Showing papers in "International Journal of Control Automation and Systems in 2004"

A Fuzzy Logic for Autonomous Navigation of Marine Vehicles Satisfying COLREG Guidelines

Abstract: An autonomous navigation algorithm for marine vehicles is proposed in this paper us- ing fuzzy logic under COLREG guidelines. The VFF (Virtual Force Field) method, which is widely used in the field of mobile robotics, is modified for application to the autonomous navi- gation of marine vehicles. This Modified Virtual Force Field (MVFF) method can be used in ei- ther track-keeping or collision avoidance modes. Moreover, the operator can select a track- keeping pattern mode in the proposed algorithm. The collision avoidance algorithm has the abil- ity to handle static and/or moving obstacles. The fuzzy expert rules are designed deliberately un- der COLREG guidelines. An extensive simulation study is used to verify the proposed method.

Anti-Sway Control of Container Cranes: Inclinometer, Observer, and State Feedback

Abstract: In this paper, a novel anti-sway control system that uses an inclinometer as a sway sensor is investigated. The inclinometer, when compared with a vision system, is very cheap, durable, and easy to maintain, while providing almost the same performance. A number of observers to estimate the angular velocity of the load and the trolley velocity are presented. A state feedback controller with an integrator is designed. After a time-scale analysis, a 1/4-size pilot crane of a rail-mounted quayside crane was constructed. The performance of the proposed control system was verified with a real rubber-tired gantry crane at a container terminal as well as with the constructed pilot crane. Experimental results are provided.

Approximate Dynamic Programming Strategies and Their Applicability for Process Control: A Review and Future Directions

Abstract: This paper reviews dynamic programming (DP), surveys approximate solution methods for it, and considers their applicability to process control problems. Reinforcement Learning (RL) and Neuro-Dynamic Programming (NDP), which can be viewed as approximate DP techniques, are already established techniques for solving difficult multi-stage decision problems in the fields of operations research, computer science, and robotics. Owing to the significant disparity of problem formulations and objective, however, the algorithms and techniques available from these fields are not directly applicable to process control problems, and reformulations based on accurate understanding of these techniques are needed. We categorize the currently available approximate solution techniques for dynamic programming and identify those most suitable for process control problems. Several open issues are also identified and discussed.

An IMM Algorithm for Tracking Maneuvering Vehicles in an Adaptive Cruise Control Environment

Abstract: In this paper, an unscented Kalman filter (UKF) for curvilinear motions in an interacting multiple model (IMM) algorithm to track a maneuvering vehicle on a road is investigated. Driving patterns of vehicles on a road are modeled as stochastic hybrid systems. In order to track the maneuvering vehicles, two kinematic models are derived: A constant velocity model for linear motions and a constant-speed turn model for curvilinear motions. For the constant-speed turn model, an UKF is used because of the drawbacks of the extended Kalman filter in nonlinear systems. The suggested algorithm reduces the root mean squares error for linear motions and rapidly detects possible turning motions. 1. INTRODUCTON Recently, the majority of automobile companies are developing various driver assistance systems to increase vehicle safety and alleviate driver workload. The driver assistance systems include adaptive cruise control (ACC), lane-keeping support, collision warning and collision avoidance, and assisted lane changes. The effectiveness of these driver assistant systems depends on the interpretation of the information arriving from sensors, which provide details of the surrounding vehicle environment and of the driver-assisted vehicle itself. In particular, all these systems rely on the detection and subsequent tracking of objects around the vehicle. Such detection information is provided by radar, lidar, and vision sensor. The assistance systems mentioned above have certain objectives that their controllers try to meet. Before a controller can make a decision that enables the driver to feel natural, the motion of the surrounding object must be properly interpreted from the available sensor information (2).

