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A new iterative learning control (ILC) updating law is proposed for tracking control of continuous linear system over a finite time interval. The ILC is applied as a feedforward controller to the existing feedback controller. By using the weighted local symmetrical integral (WLSI) of feedback control signal of previous iteration, the ILC updating law takes a simple form with only two design parameters: the learning gain and the range of local integration. Convergence analysis is presented together with a design procedure. A set of experimental results are presented to illustrate the effectiveness of the proposed WLSI-ILC scheme.

A simple microprocessor controlled mini-electrochemical (EC) instrument integrating with square wave voltammetry (SWV) was designed and fabricated. Direct digital frequency synthesizer (DDFS) was exploited in the circuit design to generate a linear and very low frequency scanning waveform for SWV. An ultra-low bias current, a high performance operational amplifier, a lock-in amplifier, and several noise reduction methods, such as cable selection, PCB cleaning, and guarding, were utilized to achieve the capability of ultra-low current measurement. The initial performance test of this SWV-based EC device was also conducted in different ferricyanide solutions. Compared with commercial electrochemical analyzers, the miniaturized EC instrument presented here has the characteristics of low-cost, small size, ease of use and reduced energy consumption. Due to its flexible design, this SWV-based instrument can easily be extended to meet the requirements of the portable and reliable EC chip device integrating other commonly used EC methods.

This paper focuses on the state-periodic adaptive compensation of cogging and Coulomb friction for permanent-magnet linear motors (PMLMs) executing a task repeatedly. The cogging force is considered as a position-dependent disturbance and the Coulomb friction is non-Lipschitz at zero velocity. The key idea of our disturbance compensation method is to use past information for one trajectory period along the state axis to update the current adaptation law. The new method consists of three different steps: 1) in the first repetitive trajectory, an adaptive compensator is designed to guarantee the l/sub 2/-stability of the overall system; 2) from the second repetitive trajectory and onward, a trajectory-periodic adaptive compensator stabilizes the system; and 3) to make use of the stored past state-dependent cogging information, a search process is utilized for adapting the current cogging coefficient. We illustrate the validity of our state-periodic adaptive cogging and friction compensator by actual PMLM-model-based simulation.

High-precision operation in gantry systems is required to meet higher demands on positioning accuracy in a wide range of applications such as circuit assembly, precision metrology, and wafer stepping. In this paper, the experimental study of an observer-augmented composite control scheme for coordinated motion control of moving gantry stages for precision applications is presented. The scheme will be implemented and compared to other control schemes currently available for this purpose, including a master-slave and a set-point coordinated scheme. Experimental results will illustrate the enhanced performance of the fully coordinated control scheme with respect to tight trajectory tracking purposes.

This paper presents the design and realization of an adaptive dither to reduce the force ripple in an iron-core permanent magnet linear motor (PMLM). A composite control structure is used, consisting of three components: a simple feedforward component, a PID feedback component and an adaptive feedforward compensator (AFC). The first two components are designed based on a dominant linear model of the motor. The AFC generates a dither signal with the motivation to eliminate or suppress the inherent force ripple, thus facilitating smooth precise motion while uncompromising on the maximum force achievable. An analysis is given in the paper to show the parameter convergence. Computer simulations and real-time experimental results verify the effectiveness of the proposed scheme for high precision motion trajectory tracking using the PMLM.

In this paper, we address the problem of precision motion control of permanent-magnet linear motors (PMLMs) under the influence of significant disturbances. We establish a mathematical model of a PMLM driven by a sinusoidal pulsewidth-modulated (PWM) amplifier, obtaining it from a describing function analysis of the essentially nonlinear characteristics. The overall model (PWM+PMLM) inevitably inherits uncertainties in the face of load changes, system parameter perturbation, noise, and inherent system nonlinearities, etc., all of which constitute disturbances to the control system that will adversely affect the precision and accuracy. We propose a robust control scheme employing a disturbance observer to address the sensitivity of the control performance to the disturbances. Real-time experimental results are provided to verify and confirm the practical effectiveness of the proposed approach.

