Jeng-Dao Lee

Real-Time PID Control Strategy for Maglev Transportation System via Particle Swarm Optimization

This paper focuses on the design of a real-time particle-swarm-optimization-based proportional-integral-differential (PSO-PID) control scheme for the levitated balancing and propulsive positioning of a magnetic-levitation (maglev) transportation system. The dynamic model of a maglev transportation system, including levitated electromagnets and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics, is first constructed. The control objective is to design a real-time PID control methodology via PSO gain selections and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. The effectiveness of the proposed PSO-PID control scheme for the maglev transportation system is verified by numerical simulations and experimental results, and its superiority is indicated in comparison with PSO-PID in previous literature and conventional sliding-mode (SM) control strategies. With the proposed PSO-PID control scheme, the controlled maglev transportation system possesses the advantages of favorable control performance without chattering phenomena in SM control and robustness to uncertainties superior to fixed-gain PSO-PID control.

Adaptive Fuzzy-Neural-Network Control for Maglev Transportation System

A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.

Wavelet neural network control for induction motor drive using sliding-mode design technique

This paper addresses an adaptive observation system and a wavelet-neural-network (WNN) control system for achieving the favorable decoupling control and high-precision position tracking performance of an induction motor (IM) drive. First, an adaptive observation system with an inverse rotor time-constant observer is derived on the basis of model reference adaptive system theory to preserve the decoupling control characteristic of an indirect field-oriented IM drive. The adaptive observation system is implemented using a digital signal processor with a high sampling rate to make it possible to achieve good dynamics. Moreover, a WNN control system is developed via the principle of sliding-mode control to increase the robustness of the indirect field-oriented IM drive with the adaptive observation system for high-performance applications. In the WNN control system, a WNN is utilized to predict the uncertain system dynamics online to relax the requirement of uncertainty bound in the design of a traditional sliding-mode controller. In addition, the effectiveness of the proposed observation and control systems is verified by simulated and experimental results.

Robust fuzzy neural network control for linear ceramic motor drive via backstepping design technique

This study presents a robust fuzzy-neural-network (RFNN) control system for a linear ceramic motor (LCM) that is driven by an unipolar switching full-bridge voltage source inverter using LC resonant technique. The structure and operating principle of the LCM are introduced. Since the dynamic characteristics and motor parameters of the LCM are nonlinear and time varying, a RFNN control system is designed based on the hypothetical dynamic model to achieve high-precision position control via the backstepping design technique. In the RFNN control system a fuzzy neural network (FNN) controller is used to learn an ideal feedback linearization control law, and a robust controller is designed to compensate the shortcoming of the FNN controller. All adaptive learning algorithms in the RFNN control system are derived from the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. The effectiveness of the proposed RFNN control system is verified by experimental results in the presence of uncertainties. In addition, the advantages of the proposed control system are indicated in comparison with the traditional integral-proportional (IP) position control system.

Robust Levitation Control for Linear Maglev Rail System Using Fuzzy Neural Network

The levitation control in a linear magnetic-levitation (Maglev) rail system is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. This study mainly designs a robust fuzzy-neural-network control (RFNNC) scheme for the levitated positioning of the linear Maglev rail system with nonnegative inputs. In the model-free RFNNC system, an online learning ability is designed to cope with the problem of chattering phenomena caused by the sign action in backstepping control (BSC) design and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. Moreover, the nonnegative outputs of the RFNNC system can be directly supplied to electromagnets in the Maglev system without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the levitation control of a Maglev system is verified by numerical simulations and experimental results, and the superiority of the RFNNC system is indicated in comparison with the BSC system.

Design of Cascade Adaptive Fuzzy Sliding-Mode Control for Nonlinear Two-Axis Inverted-Pendulum Servomechanism

Because the dynamic characteristic of a two-axis inverted-pendulum servomechanism is a nonlinear underactuated system, it is difficult to design a suitable control scheme that realizes real-time stabilization and accurate tracking control simultaneously. In general, the techniques developed for fully actuated systems cannot be used directly in underactuated systems. In this study, a cascade adaptive fuzzy sliding-mode control (AFSMC) scheme including inner and outer control loops is investigated for the stabilizing and tracking control of a nonlinear two-axis inverted-pendulum servomechanism. The aim of the inner control loop is to design an AFSMC law with fuzzy estimators so that the stick-angle vector can fit the stick-angle command vector derived from the stick-angle reference model. In the outer loop, the reference signal vector is designed via a fuzzy path-planning scheme so that the cart position vector tracks the cart-position command vector and the stick-angle tracking-error vector converges to zero simultaneously. All adaptive algorithms in the cascade AFSMC system are derived in the sense of Lyapunov stability analysis, so that system stability can be guaranteed in the entire closed-loop system. The effectiveness of the proposed control strategy is verified by numerical simulations and experimental results, and the superiority of the cascade AFSMC system is indicated in comparison with a cascade sliding-mode control system.