Design of Unknown Input Observer for Linear Time-delay Systems

Abstract: This paper deals with the unknown input observer (UIO) design problem for a class of linear time-delay systems. A case in which the observer error can completely be decoupled from an unknown input is treated. Necessary and sufficient conditions for the existences of such observers are present. Based on Lyapunov stability theory, thedesign of the observer with internal delay is formulated in terms of linear matrix inequalities (LMI). The design of the observer without internal delay is turned into a stabilization problem in linear systems. Two design algorithms of UIO are proposed. The effect of the proposed approach is illustrated by two numerical examples.

Mobile Robot Localization Using Optical Flow Sensors

Abstract: Open-loop position estimation methods are commonly used in mobile robot applications. Their strength lies in the speed and simplicity with which an estimated position is determined. However, these methods can lead to inaccurate or unreliable estimates. Two position estimation methods are developed in this paper, one using a single optical flow sensor and a second using two optical sensors. The first method can accurately estimate position under ideal conditions and also when wheel slip perpendicular to the axis of the wheel occurs. The second method can accurately estimate position even when wheel slip parallel to the axis of the wheel occurs. Location of the sensors is investigated in order to minimize errors caused by inaccurate sensor readings. Finally, a method is implemented and tested using a potential field based navigation scheme. Estimates of position were found to be as accurate as dead-reckoning in ideal conditions and much more accurate in cases where wheel slip occurs.

Robust Fuzzy Control of a Class of Nonlinear Descriptor Systems with Time-Varying Delay

Abstract: A robust fuzzy controller is designed to stabilize a class of solvable nonlinear descriptor systems with time-varying delay. First, a new modeling and control method for nonlinear descriptor systems is presented with a fuzzy descriptor model. A sufficient condition for the existence of the fuzzy controller is given in terms of a series of LMIs. Then, a less conservative fuzzy controller design approach is obtained based on the fuzzy rules and weights. This method includes the interactions of the different subsystems into one matrix. The effectiveness of the presented approach and the design procedure of the fuzzy controller are illustrated by way of an example.

Boundary Control of an Axially Moving Belt System in a Thin-Metal Production Line

Abstract: In this paper, an active vibration control of a translating steel strip in a zinc galvanizing line is investigated. The control objectives in the galvanizing line are to improve the uniformity of the zinc deposit on the strip surfaces and to reduce the zinc consumption. The translating steel strip is modeled as a moving belt equation by using Hamilton's principle for systems with moving mass. The total mechanical energy of the strip is considered to be a Lyapunov function candidate. A nonlinear boundary control law that assures the exponential stability of the closed loop system is derived. The existence of a closed-loop solution is shown by proving that the closed-loop dynamics is dissipative. Simulation results are provided. for zinc coating and is then pulled up vertically. The control objectives in the galvanizing line are to improve the uniformity of the zinc deposit on the strip surfaces and to reduce the zinc consumption. Therefore, an active control of the vibrations, with a minimal use of actuators and sensors, is currently the main research focus in the area of axially moving

Multi-Channel Vibrotactile Display for Teleoperated Assembly

Abstract: Presents the design and testing of a multi-channel vibrotactile display. It is composed of a cylindrical handle with four embedded vibrating elements driven by piezoelectric beams. Vibrations are transmitted to the hands through arrays of pins. The device was tested in sensory substitution for conveying force information during a teleoperated peg insertion. Results show that the device is effective in reducing peak forces during the insertion task.

Error Reduction of Sliding Mode Control Using Sigmoid-Type Nonlinear Interpolation in the Boundary Layer

Abstract: Sliding mode control with nonlinear interpolation in the boundary layer is proposed. A modified sigmoid function is used for nonlinear interpolation in the boundary layer and its parameter is tuned by a fuzzy controller. The fuzzy controller that takes both the sliding variable and a measure of chattering as its inputs tunes the parameter of the modified sigmoid function. Owing to the decreased thickness of the boundary layer and the tuned parameter, the proposed method has superior tracking performance than the conventional linear interpolation method.