In this paper, the development of an integrated and open-architecture precision motion control system is presented. The control system is generally applicable, but it is developed with a particular focus on direct drive servo systems based on linear motors. The overall control system is comprehensive, comprising of various selected control and instrumentation components, integrated within a configuration of hardware architecture centred around a dSPACE DS1004 DSP processor board. These components include a precision composite controller (comprising of feedforward and feedback control), a disturbance observer, an adaptive notch filter, and a geometrical error compensator. The hardware architecture, software development platform, user interface, and all constituent control components will be elaborated on in the paper.

In this paper, the development of a computer controlled piezo manipulation system for biomedical applications such as intra-cytoplasmic sperm injection (ICSI) is presented. The hardware setup and the control strategies will be described in full details to illustrate the advantages of this approach as compared to other manual-based injection methods.

In this paper, with a modest amount of modeling effort, a feedback-feedforward control structure is proposed for precision motion control of a permanent magnet linear motor for applications which are inherently repetitive in terms of the motion trajectories. First, a proportional-integral-derivative (PID) feedback controller is designed using an automatic relay tuning method. An iterative learning controller based on zero-phase filtering is applied as feedforward controller to the existing relay-tuned PID feedback controller to enhance the trajectory tracking performance by utilizing the experience gained from the repeated execution of the same operations. Experimental results are presented to demonstrate the practical appeal and effectiveness of the proposed scheme.

This paper presents a robust servo control method for high precision motion control using linear actuators. The controller consists of three components : a simple feedforward compensator, a PID feedback controller, and a RBF (radial-basis function) adaptive compensator, each to fulfill a specific objective. The first two control components can be directly tuned based on only an estimated dominant second-order linear model. The RBF compensator is self-tuning, and it will compensate for remaining uncertainties in the system, residual of the linear model. Rigid proofs are provided, guaranteeing the robust stability of the proposed controller. Experimental results confirm the much superior performance of the 3-tier composite control over a standard motion controller.

This paper presents and compares possible intelligent control designs for precision motion control applications which are based on the use of linear actuators. The control schemes developed include adaptive control, compositie control using a radial-basis function for nonlinear compensation and an iterative learning control scheme. The mathematical model for a permanent magnet linear motor is first presented on which the control designs are based. Theoretical analysis as well as full experimental results are provided for the control schemes. Practical issues in the overall implementation of a precision motion control system are also highlighted.

This paper presents the application of the acceleration sensor in the enhancement of the performance of high-precision motion tracking linear actuators which are based on permanent magnet linear motors (PMLM). A feedforward–feedback control structure is developed which harness effectively the acceleration measurements made available. It utilises a linear full-state feedback controller and an iterative learning feedforward controller (ILC). Experimental results show the acceleration feedback can improve the tracking performance and learning convergence of the control system.

In this paper, a feedforward–feedback control structure is proposed for precision motion control of a permanent magnet linear motor (PMLM) for applications which are inherently repetitive in terms of the motion trajectories. The control scheme utilises an efficient marriage of conventional PID feedback control and an intelligent feedforward control using an iterative learning control (ILC) algorithm. The PID feedback control stabilizes the PMLM system, while the ILC feedforward control enhances the trajectories tracking performance by capitalising on the experience gained from the repeated execution of the same operations. A relay automatic tuning method is developed and incorporated, so that an initial set of control settings may be automatically derived from a few cycles of self-induced controlled oscillations. This self-tuning feature enables the PMLM application system to be operated quickly near optimal conditions simply at a push-button efficiency. Extensive experimental results are presented to demonstrate the appeal and effectiveness of the proposed scheme.

A high-order iterative learning controller (ILC) is proposed for the tracking control of an electrically stimulated human limb that is repeatedly required to perform a given task. The limb is actuated by the muscles, which are out of the control of the central nerve systems (CNS), through functional electrical stimulation (FES) or functional neuromuscular stimulation (FNS). By using the proposed discrete-time high-order P-type ILC updating law and the PD-type feedback controller, it is shown that the proposed control strategy, which learns from repetitions, provides strong robustness in tracking control of the uncertain time-varying FES systems, which is essential for the adaptation and customization of FES applications. The effectiveness of the proposed control scheme is demonstrated by simulation results on a one-segment planar system. Some experimental results are also presented to validate the proposed control method.