Cascade Direct Adaptive Fuzzy Control Design for a Nonlinear Two-Axis Inverted-Pendulum Servomechanism

This paper presents and analyzes a cascade direct adaptive fuzzy control (DAFC) scheme for a two-axis inverted-pendulum servomechanism. Because the dynamic characteristic of the two-axis inverted-pendulum servomechanism is a nonlinear unstable nonminimum-phase underactuated system, it is difficult to design a suitable control scheme that simultaneously realizes real-time stabilization and accurate tracking control, and it is not easy to directly apply conventional computed torque strategies to this underactuated system. Therefore, the cascade DAFC scheme including inner and outer control loops is investigated for the stabilizing and tracking control of a nonlinear two-axis inverted-pendulum servomechanism. The goal of the inner control loop is to design a DAFC law so that the stick angle vector can fit the stick angle command vector derived from the stick angle reference model. In the outer loop, the reference signal vector is designed via an adaptive path planner so that the cart position vector tracks the cart position command vector. Moreover, all adaptive algorithms in the cascade DAFC system are derived using the Lyapunov stability analysis, so that system stability can be guaranteed in the entire closed-loop system. Relying on this cascade structure, the stick angle and cart position tracking-error vectors will simultaneously converge to zero. Numerical simulations and experimental results are given to verify that the proposed cascade DAFC system can achieve favorable stabilizing and tracking performance and is robust with regard to system uncertainties.

On-Line Supervisory Control Design for Maglev Transportation System via Total Sliding-Mode Approach and Particle Swarm Optimization

This study focuses on the design of an on-line levitation and propulsion control for a magnetic-levitation (maglev) transportation system. First, the dynamic model of a maglev transportation system including levitated electromagnets driven by linear servo amplifiers and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed. Then, a total sliding-mode (TS) control strategy is introduced, and the concept of TS control is incorporated into particle swarm optimization (PSO) to form an on-line TSPSO control framework with varied inertial weights for preserving the robust control characteristics and reducing the chattering control phenomena of TS control. In this TSPSO control scheme, a PSO control system is utilized to be the major controller, and the stability can be indirectly ensured by the concept of TS control without strict constraint and detailed system knowledge. In order to further directly stabilize the system states around a predefined bound region and effectively accelerate the searching speed of the PSO control, a supervisory mechanism is embedded into the TSPSO control to constitute a supervisory TSPSO (STSPSO) control strategy. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations and experimental results, and the superiority of the STSPSO control scheme is indicated in comparison with the adaptive fuzzy neural network, PSO-based proportional-integral-differential, TS and TSPSO control strategies.

DSP-Based Sliding-Mode Control for Electromagnetic-Levitation Precise-Position System

This paper investigates the robust tracking control problem for a bipolar electromagnetic-levitation precise-position system. The dynamic model of the precise-position device is derived by conducting a thorough analysis on the nonlinear electromagnetic forces. Conventional sliding-mode control and terminal sliding-mode control strategies are developed to guarantee asymptotic and finite-time tracking capabilities of the closed-loop system. A lumped uncertainty estimator is proposed to estimate the system uncertainties. The estimated information is then used to construct a smooth uniformly ultimately bounded sliding-mode control. An exact estimator is also proposed to exactly estimate the unknown uncertainties in finite time. The output of the exact estimator is used to design a continuous chattering free terminal sliding-mode control. The time taken for the closed-loop system to reach zero tracking error is proven to be finite. Experiment results are presented, using a real time digital-signal-processor (DSP) based electromagnetic-levitation system to validate the analysis.

High-efficiency fuel-cell power inverter with soft-switching resonant technique

In order to reduce the capital and overall operating cost of a fuel-cell system, a high-efficiency fuel-cell power inverter with a simple framework is required. The high-order two-inductance two-capacitance (LLCC) resonant technique is adopted in this study to implement a low-frequency 60-Hz sinewave voltage inverter utilized in the proton exchange membrane fuel-cell (PEMFC) system. The methodology for inverting dc voltage into low-frequency ac voltage is usually generated by the pulse-width-modulation (PWM) technique. However, the PWM-type inverter output has high-frequency harmonic components. Although an adequately designed filter could be utilized to overcome this problem, there are still some undesirable effects introduced by the high-frequency switching loss, electromagnetic-interference, harmonic current, and load variation. A novel power inverter via the LLCC resonant technique is designed for inverting dc voltage into 60-Hz ac sinewave voltage in the PEMFC system. This circuit scheme has the merits of low harmonic components, soft switching, high efficiency, and simplified implementation. The effectiveness of the proposed resonant inverter used for the PEMFC system is verified by numerical simulations and experimental results.