A Passive Multiple Trailer System with Off-axle Hitching

Abstract: This paper deals with the design and control of passive multiple trailer systems for practical applications. Due to the cost and complexity of the trailer mechanism, passive systems are preferred to active systems in this research. The design and control objective is to minimize the trajectory tracking errors occurring in passive multiple trailers. Three sorts of passive trailer systems, off-hooked, direct-hooked, and three-point, are discussed in this paper. Trajectory tracking performance and stability issues under constant curvature reference trajectories are investigated for these three types. As well, various simulations and experiments have been performed for each type. It is shown that the proposed off-hooked trailer system produces a tracking performance that is superior to the others.

Decoupled Neural Network Reference Compensation Technique for a PD Controlled Two Degrees-of-Freedom Inverted Pendulum

Abstract: In this paper, the decoupled neural network reference compensation technique (DRCT) is applied to the control of a two degrees-of-freedom inverted pendulum mounted on an x-y table. Neural networks are used as auxiliary controllers for both the x axis and y axis of the PD controlled inverted pendulum. The DRCT method known to compensate for uncertainties at the trajectory level is used to control both the angle of a pendulum and the position of a cart si- multaneously. Implementation of an on-line neural network learning algorithm has been imple- mented on the DSP board of the dSpace DSP system. Experimental studies have shown success- ful balancing of a pendulum on an x-y plane and good position control under external distur- bances as well.

Adaptive Actuator Failure Compensation Designs for Linear Systems

Abstract: This paper surveys some existing direct adaptive feedback control schemes for lin- ear time-invariant systems with actuator failures characterized by the failure pattern that some inputs are stuck at some unknown fixed or varying values at unknown time instants, and ap- plications of those schemes to aircraft flight control system models. Controller structures, plant-model matching conditions, and adaptive laws to update controller parameters are inves- tigated for the following cases for continuous-time systems: state tracking using state feed- back, output tracking using state feedback, and output tracking using output feedback. In ad- dition, a discrete-time output tracking design using output feedback is presented. Robustness of this design with respect to unmodeled dynamics and disturbances is addressed using a modified robust adaptive law.

Distinctive Point Extraction and Recognition Algorithm for Various Kinds of Euro Banknotes

Abstract: Counters for the various kinds of banknotes require high-speed distinctive point ex- traction and recognition. In this paper we propose a new point extraction and recognition algo- rithm for Euro banknotes. For distinctive point extraction we use a coordinate data extraction method from specific parts of a banknote representing the same color. To recognize banknotes, we trained 5 neural networks. One is used for inserting direction and the others are used for face value. The algorithm is designed to minimize recognition time by using a minimal amount of recognition data. The simulated results show a high recognition rate and a low training period. The proposed method can be applied to high speed banknote counting machines. Common banknote counting machines only count one single type of banknote. When depositing more then one kind of banknote we first must sort the banknotes based on their face value before counting the total sum. Doing so takes time and is also very complex. To solve these problems, counters for the various kinds of banknotes have been developed. Counting machines for the various types of banknotes require high-speed recognition and counting because the two processes are performed simultaneously. Most recognition algorithms utilize the sizes or colors of banknotes. Gori and Priami (1) and Kim (6) used banknote size and their featured character for recognition. However, it is assumed that the inserted banknote must be authentic. If any paper that has the same size as a banknote is inserted, an error will oc- cur. Furthermore, Kim (6) used a CCD camera to rec- ognize the kind of banknote by applying it to any se- lected area of the image for banknote classification. Takeda and Nishikage (2) used two sensors to in- crease the number of recognition patterns. The pur- pose of the first sensor is discrimination for a known image and the second sensor is for exclusion of an unknown image. But, these methods require too much time to recognize a banknote because the obtained image using a CCD camera is very large and also in- cludes too much information such as noise. Therefore it is unsuitable for high-speed recognition processing. Lee (4) performed training and recognition through neural network and CIS sensor. He did not use the entire image but rather any one selected horizontal line as input data for recognition. It is necessary to reduce the amount of data for high speed recognition. In this paper, to reduce the amount of data required in the recognition process, we proposed a method using a lesser amount of input data than the other methods. For data reduction, particular blocks such as characters of banknotes should be selected. This is considered to be an effective way to reduce the amount of data. We used 4-bit gray scale images of banknotes. There are many black colored parts in gray scale banknote images, particularly the face value number. Black color features are also robust to noise. When noise is added to the black color, the noise is unnoticeable with the exception of some bright color noise. By using this feature, the black colored parts can be a distinctive data of banknotes for recognition and classification. For the banknote recognition process, a back-propagation neural net- work that has input vectors consisting of distinctive points was designed. The input vectors were created from distances between distinctive points and the ori- gin of the unique block. Seven kinds of Euro bank- notes were used as sample banknotes.

