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Static friction compensation is usually required to reduce friction in precision motion control and manufacturing processes. Micro-motion control is a current trend for precision machining in the electronics industry. Dynamic friction plays an important role in systems using micro-scale motion at high-speed and high-frequency command. The Lund–Grenoble (LuGre) friction model is suitable for coping with the dynamic friction effect. However, the parameters of this model are difficult to accurately identify to ensure satisfactory control performance in miniature machining. This paper proposes an efficient and systematic three-step parameter estimation method for LuGre modeling. The friction compensation of the proposed approach was successfully demonstrated on a 400W AC servo motor. Furthermore, under a peck drilling command on DYNA 1007 CNC machine, the LuGre model also improved motion precision by 72.0% in the maximum peak error.

This paper presents a particle filter design to improve the accuracy of received signal strength indicator (RSSI)-based localization algorithms for localizing mobile robots that move in an environment with an 802.15.4 ZigBee wireless sensor network. In this study, the RSSI of transmission signals is adopted to estimate the location of a mobile robot by applying the trilateration algorithm. However, the interference and reflection effects of transmission signals and the orientation effects of the antenna can significantly affect the RSSI signals and result in serious localization errors. This study analyses the characteristics of RSSI signals and the orientation effects of the antenna and then develops a particle filter design with the fusion of the current states of the mobile robot to improve localization accuracy. A static localization experiment shows that the developed localization algorithm can significantly improve the localization accuracy, and a maximum rate of improvement of up to 85% is achieved in the test environment. A dynamic localization experiment demonstrates that the developed algorithm is suitable for localizing a mobile robot.

Available model-based networked control system (NCS) design approaches are basically formulated from the known system model and the information of the networked-induced delay time. Because the delay in remote control systems is significant and time-varied as commercial networks involved, available model- based NCS design approaches may thus become impractical. The increase of time delay due to the limited communication bandwidth usually induces data dropout to make NCS design even more difficult.Inthispaper,theschemeoftheperfectdelaycompensation (PDC) is proposed by introducing the butterfly-shaped inner feed- back loop in NCS to deal with the unknown network-induced time delay. Furthermore, analytical results indicate that the controller obtained in general control systems can be directly implemented on NCS with the proposed PDC scheme. Both simulation and experi- mental results were successfully carried out on an ac servo motor at a distance of 15 km through Ethernet to maintain a stable remote control system.

To achieve efficient and objective search tasks in an unknown environment, a cooperative search strategy for distributed autonomous mobile robots is developed using a behavior-based control framework with individual and group behaviors. The sensing information of each mobile robot activates the individual behaviors to facilitate autonomous search tasks to avoid obstacles. An 802.15.4 ZigBee wireless sensor network then activates the group behaviors that enable cooperative search among the mobile robots.An unknown environment is dynamically divided into several sub-areas according to the locations and sensing data of the autonomous mobile robots. The group behaviors then enable the distributed autonomous mobile robots to scatter and move in the search environment. The developed cooperative search strategy successfully reduces the search time within the test environments by 22.67% (simulation results) and 31.15% (experimental results).

Circular ring is the most popular specimen in industry. The location of a crack in a circular ring can strongly affect the sequence of plastic hinge development which in turn affects stability of a circular ring which has been obtained theoretically in Young [1] but not any numerically. Boundary element method is employed to investigate these effects. In fact, the ring will collapse if the number of plastic hinges is up to four. The results show the potential application of the boundary element method in this project.

In real applications, a remote control system is generally an integration of different networks consisting of a commercial network for message transmission and an industrial network to control the remote hardware through a communication gateway. Since the induced time-delay in network control system (NCS) may cause system instability, this paper proposes a remote NCS structure by implementing the adaptive Smith predictor with an online time-delay estimator. As the delay in a commercial network Ethernet is significantly time-varying depending on the number of end-users, the delay is estimated in this paper by processing the online measurement of the round-trip time (RTT) between the application layers of the server and the client. The adaptive Smith predictor control scheme is developed by directly applying the estimated time-delay. To prove the feasibility of the proposed remote control system, the developed design has been applied to an AC 400 W servo motor tested from a 15 km distance. The experimental results indicate that the significantly improved stability and motion accuracy can be reliably achieved by applying the proposed approach.

The computerized numerical control machine tool is a highly integrated mechatronic system in manufacturing processes. However, uncertainties degrade its motion accuracy. These include modeling errors, parameter variation, friction, and measurement errors that are present in either linear or nonlinear nature. In this paper, a state-space disturbance observer was successfully applied to servomotors to estimate and compensate for the uncertainties of parameter variation and current measurement problems, in the velocity and current loops, respectively. Furthermore, an autotuning procedure was developed accordingly to identify the varied parameters of the motor. Furthermore, by implementing the present servomotor systems in high-precision machine tools, the nonlinear friction compensation was adopted to reduce the slip-stick effect in contouring motion. Experimental results indicate that the roundness error has been significantly reduced from 13.3 to 2.0 ?m by applying the proposed approaches.