This paper presents an approach towards learning enhanced motion control of permanent magnet linear motor (PMLM), suitable for applications involving repeated iterations of motion trajectories. The overall structure of the control consists of a feedback and a feedforward components. The PD feedback control provides for the main system stabilisation and an iterative learning control (ILC) algorithm is proposed to serve as a feedforward compensation to nonlinear and unknown dynamics and disturbances, thereby enhancing the improvement achievable with PD alone. A physical model is first derived for the Anorad’s LS-B linear motor and it serves as the basis for simulation study of the proposed control scheme. A comparison of the performance achieved with traditional PID is also provided to highlight the advantages of the additional intelligent feedforward mode.

This paper presents various interesting developments in mechatronics within Asia. As a synergy of core technologies, mechatronics is fast becoming an important component of modern products and processes and is poised to become a key technology to employ to gain a competitive edge in the modern manufacturing era wherein products and processes are becoming highly integrated in functionalities. The development of mechatronics will therefore be crucial to the continued competitiveness of a manufacturing intensive economy, typical of many countries in Asia. This paper discusses various developments of mechatronics in Asia from three perspectives: education, research and applications of the technology.

The aerodynamic drag coefficient curve of spin-stabilized projectiles is very important to the fast generation of accurate firing tables. To identify it from Doppler tracking radar measured velocity data in flight tests, an iterative learning concept (ILC) is applied. High-order ILC algorithms are proposed. Convergence conditions are given in a general problem setting. A 3-DOF point mass trajectory prediction model is proposed. The learning gains, which vary with respect to both time and iteration number, have been used for a faster convergence compared to the constant learning parameter choices. Furthermore, in this paper, a bi-linear ILC scheme is proposed to produce even faster learning convergence. The flight testing data reduction results of an actual firing practice demonstrate that the iterative learning method is very effective in curve identification.

This paper provides a survey on different kinds of prediction models developed for the estimation of soil moisture content of an area, using empirical information including meteorological and remotely sensed data. The different models employed extend over a wide range of machine learning techniques starting from Basic Linear Regression models through models based on Bayesian framework, Decision tree learning and Recursive partitioning, to the modern non-linear statistical data modeling tools like Artificial Neural Networks. The fundamental mathematical backgrounds, pros and cons, prediction results and efficiencies of all the models are discussed.

In this paper, we have developed a light-weight and cost-efficient multispectral imager payload for low cost fixed wing UAVs (Unmanned Aerial Vehicles) that need no runway for takeoff and landing. The imager is band-reconfigurable, covering both visual (RGB) and near infrared (NIR) spectrum. The number of the RGB and NIR sensors is scalable, depending on the demands of specific applications. The UAV on-board microcomputer programs and controls the imager system, synchronizing each camera individually to capture airborne imagery. It also bridges the payload to the UAV system by sending and receiving message packages. The airborne imagery is time-stamped with the corresponding local and geodetic coordinates data measured by the onboard IMU (Inertia Measurement Unit) and GPS (Global Positioning System) module. Subsequently, the imagery will be orthorectified with the recorded geo-referencing data. The application of such imager system includes multispectral remote sensing, ground mapping, target recognition, etc. In this paper, we will outline the technologies, demonstrate our experimental results from actual UAV flight missions, and compare the results with our previous imager system.Copyright © 2009 by ASME

A robust higher-order PID (proportional plus integral plus derivative)-type ILC (iterative learning control) algorithm is presented for tracking control of delayed nonlinear time-varying MIMO (multiple-input multiple-output) repetitive systems. A convergence proof is given in a more general case. When initial state bias exists, a repetitive ILC scheme (i.e. forward learning and backward learning) is proposed to make the algorithm more robust with respect to this bias. Simulation results indicate that the proposed method converges faster than previous methods. The effect of system delay (or even multidelays and time-varying delays) on the ILC convergence is very small. Examples are provided to demonstrate the efficiency of the proposed methods. >

This paper focuses on the state-periodic adaptive compensation of cogging and Coulomb friction for permanent magnet linear motors (PMLM) executing a task repeatedly. The cogging force is considered as a position dependent disturbance and the considered Coulomb friction is non-Lipschitz at zero velocity. The key idea of our disturbance compensation method is to use one trajectory-period past information along the state axis to update the current adaptation law. The new method consists of three different steps: firstly, in the first repetitive trajectory, an adaptive compensator is designed to guarantee the l/sub 2/-stability of the overall system. Secondly, from the second repetitive trajectory and onwards, a trajectory-periodic adaptive compensator is designed to stabilize the system. Finally, to make use of the stored past state-dependent cogging information, a search process is utilized for adapting the current cogging coefficient. The validity of our adaptive cogging and friction compensator is illustrated through a simulation example.