An Automatic Control System of the Blood Pressure of Patients Under Surgical Operation

Abstract: We developed an automatic blood pressure control system to maintain the blood pressure of patients at a substantially low level during a surgical operation. The developed system discharges two functions, continuous feedback control of the mean arterial pressure (MAP) by a state-predictive servo controller and risk control based on the inference by fuzzy-like logics and rules using measured data. Twenty-eight clinical applications were made beginning in November 1995, and the effects of the automatic blood pressure control on the operation time and on bleeding were assessed affirmatively by means of Wilcoxon testing. This paper essentially reports the engineering details of the control system.

Self-Organizing Polynomial Neural Networks Based on Genetically Optimized Multi-Layer Perceptron Architecture

Abstract: In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the "conventional" SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials as well as to consider a fixed number of input nodes at polynomial neurons (or nodes) located in each layer. However, this design process does not guarantee that the conventional SOPNN generated through learning results in optimal network architecture. The design procedure applied in the construction of each layer of the SOPNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomials, and input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between the approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented using pH neutralization process data as well as sewage treatment process data. A comparative analysis indicates that the proposed SOPNN is the model having higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

Active Contour Model Based Object Contour Detection Using Genetic Algorithm with Wavelet Based Image Preprocessing

Abstract: In this paper, we present a novel, rapid approach for the detection of brain tumors and deformity boundaries in medical images using a genetic algorithm with wavelet based preprocessing. The contour detection problem is formulated as an optimization process that seeks the contour of the object in a manner of minimizing an energy function based on an ac- tive contour model. The brain tumor segmentation contour, however, cannot be detected in case that a higher gradient intensity exists other than the interested brain tumor and deformi- ties. Our method for discerning brain tumors and deformities from unwanted adjacent tissues is proposed. The proposed method can be used in medical image analysis because the exact contour of the brain tumor and deformities is followed by precise diagnosis of the deformities.

Development of a Novel Direct-Drive Tubular Linear Brushless Permanent-Magnet Motor

Abstract: This paper presents a novel design for a tubular linear brushless permanent-magnet motor. In this design, the magnets in the moving part are oriented in an NS-NS-SN-SN fashion which leads to higher magnetic force near the like-pole region. An analytical methodology to calculate the motor force and to size the actuator was developed. The linear motor is operated in conjunction with a position sensor, three power amplifiers, and a controller to form a complete solution for controlled precision actuation. Real-time digital controllers enhanced the dynamic performance of the motor, and; gain scheduling reduced the effects of a nonlinear dead band. In its current state, the motor has a rise time of 30 ms, a settling time of 60 ms, and 25% overshoot to a 5-mm step command.. The motor has a maximum speed of 1.5 m/s and acceleration up to 10 g. It has a 10-cm travel range and 26-N maximum pull-out force. The compact size of the motor suggests it could be used in robotic applications requiring moderate force and precision, such as robotic-gripper positioning or actuation. The moving part of the motor can extend significantly beyond its fixed support base. This reaching ability makes it useful in applications requiring a small, direct-drive actuator, which is required to extend into a spatially constrained environment.