The error resources of precise motion control systems are basically categorized into linear and nonlinear effects. To realize the precise motion of industrial computer numerical control (CNC) machines, this paper presents an integrated motion control structure with modular algorithms, including both the linear control and the nonlinear compensation. In the linear control design, this study applies three algorithms: (1) feedforward control to address the tracking errors, (2) cross-coupled control to reduce the contouring errors, and (3) digital disturbance observer to lessen the effects of modeling errors and disturbances in real applications. The results indicate that the linear motion controller achieves greatly improved accuracy in both tracking and contouring by reducing the servo lags and mismatched dynamics of the different axes. However, the adverse effect due to friction still exists and cannot be eliminated by applying the linear motion controller only. This study further integrates the nonlinear compensator and develops friction estimation and compensation rules for CNC machines. The digital signal processors are suitable to implement all the developed linear and nonlinear algorithms, and the present controllers have been successfully applied to industrial CNC machines. Experimental results on a vertical machining center indicate that, under different feed rates, the CNC machine with the integrated motion controller significantly reduces the maximum contouring error by 135% on average.

Recently, several model-based control designs have been proposed for motion systems and computerized numerical control (CNC) machines to improve motion accuracy. However, in real applications, their performance is seriously degraded when significant disturbances or cutting forces are applied. In this paper, we derive straightforward design procedures for a generalstructured unknown input observer (UIO) which perfectly decouples the effect of the external disturbance from the state estimation. Furthermore, we derive the optimal UIO by minimizing the estimation errors for both the state and the disturbance via the Riccati equation. Experimental results show that the performance of all advanced motion controllers suffers when external loads are applied. By compensating for the disturbance of a servo motor using the proposed optimal-UIO, the original contouring accuracy, which is degraded by the external loading, can be successfully recovered.

Recently, modeling and simulation of hybrid dynamic systems (HDSs) have attracted much attention. However, since simultaneously dealing with the discrete and continuous variables is very difficult, most of the models result in a unified but more complicated and unnatural format. Moreover, design engineers cannot be allowed to use their preferred domain models. Based on the multiparadigm modeling concept, this correspondence proposes a Petri net framework with associated state equations to model the HDSs. In the presented approach, modeling schemes of the hybrid systems are separated but combined in a hierarchical way through specified interfaces. Designers can still work in their familiar domain-specific modeling paradigms, and the heterogeneity is hidden when composing large systems. An application to a rapid thermal process in semiconductor manufacturing is provided to demonstrate the practicability of the developed approach.

The networked control system (NCS) in industrial applications, such as computerized numerical control (CNC) machine tools and robots, possess advantages of simple wiring, expansion flexibility, and easy maintenance. However, the introduced network delay is unavoidable. Moreover, in real-time synchronized multi-axis NCS, all motion messages transmitted through the network are required to be received in time to meet specifications of the deadline. However, the data dropout may occur in NCS due to its time delay in a stochastic nature, and it also may results in degradation of motion accuracy. In this paper, a motion message estimator is proposed to construct the real-time networked industrial applications to reduce the data-dropout effect. Analytical results indicate that a 3 rd -order message estimator based on the Taylor expansion successfully suppresses the uncertainties in NCS due to the dropout effect. Furthermore, this paper also proposes the networked cross-coupled control (CCC) structure to improve the contouring accuracy of the multi-axis NCS. Results indicate that the variation of the contouring error in the multi-axis NCS is suppressed greatly by applying the proposed networked CCC. Finally, experimental results indicate the proposed motion message estimator leads to improved accuracy on an industrial CNC machine tool with the NCS implementation.

The time-delay effect in network systems is unavoidable because of its limited transmission bandwidth. In real-time network control systems (NCS), all messages are required to be transmitted or received to meet specifications of the deadline. Therefore, the data dropout may thus occur in NCS due to its time delay in a stochastic nature. In this paper, a motion message estimator is proposed to construct a real-time motion NCS to significantly reduce the data-dropout effect. Both simulation and experimental results indicate that a 3rd-order message estimator based on the Taylor expansion can be properly implemented in NCS for industrial motion control systems. Furthermore, since the uncertainties of the NCS estimator are greatly suppressed by applying the message, experimental results indicate that its integration with the feedforward control leads to significantly improved accuracy for the NCS implemented on an industrial computerized numerical control (CNC) machine tool.