This paper presents the design and realization of a feedforward dither signal to reduce the force ripple in an iron-core permanent magnet linear motor (PMLM). A composite control structure is used, consisting of three components: a simple feedforward component, a PID feedback component, and a ripple compensator (RC). The first two components are designed based on a dominant linear model of the motor. The dither signal is generated based on a signal model which is identified using a multidimensional simplex downhill method. In this way, a simple approach is available to eliminate or suppress the inherent force ripple, thus facilitating smooth precise motion while uncompromising on the maximum force achievable. Real-time experimental results verify the effectiveness of the proposed scheme for high precision motion trajectory tracking.

A multichannel, flexible, versatile electrical stimulator is important in Functional Electrical Stimulation (FES) systems. The stimulator introduced in this paper is designed and used in our laboratory-based functional neuromuscular stimulation system for the restoration of the upper or lower extremity movement of paralyzed patients. It can generate 16 independent channel complex, yet useful current pulse profiles. It is routinely used to produce rectangular, bi-phasic pulse shapes having pulsewidths in the range of 5 /spl mu/s to 1000 /spl mu/s with a resolution of 1 /spl mu/s. The pulse amplitudes can be modulated from -100 to +100 mA with a maximum resolution of 0.4 mA. The experimental applications prove that the stimulator has very good performance and is very easy to use.

The EMG data acquired during voluntary movement of the active muscles of the disabled may provide useful control commands and information in functional electrical stimulation or in artificial prosthesis provided that the raw EMG data are property processed and identified. This technique may be used by the patients to transfer commands to their paralyzed extremities or artificial limbs. Combination of autoregressive and neural network technique to identify various functional hand movements is proposed. Functional hand movements such as palmar flexion and dorsiflexion, wrist pronation and supination, wrist flexion and extension, are identified. A fourth order parametric model is employed to evaluate the set of coefficients. The coefficients are then used as input for the neural network to identify the functional movement. Experiment was done on three healthy individuals and the rate of identification is shown to be adequate to be used in the development of either neural prostheses or artificial limbs.

The high-order P-type iterative learning control (ILC) algorithm is proposed for a class of uncertain nonlinear systems performing repetitive tasks. Uniform bound of the tracking error is established as a function of uncertainty, disturbance, and initialization error. In the absence of uncertainty, disturbance, and initialization error, the tracking error approaches to zero as the number of learning iterations increases.

An iterative learning controller (ILC) is proposed for the tracking control of functional neuromuscular stimulation (FNS) system performing the given task repeatedly. A P-type ILC updating law assisted by a PD closed-loop controller is suggested for a simpler implementation. This kind of learning from repetitions of control strategy supplies strong robustness in the tracking control of uncertain time-varying FNS systems, which is essential for the adaptation and customization of FNS applications. Nonlinear muscle recruitment, linear muscle dynamics in force generation, and multiplicative nonlinear torque-angle and torque-velocity scaling factors are considered in the electrically stimulated muscle model used for the simulation studies. A one-segment planar system with passive constraints on joint movement is taken as the skeletal model. Simulation results indicate that the control scheme of this paper is promising for FNS system control.

In this paper, we address the problem relating to the precision motion control of PMLMs (permanent magnet linear motors) which are under the influence of significant disturbances. A mathematical model of a PMLM driven by a sinusoidal PWM (pulse width modulated) amplifier is first established. The PWM amplifier model used is obtained from a describing function analysis of the essentially nonlinear characteristics. The overall model (PWM+PMLM) inevitably inherits uncertainties in the face of load changes, system parameter perturbation, noise and inherent system nonlinearities etc., all constitute disturbances to the control system which will adversely affect the precision and accuracy achievable. A robust control scheme employing a disturbance observer is then proposed to address the sensitivity of the control performance to the disturbances. Real-time experimental results are provided to verify and confirm the practical effectiveness of the proposed approach.