Delay-Dependent Control for Time-Delayed T-S Fuzzy Systems Using Descriptor Representation

Abstract: This paper presents a design method of delay-dependent control for T-S fuzzy sys- tems with time delays. Based on parallel distributed compensation (PDC) and a descriptor model transformation of the system, a delay-dependent control is utilized. An appropriate Lyapunov-Krasovskii functional is chosen for delay-dependent stability analysis. A sufficient condition for delay-dependent control is represented in terms of linear matrix inequalities (LMIs).

Game Theory Based Coevolutionary Algorithm: A New Computational Coevolutionary Approach

Abstract: Game theory is a method of mathematical analysis developed to study the decision making process. In 1928, Von Neumann mathematically proved that every two-person, zero- sum game with many pure finite strategies for each player is deterministic. In the early 50's, Nash presented another concept as the basis for a generalization of Von Neumann's theorem. Another central achievement of game theory is the introduction of evolutionary game theory, by which agents can play optimal strategies in the absence of rationality. Through the process of Darwinian selection, a population of agents can evolve to an Evolutionary Stable Strategy (ESS) as introduced by Maynard Smith in 1982. Keeping pace with these game theoretical studies, the first computer simulation of coevolution was tried out by Hillis. Moreover, Kauffman proposed the NK model to analyze coevolutionary dynamics between different species. He showed how coevolutionary phenomenon reaches static states and that these states are either Nash equilibrium or ESS in game theory. Since studies concerning coevolutionary phenomenon were initiated, there have been numerous other researchers who have developed coevolutionary algorithms. In this paper we propose a new coevolutionary algorithm named Game theory based Coevolutionary Algorithm (GCEA) and we confirm that this algorithm can be a solution of evolutionary problems by searching the ESS. To evaluate this newly designed approach, we solve several test Multiobjective Optimization Problems (MOPs). From the results of these evaluations, we confirm that evolutionary game can be embodied by the coevolutionary algorithm and analyze the optimization performance of our algorithm by comparing the performance of our algorithm with that of other evolutionary optimization algorithms.

Fuzzy Controller Design by Means of Genetic Optimization and NFN-Based Estimation Technique

Abstract: In this study, we introduce a noble neurogenetic approach to the design of the fuzzy controller. The design procedure dwells on the use of Computational Intelligence (CI), namely genetic algorithms and neurofuzzy networks (NFN). The crux of the design methodology is based on the selection and determination of optimal values of the scaling factors of the fuzzy controllers, which are essential to the entire optimization process. First, tuning of the scaling factors of the fuzzy controller is carried out, and then the development of a nonlinear mapping for the scaling factors is realized by using GA based NFN. The developed approach is applied to an inverted pendulum nonlinear system where we show the results of comprehensive numerical studies and carry out a detailed comparative analysis.

Further Improvements on Bose’s 2D Stability Test

Abstract: This paper proposes some further improvements on N.K. Bose's 2D stability test for polynomials with real coefficients by revealing symmetric properties of the polynomials, resultants occurring in the test and by generalizing Sturm's method. The improved test can be fulfilled by a totally algebraic algorithm with a finite number of steps and the computational complexity is largely reduced as it involves only certain real variable polynomials with degrees not exceeding half of their previous complex variable counterparts. Nontrivial examples for 2D polynomials having both numerical and literal coefficients are also shown to illustrate the computational advantage of the proposed method.