Convenience stores generally consume energy higher than other retailing merchants. As the problem of energy shortage becomes more serious during summer, almost all convenience stores sign a contract with power plants, which provides for fines if demand limiting occurs in Taiwan and many other countries. Therefore, a reliable and effective method to reduce their utility consumption is required for modern business and industry. This research integrates the remote sensors, the control network, and the embedded system technologies to construct a distributed energy management control system for dedicated convenience stores. Energy consumption can thus be reasonably managed with demand limits by measuring and analyzing the power consumption sources in four major subsystems of convenience stores, namely, (1) air-conditioning, (2) lighting, (3) heating, and (4) refrigeration. By applying the proposed demand prediction and control method, the demand limiting condition can be properly predicted, and the possible peak load can thus be eliminated via the network control mechanism. Moreover, by integrating the LonWork fieldbus and the WinCE operating system (OS), the proposed system has been successfully applied to a convenience store. The experimental results indicate that the proposed distributed energy management system suitably predicts the peak loading condition and successfully prevents its occurrence by switching the air-conditioning system without affecting the indoor temperature regulation.

The linear matrix inequality (LMI) method is generally adopted to obtain the Takagi-Sugeno (TS) fuzzy control design with guaranteed system stability only. However, in real implementations, control specifications, like the damping ratio, the natural frequency or the bandwidth, are crucial to system design. In this paper, a feasible T-S fuzzy control design approach is proposed for the nonlinear motion systems with control redundancy, like in robots and satellites with input freedom to obtain the desirable position and orientation. In this paper, by applying the T-S fuzzy modeling and the inverse of the input matrix, a feasible T-S fuzzy controller is obtained to directly meet both system stability and design specifications. The proposed controller design method has been successfully applied to an omindirectional mobile robot (ODMR) system. Both simulation and experimental results indicate that the obtained system performance and stability directly meet the control design specifications. Results also indicate that the effect of the Coriolis force is effectively suppressed by the proposed approach and it leads to improved accuracy in both tracking and contouring precision especially in high-speed motions.

In a semiconductor manufacturing system, particular human operations may violate desired requirements and lead to destructive failure. For such human-in-the-loop systems, this paper proposes a supervisory framework which guarantees that manual operations meet required specifications so as to prevent human errors in operation using Petri nets. Moreover, a modular technique with an intersection mechanism is proposed in order to cope with the state-space explosion problem of large-scale systems. A rapid thermal process in semiconductor manufacturing systems is provided to show the practicability of the proposed approach. Note to Practitioners - This work was motivated by the requirement of remote monitoring and supervision of semiconductor manufacturing systems. In such human-in-the-loop and large-scale systems, certain human operations may violate desired safety requirements and result in catastrophic failure. Moreover, the overall complexity of most existing Petri net modeling and analysis approaches significantly increases with the size of the considered systems. This paper suggests a modular supervisory scheme for modeling and synthesis of supervisory agents using Petri nets. The proposed method contributes a promising tool for preventing abnormal operations from being carried out to semiconductor manufacturing systems which, if appropriately modified, may be also applied to other types of discrete event systems.

For remote control systems, certain human operations may violate desired requirements and result in catastrophic failure. For such human-in-the-loop systems, this paper implements a hierarchical supervision system to guarantee that remote human-issued commands meet required specifications. In the presented scheme, Petri nets are applied to model, design, and verify both the local controller and the remote supervisor. Then, the agent technology is adopted to implement the supervisor as an intelligent agent for an online supervision of the remote control system. Hence, undesired resource conflicts and deadlock states can be avoided. An application to a flexible manufacturing system controlled over the Internet is provided to illustrate the developed approach. Implementation results show that by applying the presented hierarchical scheme, the supervisor has a more compact model with fewer states. Moreover, fewer request/response transmissions are consumed so that the effects of time delays and packet losses across the Internet could be moderated

In industrial applications servo motors are applied to different loads and varied control performance is thus unavoidable. Under such circumstances, engineers usually re-design or re-tune controller parameters to meet certain design specifications without reliable and consistent control performance or motion precision. In robust controller design, the quantitative feedback theory (QFT) is adopted by applying both the loop-shaping design and the feedforward controller in its feedback loop with a suitable order determined from experience. On the other hand, H ∞ controller is designed in frequency domain but is directly calculated in time domain with the controller type without restriction in selecting its order. However, H ∞ controller only deals with sensitivity without meeting control performance as in QFT design. In this study, the combined approach by using both the H ∞ algorithm and the QFT controller with better robustness is applied to the servo motor control design; in addition, the QFT feedforward controller in loop-shaping is included to lead to desirable control performance. Experimental results show that by applying the present H ∞ /QFT approach, the similar system responses under different external loading on the CNC servo motor can be successfully obtained to prove the feasibility of the present method in industrial automation.

