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ABSTRACT: A digital signal processor-based cross-coupled functional link (FL) radial basis function network (FLRBFN) control is proposed in this study for the synchronous control of a dual linear motors servo system that is installed in a gantry position stage. The dual linear motors servo system comprises two parallel permanent magnet linear synchronous motors (PMLSMs). First, the dynamics of the field-oriented control PMLSM servo drive with a lumped uncertainty, which contains parameter variations, external disturbance and friction force, is introduced. Then, to achieve accurate trajectory tracking performance with robustness, an intelligent control approach using FLRBFN is proposed for the field-oriented control PMLSM servo drive system. The proposed FLRBFN is a radial basis function network embedded with a FL neural network. Moreover, to guarantee the convergence of the FLRBFN, a discrete-type Lyapunov function is provided to determine the varied learning rates of the FLRBFN. In addition, since a cross-coupled technology is incorporated into the proposed intelligent control scheme for the gantry position stage, both the position tracking errors and synchronous errors of dual linear motors will converge to zero, simultaneously. Finally, some experimental results are illustrated to depict the validity of the proposed control approach.
IET Control Theory and Applications 04/2011; · 0.99 Impact Factor
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ABSTRACT: A field-programmable gate array (FPGA)-based intelligent dynamic sliding-mode control (IDSMC) using recurrent wavelet neural network (RWNN) estimator is proposed to control the mover position of a linear ultrasonic motor (LUSM) in this study. First, the structure and operating principles of the LUSM are introduced briefly. Then, the dynamics of LUSM mechanism with the introduction of a lumped uncertainty, which include the friction force, is derived. Since the dynamic characteristics and motor parameters of the LUSM are non-linear and time-varying, an IDSMC using RWNN estimator is designed to achieve robust control performance of the LUSM drive system. The RWNN estimator is employed to estimate the non-linear functions including the system parameters and external disturbance. Moreover, the adaptive learning algorithm trained the parameters of the RWNN online is derived using the Lyapunov stability theorem. Furthermore, an FPGA chip is adopted to implement the developed control and on-line learning algorithms for possible low-cost and high-performance industrial applications. The experimental results show that excellent positioning and tracking performance are achieved. In addition, the robustness to parameter variations and friction force can be obtained as well using the proposed control system.
IET Control Theory and Applications 10/2010; · 0.99 Impact Factor
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ABSTRACT: This study aimed to investigate the outcome in patients with aspiration pneumonia during definitive concurrent chemoradiotherapy for head and neck cancer. The data of 595 patients with head and neck cancer treated by chemoradiotherapy were reviewed. Forty-one patients were identified as developing symptomatic aspiration pneumonia during treatment and were analysed for this study. The definition of symptomatic aspiration pneumonia fit three criteria: (1) at least one event of aspiration during the treatment or evidence of grade 2 or above dysphagia during treatment; (2) clinical or radiographic signs of pneumonia or pneumonitis; and (3) no evidence of grade 4 haematological toxicity before the outbreak of pneumonia. Termination of allocated radiotherapy was noted in 10 patients. A treatment break was observed in 26 patients, whereas irradiation was prolonged more than 1 week in 11 patients. Logistic regression analysis showed the dysphagia score during the treatment course and the chest roentgenography pattern following symptomatic aspiration pneumonia were found to independently influence the outcome. Aspiration pneumonia occurring during chemoradiotherapy for head and neck cancer has a detrimental effect on the treatment outcome. Intensive medical care is essential for this group of patients with a dysphagia score of 3 during treatment and an unfavourable chest film pattern.
