Earticle

Electric motors in electric vehicles has quite fast torque response, thus fast minor feedback loops can be applied for vehicle slip control. In this study, a model following control based slip ratio controller is suggested. This paper introduced a novel strategy of control for wheel slip in electric vehicle by computer simulations. This paper suggested that the control strategy in this paper can control the driving motor torque to drive the wheel on the slippery road surface.

In this paper, we proposed a model-following controller for single-input-single-output (SISO) and multiple-input-multiple-output (MIMO) systems. This control system does not include plant parameters, and thus is designed using normalized plant. Moreover, we propose a design method of the normalized plant for the controlled plant with zero. And furthermore, we propose a design method for model-following controllers, which can easily decouple for MIMO systems. Simulation and experimental results show that the proposed method is robust to plant parameter variations and external disturbances.

In this paper, a reconfigurable flight control system is designed by applying the sliding mode control scheme. The sliding mode control method is a nonlinear control method which has been widely used because of its merits such as robustness and flexibility. In the sliding mode controller design, the signum function is usually included, but it causes the undesirable chattering problem. The chattering phenomenon can be avoided by using the saturation function instead of signum function. However, the boundary layer of the sliding surface should be carefully treated because of the use of the saturation function. In contrast to the conventional approaches, the thickness of the boundary layer of our approach does not need to be small. The reachability to the boundary layer is guaranteed by the sliding mode controller. The fault detection and isolation process is operated based on a sliding mode observer. To evaluate the reconfiguration performance, a numerical simulation using six degree-of-freedom aircraft dynamics is performed.

In this paper, a designing method of model following control system for linear descriptor system with disturbances is proposed. The features of this method are:1) both the physical structure of the system and the physical system variables properties can be preserved because there is no necessary to make transformation of this system. 2) boundedness of internal states are proved by means of coprime factorization of descriptor system.

In this paper, the variable structure model following control scheme is proposed for the nonlinear robot manipulator system. The proposed control system guarantees that the system state is in the sliding mode for all time. Therefore, error transient can be prescribed in advance for all time. Furthermore, overall system is globally exponentially stable. Chattering problem is reduced by the introduction of a boundary layer. Simulation results are given to show the usefulness of the proposed control scheme.

This paper is concerned with a robust model following control scheme for manipulators which contains uncertain terms. Our method consists of nonlinear compensation and linear compensation. The former ensures the robustness of the plant, the later achieves both the desired model following response and the desired initial error convergence.

A design method of model following control system using neural networks is proposed. An unknown nonlinear single-input single-output plant is identified using a multilayer neural networks. A linear controller is designed fer the linear approximation model obtained by linearinzing the identification model. The identification model is also used as a plant emulator to obtain the prediction error. Deficient servo performance due to controlling nonlinear plant with only linear controller is mended by adjusting the linear controller output using the prediction output and the parameters of the identification model. An optimal preview controller is adopted as the linear controller by reason of having good servo performance lowering the peak of control input. Validity of proposed method is illustrated through a numerical simulation.

In this paper, a new design method of variable structure model following control system(VSMFCS) for robot manipulators is proposed. The proposed controller overcomed reaching phase problem by using function augmenting scheme to the sliding surface. Therefore, it can be guaranteed that the overall system always has a robust property against parameter variations and external disturbances. Furthermore, the proposed controller does not use the model state, .chi.$_{m}$, different from other previous works. Regardless of not using the model state, the model following error dynamics, virtual dynamics, is shown to be globally exponentially stable. The efficiency of the proposed method has been demonstrated by an example.e.

In this paper an effective direct torque control (DTC) and stator flux control is developed for a quasi six-phase induction motor (QIM) drive with sinusoidally distributed windings. Combining sliding-mode (SM) control and adaptive input-output feedback linearization, a nonlinear controller is designed in the stationary reference frame, which is capable of tracking control of the stator flux and torque independently. The motor controllers are designed in order to track a desired second order linear reference model in spite of motor resistances mismatching. The effectiveness and capability of the proposed method is shown by practical results obtained for a QIM supplied from a voltage source inverter (VSI).

In this paper, a novel model following sliding mode control is proposed by using a novel sliding mode with virtual state. This sliding surface has nominal dynamics of an original system and makes it possible that the Sliding Mode Control(SMC) technique is combined with the optimal controller. Its design is based on the argument system whose dynamics have one higher order than that of the original system. The reaching phase is eliminated by using an initial virtual state that makes the initial sliding function equal to zero.