High-order discrete-time P-type iterative learning controller (ILC) is proposed for the robust tracking control of the functional neuromuscular stimulation (FNS) systems, i.e., control of the electrical stimulation of human limb stimulation of the human limb which is no longer under voluntary control by the Central Nervous System (CNS). Control input saturation, which represents the maximum allowable stimulation pulse width (PW), is considered. A detailed musculoskeletal model is given for the simulation studies. The effectiveness of the proposed control scheme is demonstrated by simulation results. Some experimental results are presented. Finally, some of the theoretical challenges are introduced to stimulate further theoretic investigation in learning control of FNS systems.

Experiments on the transmission by Haemaphysalis punctata of three large Babesia strains were carried out. Three Babesia-free batches of laboratory reared H. punctata ticks were infected with two strains of Babesia major, B. major (Xingjiang strain), isolated with adult ticks of H. punctata and B. major (Henan strain), isolated with H. longicornis and a strain of Babesia bigemina by feeding them on the calves infected by inoculation of blood stabilates. H. punctata was shown to be capable of transmitting the B. major strains transovarially. The larvae, nymphs and adults developed from female ticks engorged on the calf infected with B. major (Xingjiang strain) transmitted the pathogen to splenectomised calves with prepatent periods of 15, 11 and 12 days, respectively. The calves infested with larvae and nymphs died of babesiosis with parasitemias of 400 and 710 per 1000 erythrocytes. The calf infested with adult ticks survived babesiosis, but the number of erythrocytes and the amountof haemoglobin were reduced greatly. H. punctata transmitted B. major (Henan strain) in the same way. The prepatent periods of the calves infested with larvae, nymph and adult ticks were 9, 10 and 12 days, respectively. Calves infested with larvae survived, but those infested with nymphal and adult ticks died of babesiosis with parasitemias of 410 and 100 per 1000 erythrocytes, respectively. H. punctata ticks did not transmit the B. bigemina strain to splenectomised calves. There were no clinical symptoms and no parasites were discovered in the blood films during a 2 month observation period after the calves were infested with larval, nymphal and adult ticks derived from female ticks engorged on calves inoculated with B. bigemina.

A high-order iterative learning controller (ILC) is proposed for the tracking control of a functional neuromuscular stimulation (FNS) system. The high-order ILC scheme may improve the transient control performance along the iteration number direction. The proposed discrete-time high-order P-type ILC updating law and the PD-type closed-loop controller provide strong robustness in tracking control of the uncertain time-varying FNS systems, which is essential for the adaptation and customization of FNS applications. Moreover, control input saturations are considered in the convergence analysis. Simulation results based on an electrically stimulated muscle model and a single joint skeletal model are presented to show the effectiveness of the control scheme proposed. A set of experimental results are briefly presented to support the simulation results.

The application of fractional controller attracts more attention in the recent years. In this paper, a new tuning method for PI α controller design is proposed for a class of unknown, stable, and minimum phase plants. We are able to design a PI α controller to ensure that the phase Bode plot is flat, i.e., the phase derivative w.r.t. the frequency is zero, at a given gain crossover frequency so that the closed-loop system is robust to gain variations and the step responses exhibit an iso-damping property. Several relay feedback tests can be used to identify the plant gain and phase at the given frequency in an iterative way. The identified plant gain and phase at the desired tangent frequency are used to estimate the derivatives of amplitude and phase of the plant with respect to frequency at the same frequency point by Bode's integral relationship. Then, these derivatives are used to design a PI α controller for slope adjustment of the Nyquist plot to achieve the robustness of the system to gain variations. No plant model is assumed during the PI α controller design. Only several relay tests are needed.

An iterative learning identification method is proposed for curve identification problems. The basic idea is to convert the curve identification problem into an optimal tracking control problem. The measured trajectories are regarded as the desired trajectories to be optimally tracked and the curve to be identified is taken as a virtual control function. A high-order learning updating law is applied. A convergence condition is obtained in a general problem setting. Two case studies, which are related to the aerodynamic drag coefficient curve extraction from actual flight testing data, are presented to show the practical usefulness of the proposed method.

In this tutorial paper, we introduce the fractional order calculus and fractional order control with possible applications in biomimetic actuator control. Since purely analog type fractional order integrator can be made with certain biomimetic materials or intelligent materials in general, it is natural to advocate the concept of "intelligent control of intelligent materials by using intelligent materials"