, the extremum seeking control algorithm is used to find a Lyapunov function for the hybrid system. In general, a sliding sector is defined as a subset of the state space inside which some norm of state decreases. In the paper, then a sliding sector is designed for each subsystem of the hybrid system so that each state in the state space is inside at least one sliding sector with its corresponding subsystem, where the Lyapunov function found by the extremum seeking control is decreasing. Finally a variable structure control law is designed to switch the hybrid system among subsystems to ensure the decrease of the Lyapunov function in the state space. The resulted VS control system is quadratically stable as the Lyapunov function decreases in the state space. Simulation results are given to show the efficiency of the proposed VS controller.

In this paper, it is presented a fault-tolerant system based on output feedback H ∞ Markovian control for manipulator robots. Firstly, it is shown that with a control strategy based on deterministic nonlinear H ∞ approach the stability is guaranteed only if the robot stops completely after the fault detection, and then restarts the movement from zero velocity in an underactuated configuration. Secondly, it is shown that with the fault-tolerant system proposed in this paper, the stability is guaranteed, after the fault be detected, even with the robot still moving. Experimental results are presented.

In this paper, a nonlinear anti-sway control law for overhead cranes is investigated. The crane model itself is adopted from the literature; however, a new nonlinear decoupling control law that provides superior position-regulation and sway-suppression characteristics is proposed in this paper. The derived control law uses the sway angular rate as well as the sway angle as feedback. The performance of the proposed control law is compared with those of the PD and E 2 control laws in the literature. The transient characteristics of the proposed control law are also verified using a 3-D pilot crane. Simulation and experimental results are discussed.

Threaded fastenings are a common assembly method, accounting for over a quarter of all assembly operations. Threaded fastenings are popular because they permit easy disassembly for maintenance, repair, relocation and recycling. A model based approach for monitoring screw fastenings is presented in this paper. The proposed strategy can be integrated with a mechatronics based approach for automating threaded assembly systems. The screw insertion process is typically monitored using the torque vs insertion depth signature signals. For a screw insertion with a given set of parameters, the insertion signature signal will have a unique identity and by comparing the on line signature signals with the ideal signature signal, the integrity of the joint can be predicted. The approach adopted in this study is to use an analytical model developed by the authors to predict ideal insertion signals. However, this model requires various fixed process parameters as input, and it is not always possible to know these parameters in advance with sufficient accuracy. Hence the focus of this paper is the on-line parameter estimation during threaded assembly. A methodology for estimating four unknown parameters of a general self-tapping screw insertion is presented. The approach is based on the Newton Raphson Method (NRM). It is shown that up to four parameters required by the model can be reliably estimated in real time, from on-line torque signature signals. Experimental results are presented to validate the estimation procedure.

This paper addresses the problem of coordinated motion control of marine robots. A nonlinear control law is derived to steer two underwater vehicles along identical parallel paths, while ensuring that the lateral distance between them remains constant. The key idea explored in the paper is to elect one of the vehicles as leader and the other as follower. Both the leader and follower execute the same path following algorithm, the leader traveling along its assigned path at a desired speed profile. The velocity of the follower is then adapted based on the measure of a "generalized along-path distance" between the two vehicles. The amount of information to be exchanged between the two vehicles is thus kept at a minumum. Controller design builds on Lyapunov theory and backstepping techniques. Simulation results illustrate the performance of the coordinated motion control system proposed.

This paper extends the power hierarchy of dependability models developed by Malhotra and Trivedi (1994) and Muppala et al . (2000) to include Piecewise Deterministic Markov Processes (PDP) and PDP-related Petri Nets. PDPs are known as the largest class of continuous-time hybrid state Markov processes not involving diffusions. Since Petri Nets have proven to be extremely useful in developing Markov process models of complex practical processes, there is a clear need for a type of Petri Net that can play such role for developing PDP models. This paper defines such Petri Nets and shows their relation to PDPs and other Petri Nets.