In this paper, an intelligent sliding-mode position controller (ISMC) for achieving favorable decoupling control and high precision position tracking performance of permanent-magnet synchronous motor (PMSM) servo drives is proposed. The intelligent position controller consists of a sliding-mode position controller (SMC) in the position feed-back loop in addition to an on-line trained fuzzy-neural-network model-following controller (FNNMFC) in the feedforward loop. The intelligent position controller combines the merits of the SMC with robust characteristics and the FNNMFC with on-line learning ability for periodic command tracking of a PMSM servo drive. The theoretical analyses of the sliding-mode position controller are described with a second order switching surface (PID) which is insensitive to parameter uncertainties and external load disturbances. To realize high dynamic performance in disturbance rejection and tracking characteristics, an on-line trained FNNMFC is proposed. The connective weights and membership functions of the FNNMFC are trained on-line according to the model-following error between the outputs of the reference model and the PMSM servo drive system. The FNNMFC generates an adaptive control signal which is added to the SMC output to attain robust model-following characteristics under different operating conditions regardless of parameter uncertainties and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed intelligent sliding mode position controller. The results confirm that the proposed ISMC grants robust performance and precise response to the reference model regardless of load disturbances and PMSM parameter uncertainties.

A discrete-time integral sliding mode control scheme is proposed to realize the problem of robust tracking and modeling following for a class of uncertain linear systems. It will be shown that the proposed scheme guarantees the stability of closed-loop system and achieves zero-tracking error in the presence of parameter uncertainties and external disturbances. The selection of switching surface and the existence of sliding mode are two important issues, which have been addressed. This scheme assures robustness against system uncertainties and disturbances. Chattering phenomenon and reaching phase are eliminated. Moreover, the knowledge of upper bound of uncertainties is not required. Both the theoretical analysis and illustrative example demonstrate the validity of the proposed scheme.

A model following control system(MFCS) can give general output signals following desired ones. In previous studies, a method of nonlinear MFCS was proposed by S.Okubo[1]. In this paper, the method of nonlinear MFCS will be extended to discrete time nonlinear systems. It is easy to extend the method to discrete time systems. But in the case .gamma.=1 discrete time systems, the proof becomes difficult, because the transfer function from f(v(k)) to v(k) can't be a positive real function. In this case, to ensure that internal states are stable, a new criterion is proposed.

본 논문은 모델추종 제어을 위한 PID제어기법을 제안한다. 이산시간영역에서 제어성능을 개선하기 위해 제안하였고, PID 제어계에 새로운 전치 보상기를 삽입하여 모델추종제어계가 되도록 하였다. 외란이나 부하변동에 의해서 계의 응답이 변할 때 PID 제어기의 이득을 재조정할 필요가 있다. PID 제어계에서 각 PID 이득이 제어계의 성능을 크게 좌우하게 되므로 신경망을 PID제어기에 결합하여 제어계의 성능을 향상시켰고 제안한 제어계에서 PID제어기의 이득은 역전파 알고리즘에 의해 자동적으로 조정되어지도록 하였다. 모델추종 제어계의 제어성능을 확인하기 위하여 제어대상을 직류 서보 전동기의 각 위치로 하였다. 이것을 위치 제어계에 적용하여 실험을 통해 그 성능을 증명하였다.

This paper proposes the design of the model following control system using the PID control structure. PID control system became model following control by inserting new pre-compensator in order to improve control performance in discrete-time region. Gain of the PID controller needs to be readjusted when response of system changes due to disturbance or load fluctuation. Performance of control system improves by joining neural network to PID control system because performance of control system depends largely on each PID gain in PID control system. And the games of the PID controller in the proposed control system are automatically adjusted by back-propagation algorithm of the neural network. Angular position of DC servo motor is selected as a plant in order to verify control performance in model following control. After it is applied to the position control system, it's performance is verified through computer experiment.

In this paper the design of the model following control system is proposed using the PID control structure. The games of the PID controller in the proposed control system are automatically adjusted by back-propagation algorithm of the neural network. And applying to the position control system, it's performance is verified through the results of computer experiment.

In this paper, GMDH(Group Method of Data Handling) algorithm, which is based on heuristic self organization to predict and identify the complex system, is applied to the control system of DC servo motor. The mathematical relation between input voltage and motor speed is obtained by GMDH algorithm. A design method of model following control system based on GMDH algorithm is developed. As a result of applying this method to DC servo motor, the simulation and experiment have shown that the developed method gives a good performance in tracking the reference model and in rejection of disturbance, in spite of constant load and changing load.

This paper presents a decentralized model reference adaptive control scheme for an interconnected continuous linear system composed of a number of single-input single-output subsystems in which outgoing interactions pass through the measurement channel and are subject to bounded external disturbance. The scheme can treat the unknown strength of interactions as well as the uncertainty of subsystems.