European Journal of Cancer Care 09/2010; 19(5):631-5. · 1.17 Impact Factor
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ABSTRACT: A field-programmable gate array (FPGA)-based functional link radial basis function network (FLRBFN) control is proposed in this study to control the mover of a permanent magnet linear synchronous motor (PMLSM) servo drive system to track periodic reference trajectories. First, the dynamics of the field-oriented control PMLSM servo drive with a lumped uncertainty, which contains parameter variations, external disturbances and non-linear friction force, is derived. Then, to achieve accurate trajectory tracking performance with robustness, an intelligent control approach using FLRBFN is proposed for the field-oriented control PMLSM servo drive system. The proposed FLRBFN is a radial basis function network (RBFN) embedded with a functional link neural network (FLNN). Moreover, the on-line learning algorithm of the FLRBFN, including the connective weights, the centres and the centres' width of the receptive field functions, are derived using back-propagation (BP) method. Furthermore, an FPGA chip is adopted to implement the developed control and on-line learning algorithms for possible low-cost and high-performance industrial applications using PMLSM. Finally, the effectiveness of the proposed control scheme and the robustness to parameter variations, external disturbances and friction force of the PMLSM servo drive system are verified by some experimental results.
IET Electric Power Applications 06/2010; · 1.17 Impact Factor
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ABSTRACT: A novel recurrent wavelet-based Elman neural network (RWENN) control system is proposed in this study to control the mover position of a multi-axis motion control stage using linear ultrasonic motors (LUSMs) for the tracking of various contours. First, the structure and operating principles of the LUSMs are introduced briefly. Since the dynamic characteristics and motor parameters of the LUSMs are non-linear and time varying, the RWENN is proposed to control the mover of the X - Y -- motion control stage to track various contours precisely using a direct decentralised control strategy. In the proposed RWENN, each hidden neuron employs a different wavelet function as an activation function. Moreover, the recurrent connective weights are added in the RWENN. Therefore compared with the conventional Elman neural network (ENN), both the precision and time of convergence are improved. Furthermore, the on-line learning algorithm based on the supervised gradient descent method and the convergence analysis of the tracking error using a discrete-type Lyapunov function of the RWENN are developed. Finally, some experimental results of various contours tracking show that the tracking performance of the RWENN is significantly improved compared with the ENN.
IET Electric Power Applications 06/2010; · 1.17 Impact Factor
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ABSTRACT: In this study, a filtering-type sliding-mode control with a radial basis function network (FSCRBFN) for a two-axis motion control system, which consists of two permanent magnet linear synchronous motors (PMLSMs), is proposed. First, the dynamics of the single-axis motion system with a lumped uncertainty which contains parameter variations, external disturbances, cross-coupled interference and non-linear friction force is derived. Next, a filtering-type sliding-mode control (FSC) is adopted for the two-axis motion control system to confront the lumped uncertainty. Then, to improve the control performance in contour tracking, the FSCRBFN control approach is developed. In the control approach, a radial basis function network (RBFN) is employed mainly to estimate the lumped uncertainty. Moreover, the proposed control approach is performed on a digital signal process (DSP)-based control system using TMS320C32. Finally, some experimental results are illustrated to show the validity of the proposed control approach.
IET Control Theory and Applications 05/2010; · 0.99 Impact Factor
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ABSTRACT: A field-programmable gate array (FPGA)-based recurrent wavelet neural network (RWNN) control system is proposed to control the mover position of a linear ultrasonic motor (LUSM). First, the structure and operating principles of the LUSM are introduced. Since the dynamic characteristics and motor parameters of the LUSM are non-linear and time-varying, an RWNN controller is designed to improve the control performance for the precision tracking of various reference trajectories. The network structure and its on-line learning algorithm using delta adaptation law of the RWNN are described in detail. Moreover, the connective weights, translations and dilations of the RWNN are trained on-line. Furthermore, to guarantee the convergence of the tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RWNN. In addition, an FPGA chip is adopted to implement the developed control algorithm for possible low-cost and high-performance industrial applications. Finally, the effectiveness of the proposed control system is verified by some experimental results.
IET Electric Power Applications 08/2009; · 1.17 Impact Factor
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ABSTRACT: A recurrent wavelet neural network (RWNN) controller with improved particle swarm optimisation (IPSO) is proposed to control a three-phase induction generator (IG) system for stand-alone power application. First, the indirect field-oriented mechanism is implemented for the control of the IG. Then, an AC/DC power converter and a DC/AC power inverter are developed to convert the electric power generated by a three-phase IG from variable frequency and variable voltage to constant frequency and constant voltage. Moreover, two online trained RWNNs using backpropagation learning algorithm are introduced as the regulating controllers for both the DC-link voltage of the AC/DC power converter and the AC line voltage of the DC/AC power inverter. Furthermore, an IPSO is adopted to adjust the learning rates to further improve the online learning capability of the RWNN. Finally, some experimental results are provided to demonstrate the effectiveness of the proposed IG system.