To pursue high-speed motion control for robots, applications of servo motors with appropriate controllers may render fast responses with satisfactory precision. However, the current coupling effect on the servo motor control unavoidably downgrades stability and contouring precision as the operation speed increases in practice. This paper proposes a control structure simply by adding a decoupling gain Kc to significantly improve stability of motor control systems under high-speed operations. Simulation results on the joints of a Delta robot indicate that the present decoupling control structure effectively leads to satisfactory performance and precision under high-speed operations.

In a distributed robotic system, both human-controlled (semi-autonomous) and computer-controlled (fully autonomous) robots may simultaneously exist. From the global system's point of view, supervisory control for the interactions between the human and computer are important and necessary. For such human-computer interactive systems, this paper proposes a supervisory framework to guarantee that both human and computer commands meet collision-free and deadlock-free requirements. In the presented approach, Petri nets are applied to construct a system model and synthesize a desired supervisor. An application to a two-robot remote surveillance system is provided to demonstrate the practicability of the developed supervisory control approach. It is believed that the technique developed in this paper is significant in the industrial practice.

This paper presents a systematic approach for the design and implementation of the sequence controller in manufacturing systems. By employing the IDEF0, we construct the simplified Petri net controller (SPNC) through the material flow diagram and the information flow diagram. Then, the ladder logic diagram (LLD) can be transformed from the SPNC through the token passing logic (TPL). The proposed approach, including the IDEF0, SPNC, and TPL tools, leads to the standard IEC1131-3 LLD for PLC implementation. Finally, an application of a stamping process is provided to illustrate the design procedure of the developed approach .

For motion systems with multiple axes, the approach of matched direct current gains has been generally adopted to improve contouring accuracy under low-speed operations. To achieve high-speed and high-precision motion in modern manufacturing, a perfectly matched feedback control (PMFBC) design for multiaxis motion systems is proposed in this paper. By applying stable pole-zero cancellation and including complementary zeros for uncancelled zeros for all axes, matched dynamic responses across the whole frequency range for all axes are achieved. Thus, contouring accuracy for multiaxis systems is guaranteed for the basic feedback loops. In real applications, the modeling error is unavoidable and the degradation and limitations of the model-based PMFBC exist. Therefore, a newly designed digital disturbance observer is proposed to be included in the proposed PMFBC structure for each axis to compensate for undesirable nonlinearity and disturbances to maintain the matched dynamics among all axes for the PMFBC design. Furthermore, the feedforward control loops zero phase error tracking controller are employed to reduce tracking errors. Experimental results on a three-axis CNC machining center indicate that both contouring accuracy and tracking accuracy are achieved by applying the present PMFBC design. @DOI: 10.1115/1.1789970#

For large-scale and long-distance distributed systems, this paper proposes a systematical multiparadigm approach to develop the simple network management protocol (SNMP) agents for remote monitoring and control. The standard unified modeling language (UML) is adopted for modeling the system, and then the Petri-net model is applied to achieve both qualitative and quantitative analyses for the system's dynamic behavior. In real applications, the present design can be further implemented with Java and ladder diagrams on programmable logic controllers (PLC). The developed system has been used successfully in a mobile switching center (MSC) of Taiwan Cellular Corporation for the remote monitoring and control, through the Internet, of its environmental conditions, including the temperature, humidity, power, and security, with a total of 316 sensors and 140 actuators.

In modern manufacturing, the design of multi-axis motion controllers for high-speed/high-precision and complex machining is becoming more critical. In the past, controllers for each axis were, in general, designed individually to obtain desirable tracking accuracy. Although some advanced motion control algorithms have been developed recently, they are mostly applicable to single-axis or biaxial systems. In particular, the design of two-axis integrated control, which includes: 1) feedback loops; 2) feedforward loops; and 3) the cross-coupled control (CCC), achieves significantly improved performance. However, its applications are limited to basic linear and circular contour commands for two-axis systems only. In this paper, system analysis for the multi-axis integrated control structure is proposed in order to achieve tracking and contouring accuracy simultaneously. With the derived contouring error transfer function (CETF), the integrated control design for multi-axis motion systems can be formulated simply as a single-input-single-output (SISO) design. Thus, a robust design with desirable performance for multi-axis motion systems can then be achieved straightforwardly. Moreover, by estimating the contouring error vector, the proposed multi-axis integrated control can be widely applied to general contour commands. Experimental results on a three-axis CNC machining center indicate that both tracking and contouring accuracy are simultaneously improved by applying the proposed control design.