Estimation Technique of Fixed Sensor Errors for SDINS Calibration

Abstract: It is important to estimate and calibrate sensor errors in maintaining the performance level of SDINS. In this study, an estimation technique of fixed sensor errors for SDINS calibration is discussed. First, the fixed errors of gyros and accelerometers, excluding gyro biases are estimated by the navigation information of SDINS in multi-position. The SDINS with RLG includes flexure errors. In this study, the gyros flexures are out of consideration, but the proposed procedure selects certain positions and rotations in order to minimize the influence of flexures. Secondly, the influences of random walks, flexures and orientation errors are verified via numerical simulations. Thirdly, applying the previous estimated errors to SDINS, the estimation of gyro biases is conducted via the additional control signals of close- loop self-alignment. Lastly, the experiments illustrate that the extracted calibration parameters are available for the improvement of SDINS. In order to realize the navigation and alignment algorithms of the INS (Inertial Navigation System), the calibration of sensor errors has to be done prior to the actual flight. It is important to calibrate sensor errors in maintaining the performance of INS. Two calibration approaches can be used for the estimation of calibration parameters (1-5). The first approach employs the raw data of accelerometers and gyros. However this approach deals with the small magnitude of earth rotation rate that leads to difficulties in gyro parameter extraction, and requires precise orientation with respect to the local-level frame. Otherwise the orientation errors will affect calibration accuracy. In order to remove the above disadvantages the second calibration procedure must be employed. The second one deals with the velocity (acceleration) indications of the INS on the local-level frame. In this case, the IMU block is also turned in different angles, but this procedure deals with the INS output in the navigation mode during all calibration procedures. The orientation accuracy of the IMU block with respect to the local-level frame will not be critical, because the velocity (acceleration) indications of the INS are available following completion of system alignment. Any misalignment errors take place as unknown ones, which have to be estimated together with other parameters. Particularly, the SDINS (Strapdown INS) frequently employs this calibration procedure to improve accuracy with an inexpensive turning table. The SDINS, which is compared to the GINS (Gimbaled INS) of the same navigation accuracy, requires a more accurate calibration of sensor errors. The reason for this is because the SDINS sensors are attached to the body of the vehicle and all evaluations have direct influenced system output. This study describes the second approach for the extraction of desired calibration parameters such as in (1-5). Generally, assuming the flexure errors have first been compensated, the system will behave as if the g- sensitive misalignments are zeros. Then the estimation of remaining coefficients is accomplished by their procedures. In this study, the flexures of gyros are out of consideration, but the proposed procedure is to select 15 positions and rotations in order to minimize (separate) the influence of flexures. The advantage of the proposed procedure is that the calibration can be independently performed without the consideration of flexures. The simulations indicate the influences of random walks and flexures of gyros and orientation errors. And the estimation technique of gyro biases is introduced through the close-loop self-alignment procedure. The experiments illustrate that the calibration parameters estimated using the suggested procedures improve the performance of SDINS with RLG.

Direct Control of a Passive Haptic Device Based on Passive Force Manipulability Ellipsoid Analysis

Abstract: In displaying a virtual wall using a passive haptic device equipped with passive ac- tuators such as electric brakes, unsmooth motion frequently occurs. This undesirable behavior is attributed to time delay due to slowness in the virtual environment update and force ap- proximation due to the inability of a brake to generate torque in arbitrary directions. In this paper a new control scheme called direct control is proposed to achieve smooth display on the wall-following task with a passive haptic device. In direct control, brakes are controlled so that the normal component of a resultant force at the end-effector vanishes, based on the force analysis at the end-effector of the passive haptic device using the passive FME (Force Ma- nipulability Ellipsoid). Various experiments have been conducted to verify the validity of the direct control scheme with a 2-link passive haptic system.

Self-Organization for Multi-Agent Groups

Abstract: This paper presents a framework for the self-organization of swarm systems based on coupled nonlinear oscillators (CNOs). In this scheme, multiple agents in a swarm selforganize to flock and arrange themselves as a group using CNOs, which are able to keep a certain distance by the attractive and repulsive forces among different agents. A theoretical approach of flocking behavior by CNOs and a design guideline of CNO parameters are proposed. Finally, the formation scenario for cooperative multi-agent groups is investigated to demonstrate group behaviors such as aggregation, migration, homing and so on. The task for each group in this scenario is to perform a series of processes such as gathering into a whole group or splitting into two groups, and then to return to the base while avoiding collision with agents in different groups and maintaining the formation of each group.