IET Electric Power Applications 04/2009; · 1.17 Impact Factor
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ABSTRACT: A field-programmable gate array (FPGA)-based adaptive backstepping control system with radial basis function network (RBFN) observer is proposed to control the mover position of a linear induction motor (LIM). First, the indirect field-oriented mechanism is adopted for controlling the LIM. Next, a backstepping control law is designed step by step for the tracking control of periodic reference trajectories, in which the uncertainties are lumped by a conservative constant. However, the lumped uncertainty is unknown and difficult to obtain in advance in practical applications. Therefore an RBFN is derived to observe the lumped uncertainty in real-time, and an adaptive backstepping control system with RBFN observer is resulted. Then, an FPGA chip is adopted to implement the indirect field-oriented mechanism and the developed control algorithms for possible low-cost, high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some simulated and experimental results. By using the adaptive backstepping control system with RBFN observer, the FPGA-based LIM drive possesses the advantages of good transient control performance and robustness to uncertainties in the tracking of periodic reference trajectories.
IET Electric Power Applications 12/2008; · 1.17 Impact Factor
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ABSTRACT: To investigate prognostic factors for survival and locoregional control in patients with stage I-IVA hypopharyngeal cancer treated with laryngeal preservation radiotherapy.
This study was a retrospective analysis of 108 patients with stage I-IVA squamous cell carcinoma of the hypopharynx, treated with laryngeal preservation radiotherapy. Actuarial survival, disease-specific survival and local relapse-free survival were calculated, and multivariate analyses were performed using Cox's proportional hazards model.
After a median follow-up duration of 39 months, the five-year local relapse-free survival rate was 35 per cent for all patients, 66 per cent for those with stage I-II disease, 46 per cent for those with stage III disease and 20 per cent for those with stage IVA disease (p = 0.004). Multivariate analyses showed that tumour and node stages were independent prognostic factors.
Patients with stage I-II disease were suitable for laryngeal preservation radiotherapy. For most patients with stage III-IVA disease, other than those who were T1 N1 or T2 N1, the treatment results were poor.
The Journal of Laryngology & Otology 06/2008; 122(5):506-12. · 0.60 Impact Factor
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ABSTRACT: A modified Elman neural network controller is proposed to control the mover of a permanent magnet linear synchronous motor (PMLSM) servo drive to track periodic reference trajectories. First, the dynamic model of the PMLSM drive system is derived. Next, a modified Elman neural network is proposed to control the PMLSM. Moreover, the connective weights of the modified Elman neural network are trained online by back-propagation (BP) methodology. However, the learning rates of the online-training weights are usually selected by trial-and- error method, which is time-consuming. Therefore an improved particle swarm optimisation (IPSO) is adopted in this study to adapt the learning rates in the BP process of the modified Elman neural network to improve the learning capability. Finally, the control performance of the proposed modified Elman neural network controller with IPSO is verified by the simulated and experimental results.
IET Electric Power Applications 06/2008; · 1.17 Impact Factor
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ABSTRACT: A robust fuzzy neural network controller with nonlinear disturbance observer (RFNNCNDO) is proposed for the precision control of a two-axis motion control system. The adopted two-axis motion control system is composed of two permanent magnet linear synchronous motors (PMLSMs). The single-axis motion dynamics with the introduction of a lumped disturbance, which includes parameter variations, external disturbances, cross-coupled interference between the two PMLSMs and fiction force, is derived. Then, a nonlinear disturbance observer is applied to estimate the lumped disturbance and a feedback linearisation controller is adopted to stabilise the control system. However, the system responses are degraded by the existed observer error. To improve the control performance in the tracking of the reference contours, a Sugeno-type adaptive fuzzy neural network (SAFNN) is employed in the proposed RFNNCNDO to estimate the observer error directly. The online learning algorithms of the SAFNN guarantee the stability of closed-loop systems on the basis of the Lyapunov theorem. Moreover, the proposed control algorithms are implemented in a TMS320C32 digital signal processor (DSP)-based control computer. From the simulated and experimental results, the contour tracking performance of the two-axis motion control system is significantly improved and the robustness can be obtained as well using the proposed RFNNCNDO control system.