In biaxial motion systems, applying the cross-coupled control (CCC) significantly improves contouring accuracy for linear and circular contours. As geometrical and parametric curves become more popular in modern manufacturing, machining processes with multiaxis motion systems are required, however, the available biaxial CCC cannot be directly applied to arbitrary contours with multiaxis machining systems. In this paper, we propose a novel approach for arbitrary contours by estimating the contouring error vector to efficiently determine the variable gains for CCC. Experimental results for a biaxial motion system indicate that the proposed approach efficiently yields variable gains similar to those in traditional CCC. Furthermore, results on a three-axis CNC machining center show that the present approach significantly improves motion accuracy in multiaxis motion systems.

Recently, modern manufacturing systems have been designed which can machine arbitrary parametric curves while greatly reducing data communication between CAD/CAM and CNC systems. However, a constant feedrate and chord accuracy between two interpolated points along parametric curves are generally difficult to achieve due to the non-uniform map between curves and parameters. A speed-controlled interpolation algorithm with an adaptive feedrate is proposed in this paper. Since the chord error in interpolation depends on the curve speed and the radius of curvature, the feedrate in the proposed algorithm is automatically adjusted so that a specified limit on the chord error is met. Both simulation and experimental results for non-uniform rational B-spline (NURBS) examples are provided to verify the feasibility and precision of the proposed interpolation algorithm.

Micro-motion control on the peak drilling, particularly in 3C industry processing, is difficult for the spindle to precisely follow the high-frequency commands, mainly because the repetitive motion distance is very small and it passes through zero velocity frequently to cause serious problems of friction. Experimental results indicate that the axis is nearly unmoved by applying traditional controllers in practice. By applying the static friction compensation constructed under constant-speed operations, results indicate that it is suitable only when the motion command is slowly-varying in a low frequency range. In this paper, the LuGre model is adopted and a systematic approach is proposed to obtain corresponding modeling coefficients. Results indicate that by applying the controller with the LuGre model, the bandwidth of the velocity loop is greatly improved from 394Hz to 663.7Hz, and the position loop is greatly improved from 9.5 Hz to 113.8 Hz. The LuGre model has also been realized on a DYNA 1007 CNC machine to prove its feasibility. Moreover, the tracking error of its peck drilling is improved 67.5% in RMS and the maximum peak error improvement is 72.0%.Copyright © 2014 by ASME

As messages and nodes over a network increase, delay time in a networked control system (NCS) increases and results in frequent dropout, thereby seriously degrading the performance and stability of NCS. Therefore, effective methods are urgently needed to handle the time delay and dropout effects of NCS. In this paper, a real-time transition probability estimator for the two-state Markov chain model is proposed for online monitoring of the dropout condition by using a short-window technique. Furthermore, under time-varied dropout conditions, the most suitable least-square estimator can be determined in real time by applying intelligent message estimator (IME), and missing data from NCS can be efficiently estimated and restored.

The quadratic constraint equation error (QCEE) method was proposed by Ho and Chan (see ibid., vol.43, no.1, p.51-62, 1995) to achieve bias removal for the equation error adaptive IIR filters. However, this approach is limited to slow convergence in estimation when a small step size is used; large step sizes lead to significant misadjustment. This article presents a variable step size (VSS) technique to greatly improve the QCEE under its quadratic constraint to achieve both fast convergence and reduced misadjustment for adaptive IIR filters.

The cross-coupled control (CCC) has been recognized as an efficient motion controller that reduces contouring errors, but theoretical analysis of it is lacking, and there is no systematic design approach for obtaining a CCC system with guaranteed control performance. Consequently, the compensators C in CCC are commonly implemented in a PID structure and their contouring accuracy is usually degraded in real applications under different operating conditions. In an attempt to overcome the CCC design limitations described above, this paper introduces a robust CCC design based on a novel formulation: the contouring error transfer function (CETF), leading to an equivalent formulation as in the feedback control design problem. Then, methods in robust control design can be readily employed to achieve robust CCC with specified stability margins and guaranteed contouring performance. Furthermore, the proposed design has been verified as being internally stable. All provided experimental results indicate that the proposed robust CCC design consistently renders satisfactory contouring accuracy under different operating conditions.