Advances in Nonlinear Predictive Control : A Survey on Stability and Optimality

Abstract: Some recent advances in stability and optimality for the nonlinear receding horizon control (NRHC) or the nonlinear model predictive control (NMPC) are assessed. The NRHCs with terminal conditions are surveyed in terms of a terminal state equality constraint, a termi- nal cost, and a terminal constraint set. Other NRHCs without terminal conditions are surveyed in terms of a control Lyapunov function (CLF) and cost monotonicity. Additional approaches such as output feedback, fuzzy, and neural network are introduced. This paper excludes the results for linear receding horizon controls and concentrates only on the analytical results of NRHCs, not including applications of NRHCs. Stability and optimality are focused on rather than robustness.

A Neural Network Adaptive Controller for Autonomous Diving Control of an Autonomous Underwater Vehicle

Abstract: This paper presents a neural network adaptive controller for autonomous diving control of an autonomous underwater vehicle (AUV) using adaptive backstepping method. In general, the dynamics of underwater robotics vehicles (URVs) are highly nonlinear and the hydrodynamic coefficients of vehicles are difficult to be accurately determined a priori be- cause of variations of these coefficients with different operating conditions. In this paper, the smooth unknown dynamics of a vehicle is approximated by a neural network, and the re- maining unstructured uncertainties, such as disturbances and unmodeled dynamics, are as- sumed to be unbounded, although they still satisfy certain growth conditions characterized by 'bounding functions' composed of known functions multiplied by unknown constants. Under certain relaxed assumptions pertaining to the control gain functions, the proposed control scheme can guarantee that all the signals in the closed-loop system satisfy to be uniformly ultimately bounded (UUB). Simulation studies are included to illustrate the effectiveness of the proposed control scheme, and some practical features of the control laws are also discussed.

Necessary and Sufficient Stability Condition of Discrete State Delay Systems

Abstract: A new method to solve a Lyapunov equation for a discrete delay system is proposed. Using this method, a Lyapunov equation can be solved from a simple linear equation and N -th power of a constant matrix, where N is the state delay. Combining a Lyapunov equation and frequency domain stability, a new stability condition is proposed for a discrete state delay system whose state delay is not exactly known but only known to lie in a certain interval.

Neural Network-Based System Identification and Controller Synthesis for an Industrial Sewing Machine

Abstract: The purpose of this paper is to obtain an accurate nonlinear system model to test vari- ous control schemes for a motion control system that requires high speed, robustness and accu- racy. An industrial sewing machine equipped with a Brushless DC motor is considered. It is modeled by a neural network that is configured as an output-error dynamical system. The identi- fied model is essentially a one step ahead prediction structure in which past inputs and outputs are used to calculate the current output. Using the model, a 2 degree-of-freedom PID controller to compensate the effects of disturbance without degrading tracking performance has been de- signed. In this experiment, it is not preferable for safety reasons to tune the controller online on the actual machinery. Experimental results confirm that the model is a good approximation of sewing machine dynamics and that the proposed control methodology is effective. 1. INTRODUCTON To accurately control a system, it is beneficial to first develop a model of the system. The main objec- tive for the modeling task is to obtain a good and re- liable tool for analysis and control system develop- ment. A good model can be used in off-line controller design and implementation of new advanced control schemes. In some applications, such as in an indus- trial sewing machine, it may be time consuming or dangerous to tune controllers directly on the machin- ery. In such cases, an accurate model must be used off-line for the tuning and verification of the control- ler. While nearly all aspects of modeling and simula- tion in control systems have now reached a reason- able stage of development, the aspect which remains least satisfactory at the present time is that of repre- senting the loads supplied from systems due to the very wide range of load types. Most motion control systems driven by motors ex- hibit nonlinear behavior and are often difficult or un- realistic to model directly using laws of physics. Fric- tion is the main nonlinear element in motion control systems. In general, a linear system allows the use of more sophisticated advanced control schemes to achieve higher performance. Lai 0 identified a nonlinear model with a combination of linear dynam- ics and friction for the Virtual Reality (VR) Mouse, and used a few friction compensation strategies to linearize the VR Mouse dynamics. Turner 0 applied a creep random search based on Genetic Algorithms to simultaneously identify the linear motor parameters and the nonlinear friction parameters for a stereo