IET Control Theory and Applications 03/2008; · 0.99 Impact Factor
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ABSTRACT: An interval type-2 fuzzy neural network (IT2FNN) is developed for the position control of a thetas-axis motion-control stage using a linear ultrasonic motor to confront the uncertainties of the motion-control stage. A T2FNN consists of a type-2 fuzzy linguistic process as the antecedent part and a three-layer interval neural network as the consequent part. A general T2FNN is computationally intensive due to the complexity of reducing type 2 to type 1. Therefore an IT2FNN is adopted to simplify the computational process. Moreover, the developed IT2FNN combines the merits of an interval type-2 fuzzy logic system and a neural network. Furthermore, the parameter-learning of the IT2FNN, which is based on the supervised gradient decent method using a delta adaptation law, is performed on line. Experimental results show that the dynamic behaviours of the proposed IT2FNN control system are more effective and robust with regard to uncertainties than the type-1 FNN control system.
IET Electric Power Applications 02/2008; · 1.17 Impact Factor
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ABSTRACT: A sliding-mode controller (SMC) for a two-dimensional piezo-positioning stage is proposed. A mathematical model representing the motion dynamics of the stage is developed in which a hysteresis friction force describing the hysteresis behaviour of one-dimensional motion is used and a non-contant stiffness with the cross-coupling dynamics due to the effect of bending of lever mechanism in the x and y axes is also included. Based on the dynamic model, the proposed SMC with an asymptotic sliding surface is designed. A stability analysis is performed, and the transient performance is governed by the choice of control parameter values. With the proposed control scheme, the piezo-positioning stage is suitable for practical applications, especially in microscopy, with its need for validity of various trajectories. Experimental results show that the proposed controller provides high-performance dynamic characteristics and robustness to external load.
IET Control Theory and Applications 08/2007; · 0.99 Impact Factor
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ABSTRACT: We propose an intelligent adaptive backstepping control system using a recurrent neural network (RNN) to control the mover position of a magnetic levitation apparatus to compensate for uncertainties, including friction force. First, we derive a dynamic model of the magnetic levitation apparatus. Then, we suggest an adaptive backstepping approach to compensate disturbances, including the friction force, occurring in the motion control system. To further increase the robustness of the magnetic levitation apparatus, we propose an RNN estimator for the required lumped uncertainty in the adaptive backstepping control system. We further propose an online parameter training methodology, derived by the gradient descent method, to increase the learning capability of the RNN. The effectiveness of the proposed control scheme has been verified by experiment. With the proposed adaptive backstepping control system using RNN, the mover position of the magnetic levitation apparatus possesses the advantages of good transient control performance and robustness to uncertainties for the tracking of periodic trajectories
IEEE Transactions on Magnetics 06/2007; · 1.36 Impact Factor
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ABSTRACT: A frequency controlled three-phase induction generator (IG) system using ac-dc power converter is developed in this study. The electric frequency of the IG is controlled using the indirect field-oriented control mechanism. Moreover, an ac-dc power converter is adopted to convert the electric power generated by a three-phase IG from variable-frequency and variable-voltage to constant dc voltage. The rotor speed of the IG, the dc-link voltage and current of the power converter are detected simultaneously to yield maximum power output of the IG through dc-link power control. In this study, first, the indirect field-oriented mechanism is designed for the control of the IG. Then, a novel fuzzy modeling is developed to determine the flux control current and the maximum output power of the IG according to the rotor speed and the desired terminal voltage of the IG. Moreover, an online training recurrent fuzzy neural network (RFNN) with backpropagation algorithm is introduced as the tracking controller of dc-link power. Furthermore, some experimental results are provided to show the effectiveness of the proposed IG system using the RFNN controller for the dc-link power control. Finally, the control performance of the dc-link voltage control using the RFNN is also discussed by some experimental results
IEEE Transactions on Power Electronics 02/2007; · 4.65 Impact Factor