Modern CNC systems are designed with the function of machining arbitrary parametric curves to save massive data communication between CAD/CAM and CNC systems and improve their machining quality. Although available CNC interpolators for parametric curves generally achieve contouring position accuracy, the specified feedrate, which dominates the quality of the machining processes, is not guaranteed during motion. Recently, some approximation results concerning motion speed have been reported [Shpitalni M, Koren Y, Lo CC. Computer-Aided Design 1994;26(11):832–838; Bedi S, Ali I, Quan N. Trans ASME J Engng Industry 1993;115:329–336; Chou JJ, Yang DCH. Trans. ASME J Engng Industry 1991;113:305–310; Chou JJ, Yang DCH. Trans ASME J Engng Industry 1992;114:15–22]. In this paper, by deriving a compensatory parameter, the proposed interpolation algorithm has significantly improved curve speed accuracy. In real applications, the proposed algorithm results in: (1) constant speed; and (2) specified acceleration and deceleration (ACC/DEC) to meet the feedrate commands. The motion accuracy and feasibility of the present interpolator have been verified with a provided non-uniform rational B-spline (NURBS) example.

This paper presents a real-time intelligent supervision system for IC ion implantation processes. A hardware interface is developed, to extract the features directly from the 2-D analog image signals of a beam map. A qualitative model for the beam scanning is then obtained, and the symptoms of abnormal operations can be analyzed to achieve on-line diagnosis. Furthermore, a fuzzy expert system is developed to advise operators on making appropriate adjustments for the beam scanning. This supervisory system has been implemented on Eaton NV-6200 A/AV ion implanters at the Taiwan Semiconductor Manufacturing Company.

The zero phase error tracking controller (ZPETC) in motion control, as proposed by Tomizuka (1987) , renders the desirable zero phase error, but with a limited gain response. Moreover, a ZPETC, which is basically in a feedforward control structure, is very sensitive to modeling error. To improve the tracking accuracy of the ZPETC, this paper presents an optimal ZPETC design with a concise polynomial digital prefilter (DPF). The parameters of this well-designed DPF are obtained through the derived L/sub 2/-norm optimization. By cascading the developed DPF to the ZPETC, the resultant optimal ZPETC greatly improves the bandwidth of the tracking control systems while maintaining the zero phase error. Compared with other optimal approaches, the present design leads to much simpler procedures and fewer computations. Furthermore, the proposed optimal ZPETC can be adequately implemented as an adaptive ZPETC by including real-time estimation technique to cope with the external load perturbation and parameter variation. Compared with the other adaptive approaches, the optimal concept is used in the present adaptive ZPETC, and it also renders more accurate results because of its improved magnitude response. Experimental results on a DC servo table with different controllers indicate that when there is no loading, the present optimal ZPETC achieves the best tracking performance. Moreover, the adaptive ZPETC achieves the most satisfactory results when an external load is applied.

Recently, feedforward controllers like zero phase error tracking controllers (ZPETC) and cross-coupled controllers (CCC) have been developed to effectively reduce tracking error and contouring error, respectively. This paper proposes an integrated controller which combines ZPETC and CCC to achieve both tracking and contouring accuracy. Furthermore, studies indicate that ZPETC and CCC can be designed separately in the present integrated control design. In the provided experimental setup with a servo table, an optimal ZPETC and a robust CCC based on the contouring error transfer function (CETF) were designed to achieve desirable frequency responses and stability. Experimental results show that the proposed integrated controller renders significantly improved accuracy in both tracking and contouring.

The robust diagnostics design has gained more attentions recently in real applications. It is known that the freedom in eigenstructure assignment leads to the well-conditioned design which is robust to unstructured uncertainties (Shen et al., 1995, 1998). Moreover, the unknown input fault detection observer (UIFDO) decouples the known inputs, the structured uncertainties, from the diagnostic residuals. In this paper, we present an unknown input fault isolation observer (UIFIO) based on the UIFDO by using the eigenstructure assignment. It achieves robust fault isolation subject to both structured and unstructured uncertainties. Moreover, the optimal gain matrix of the present diagnostic observer is obtained to improve its robustness without degrading diagnostic performance. Experimental results of an automotive engine show the validity and applicability of the proposed robust UIFIO design.

By using the unknown input decoupling technique, this paper presents a robust fault detection and isolation (FDI) system design for a supersonic wind tunnel. A decoupled fault/residual map is designed in the sensor FDI system to achieve multiple fault isolation for principal sensors. Since the relations between actuator faults and the generated residuals are insignificant, a decoupled fault/residual map for actuator is not achievable. Nevertheless, based on the fault specific directions, single fault isolation can still be achieved for actuators with the proposed FDI system.

A numerical implementation is presented for the eigenstructure assignment approach to the design of a robust fault detection and isolation observer. By formulating the problem of multiple fault isolation in an eigenstructure, wedevelopaparametriccharacterizationofallallowableeigenspaces,whichprovidesadiagnosticobservercapable ofmultiplefaultisolation.Inaddition,awell-conditioneddesignoftheallowableeigenvectorsisshowntoreducethe effectofthesystem unstructured uncertainty.Theresulting diagnosticobserverisrobustto unstructuredmodeling errorswithoutlosingfaultdetectionsensitivity.Aprocedureforthesynthesisofareduced-orderdiagnosticobserver is also presented to increase the computational efe ciency of the proposed observer.

This paper presents a real-time intelligent supervision system for IC ion implantation processes. A hardware interface is developed to directly extract the features from the 2-D analog image signals of a beam map. A qualitative model for the beam scanning is then obtained and the symptoms of abnormal operations can be analyzed to achieve on-line diagnosis. Furthermore, a fuzzy expert system is developed to advise operators taking appropriate adjustment for the beam scanning. This supervisory system has been implemented on Eaton NV-6200 A/AV ion implanters at the Taiwan Semiconductor Manufacturing Company.

This paper proposes a robust proportional−integral (PI) controller design for multivariable complex processes to achieve both well-decoupled and well-damped output behavior. By reformulating PI-controlled processes in a way similar to the LQG/LTR control design problem, the proposed model matching technique in the first design stage successfully results in the robust PI controller with well-decoupled output behavior and sufficient stability. Moreover, control performance can be further improved to achieve well-damped responses by applying gain modification in a second design stage. Both simulated results using chemical processes and experimental results using a wind tunnel have proven the feasibility of the proposed PI controller design in real applications.

Using the framework of the general structured (GS) observer, we present a straightforward procedure for designing an unknown input observer (UIO) for a linear system subject to unknown inputs or uncertain disturbances. The set of all GS observers insensitive to unknown inputs is derived in this paper. Moreover, an extension of the UIO, called the extended UIO, is developed to estimate both the system state and the unknown input simultaneously. We show the existence conditions of a stable UIO are the same as those of a stable left inverse system. In addition, well-conditioned designs for both the state and unknown input estimations are also explored. Conditions of transmission zeros reveal that to achieve a stable UIO, the uncertain system should be minimum-phase. To overcome this restriction, we adopt a two-delay output stabilized method to design the stabilized UIO without implementing extra sensors. Experimental results for a DC servo motor system demonstrate the applicability of the proposed methodologies.

Based on a development for the unknown input observer (VIO) design, this paper presents a straightforward method of designing a robust fault detection and isolation (FDI) system for uncertain systems with unknown inputs. By exploiting the properties of a nominal model structure, we form a basic set of residuals which is independent of the unknown input but is dependent on the measurements. Then, with this basic residual set, we append a dynamic weighting matrix to the residuals to increase the design freedom for meeting the required detection and isolation performance. Additionally, a generalized residual generator scheme (GRGS) is proposed to synthesize the parity relations for the isolation of faulty sensors in an uncertain system. The proposed strategy is verified through simulation studies performed on the control of a vertical take-off and landing (VTOL) aircraft in the vertical plane.

When machining conditions change significantly, applying parameter-adaptive control to the cutting system by varying the table feedrate allows a constant cutting force to be maintained. Although several controller schemes have been proposed, their cutting control performance is limited especially when the cutting conditions vary significantly. This paper presents an adaptive fuzzy logic control (FLC) developed for cutting processes under various cutting conditions. The controller adopts on-line scaling factors for cases with varied cutting parameters. In addition, a reliable self-learning (SL) algorithm is proposed to achieve even better cutting performance by modifying the adaptive FLC rule base according to properly weighted performance measurements. Both simulation and experimental results show that given a sufficient number of learning cases, the adaptive SL-FLC is effective for a wide range of applications. The successful implementation of the proposed adaptive SL-FLC algorithm on an industrial heavy-duty machining center indicates that the proposed adaptive SL-FLC is feasible for use in manufacturing industries.

Enhancement of unknown input fault detection observer (UIFDO) to obtain unknown input fault isolation observer (UIFIO) is presented in this paper by eigenstructure assignment approach. Not only the unknown inputs are concerned but also the effects due to unstructured uncertainties is adopted and suppressed with well-conditioned eigenstructure assignment. Experimental results of a Nissan commercial engine is illustrated to prove the validity of theoretical derivation.