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ABSTRACT: We propose a recurrent radial basis function network-based (RBFN-based) fuzzy neural network (FNN) to control the position of the mover of a field-oriented control permanent-magnet linear synchronous motor (PMLSM) to track periodic reference trajectories. The proposed recurrent RBFN-based FNN combines the merits of self-constructing fuzzy neural network (SCFNN), recurrent neural network (RNN), and RBFN. Moreover, it performs the structureand parameter-learning phases concurrently. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient descent method, using a delta adaptation law. Furthermore, all the control algorithms are implemented in a TMS320C32 DSP-based control computer. The simulated and experimental results due to periodic reference trajectories show that the dynamic behaviors of the proposed recurrent RBFN-based FNN control system are robust with regard to uncertainties
IEEE Transactions on Magnetics 12/2006; · 1.36 Impact Factor
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ABSTRACT: A radial basis-function network (RBFN) is proposed for the intelligent control of a wind-turbine emulator and an induction-generator (IG) system with an AC/DC power converter. First, the indirect field-oriented mechanism is designed for the control of the IG system. Since the IG system is a nonlinear and time-varying dynamic system, an on-line trained RBFN is developed for the tracking controller of DC-link power to improve the control performance. The rotor speed of the IG, the DC-link voltage and current of the power converter are detected simultaneously to yield maximum power output of the converter through DC-link power control. Then, a closed-loop wind-turbine emulator, also using the RBFN, is designed to produce the maximum power for the IG system at various wind speeds to confront the parameter dependency of the wind-turbine emulator. Further, some experimental results are provided to show the effectiveness of the proposed wind-turbine emulator and IG system using the RBFN controller. Finally, the control performance of the DC-link voltage control using the RBFN is also discussed
IEE Proceedings - Electric Power Applications 08/2006; · 0.55 Impact Factor
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ABSTRACT: An adaptive backstepping controller is proposed to control the mover position of a linear-induction-motor (LIM) drive to compensate the uncertainties including the friction force. First, the dynamic model of an indirect field-oriented LIM drive is derived. Next, a backstepping approach is designed to compensate the uncertainties occurred in the motion-control system. Moreover, the uncertainties are lumped and the upper bound of the lumped uncertainty is necessary in the design of the backstepping controller. However, the upper bound of the lumped uncertainty is difficult to obtain in advance in practical applications. Therefore, an adaptive law is derived to adapt the value of the lumped uncertainty in real time, and an adaptive backstepping control law is resulted. Then, a field-programmable-gate-array (FPGA) chip is adopted to implement the indirect field-oriented mechanism and the developed control algorithms for possible low-cost and high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some experimental results. With the adaptive backstepping controller, the mover position of the FPGA-based LIM drive possesses the advantages of good transient-control performance and robustness to uncertainties in the tracking of periodic reference trajectories
IEE Proceedings - Electric Power Applications 08/2006; · 0.55 Impact Factor
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ABSTRACT: A robust fuzzy neural network (RFNN) sliding-mode control based on computed torque control design for a two-axis motion control system is proposed in this paper. The two-axis motion control system is an x-y table composed of two permanent-magnet linear synchronous motors. First, a single-axis motion dynamics with the introduction of a lumped uncertainty including cross-coupled interference between the two-axis mechanism is derived. Then, to improve the control performance in reference contours tracking, the RFNN sliding-mode control system is proposed to effectively approximate the equivalent control of the sliding-mode control method. Moreover, the motions at x-axis and y-axis are controlled separately. Using the proposed control, the motion tracking performance is significantly improved, and robustness to parameter variations, external disturbances, cross-coupled interference, and friction force can be obtained as well. Furthermore, the proposed control algorithms are implemented in a TMS320C32 DSP-based control computer. From the simulated and experimental results due to circle and four leaves reference contours, the dynamic behaviors of the proposed control systems are robust with regard to uncertainties
IEEE Transactions on Industrial Electronics 07/2006; · 5.16 Impact Factor