Unlike cases where only a single failure occurs, fault detection and isolation of multiple sensor and actuator failures for engines are difficult to achieve because of the interactive effects of the failed components. If faults all appear either in sensors only or in actuators only, many existing residual generators which provide decoupled residual signals can be employed directly to obtain proper fault detection and isolation. However, when both sensor and actuator failures occur at the same time, their mutual effects on residuals make fault isolation particularly difficult. Under such circumstances, further decision logic is required. In the paper, the authors propose a hexadecimal decision table to relate all possible failure patterns to the residual code. The residual code is obtained through simple threshold testing of the residuals, which are the output of a general scheme of residual generators. The proposed diagnostic system incorporating the hexadecimal decision table has been successfully applied to automotive engine sensors and actuators in both simulation and experimental analyses. Enhancement of the present diagnostic performance by implementing an additional sensor is also described. >

The indirect rotor field-oriented control with the optimal slip factor adjustment is exploited for the a 0.75kW induction machine (IM) control and it has been successfully implemented on an electric vehicle (EV) to provide satisfactory acceleration and efficiency. To achieve dynamic control of the flux and torque, precise synchronous angle estimation based on the rotor time constant is crucial for stator current decoupling, and thus control performance is highly dependent on the accuracy of parameter identification. Since traditional identification approaches are suffered from difficulty of implementation, a simple and accurate identification process by applying experiments to obtain the rotor time constant corresponding to the maximum acceleration is proposed in this paper. As a low-power IM implemented on EVs is usually overloaded during the acceleration period, the maximum torque per amperage (MTPA) control is thus desirable. A novel control approach by which the optimal slip factor is obtained through experiments for the maximum acceleration and efficient is proposed. Experimental results show that the proposed slip factor adjustment is more suitable to traction control of the EV because of its smooth operating performance.

In this paper, linear and nonlinear control design techniques including: (1) cross-couple control, (2) nonlinear friction compensation, and (3) disturbance observer are suitably integrated to improve control performance in synchronization. The developed controller has been applied to a two-axis motion system with an induction motor and a servo motor, in which they have very different dynamic characteristics. Experimental results indicate that the maximum synchronized error is significantly reduced from 132.1 um to 4.3 um. Moreover, the positional type cross-coupled controller has been realized on a commercial CNC tapping machine to improve the maximum synchronized error. Results of the tapping process have led to synchronization accuracy under 10 um when the spindle speed increases to the CNC maximum speed 6000 rpm to maintain satisfactory tapping processes.

Most successful applications of parameter adaptive control to obtain constant cutting forces in machining processes have been limited to laboratory environments. This paper presents a methodology for realizing efficient adaptive control algoritms in industrial CNC machines. Experimental results concerning the fixed gain integral control and variable gain MRAC show that the present set-up in a heavy-duty CNC machining center leads to predictable control behavior. Furthermore, two effective algorithms—a deadbeat self-tuning control with increased order and a PID-type self-tuning control based on the pole-placement method—are implemented to obtain more effective cutting control performance. In this study, repeatable and satisfactory experimental results show that the technique developed here for real manufacturing processes is feasible.

This paper describes the implementation of an integrated control/diagnostic system for an advanced hard disk drive (HDD) servo system. An observer-based state feedback control design with the pole placement technique has been successfully applied to achieve a bandwidth of 688 Hz, which is quite satisfactory at a 4.5 kHz sampling frequency. Experimental results indicate, however, that the performance of this high precision, high-order control system deteriorates seriously when faults occur. Therefore, a fault identification filter (FIDF) was adopted for fault detection of sensor and actuator failures and accommodation of the actuator failure. In cases where either sensor or actuator fault occurs, experimental results indicate that to maintain high precision and control performance for the HDD system, DSP implementation of the high-order integrated control/diagnostic system is necessary. >

With the rapid development and implementation of Internet technologies, well-designed/tuned controllers obtained from control design without network are desired, which can be further realized in remote control systems directly. Theoretically, available networked control system (NCS) designs are developed with a known system model and time delay. However, the nature of network-induced delay is unpredictable, which depends on network protocol and hardware, and available design approaches usually become impractical in real concerns. The study presents an NCS structure with the proposed perfect delay compensation (PDC) scheme, which is constructed with modified butterfly elements to deal with unknown delay effects without system model and delay information. Both analytical and simulation results prove that the proposed model-free PDC scheme effectively compensates for the unknown network-induced delays. Furthermore, the proposed PDC is carried out using Ethernet, and a well-tuned PI controller for the AC servo motor was directly networked to the remote control system. Experimental results indicate that the proposed PDC renders satisfactory and desirable design results in real NCS applications.

The paper presents an efficient realtime simulator for 3-axis milling processes. In this system, solid modeling is performed using isometric projection with a z-map structure. As a result, all the solid elements are either fully exposed or fully hidden, and they are well arranged and symmetrical. These characteristics make visible surface determination for the raster display more efficient, and they reduce the memory requirements considerably. As a result, this efficient and realtime voxel-based model can simulate NC milling processes satisfactorily even in personal computers, without the aid of extra hardware devices or coprocessors. This simulation system will be useful to NC programmers and machining operators for identifying programs and checking gross programming errors visually.