Earticle

This paper focuses on a sensorless control method for small wind turbines based on permanent magnet synchronous generators. An adaptive intelligent algorithm is proposed to estimate the rotor speed for designing sensorless controllers of small wind turbines. In the proposed algorithm, a model reference adaptive system has been formed by using the instantaneous and steady-state powers to estimate the rotor speed. Then a power speed feedback (PSF) control carries out maximum power point tracking by using the optimum relationship between speed and power, which is kept in a lookup table. The efficiency of the system is improved by vanishing the observation errors and tracking errors. The validity of the proposed algorithm is verified by the simulation results.

A tremor is an involuntary, somewhat rhythmic, muscle contraction and relaxation involving to and from movements (oscillations or switching) of one or more body parts. It is the most common of all involuntary movements and can affect the hands, arms, eyes, face, head, vocal folds, trunk, and legs. Most tremors occur in the hands. In some people, tremor is a symptom of another neurological disorder. This research focuses on the design and analysis of a model reference-based controller for highly nonlinear dynamic multi degrees of freedom, in presence of hand tremors. In order to provide a high performance nonlinear methodology, the inverse dynamical controller is selected. The inverse dynamic controller is the type of model reference controller. It is nonlinear and stable controller. To reduce the challenges of this controller hybrid fuzzy controller is used. However, this method has many advantages, but it has challenged in presence of uncertainties. To solve this challenge model reference-based direct adaptive methodology is used and applied to hybrid inverse dynamic fuzzy controller.

This paper presents a robust model reference adaptive controller for continuous-time single-input single-output linear time-invariant systems which are subjected to output-dependent disturbances as well as bounded external disturbances. In the derived controller form, an additional output error feedback term is included to over-ride the destabilizing effects by the output-dependent disturbances.

The paper presents a model reference adaptive control containing a fuzzy algorithm for tuning the gain coefficient which adjusts the level of the fuzzy controller output. The synthesis of a fuzzy tuning algorithm has been performed for the inverted pendulum system. The computer simulation results have proved the efficiency of the proposed method, showing stable system responses.

The aim of this paper is to design an adaptive speed control system of a marine diesel engine by fusion of hard computing based proportional integral derivative (PID) control and soft computing based fuzzy control methods. The model of a marine diesel engine is considered as a typical non oscillatory second order system. When its model and the actual marine diesel engine ate not matched, it is hard to control the speed of the marine diesel engine. Therefore, this paper proposes two methods in order to obtain the speed control characteristics of a marine diesel engine. One is an efficient method to determine the PID control parameters of the nominal model of a marine diesel engine. Second is a reference adaptive speed control method that uses a fuzzy controller and derivative operator for tracking the nominal model of the marine diesel engine. It was found that the proposed PID parameters adjustment method is better than the Ziegler & Nichols' method, and that a model reference adaptive control is superior to using only PID controller. The improved control method proposed here, could be applied to other systems when a model of a system does not match the actual system.

In this paper, an adaptive control algorithm using system identification is proposed for an aircraft fault tolerant control system. A discrete state-space system is reformulated to be the ARX model which has the advantage in handing variable structure systems. Discrete model reference adaptive control is used to make the output of fault system follow the output of reference model. To validate the performance of the proposed control scheme, numerical simulations are performed for the high performance aircraft with control surface damage.

본 연구에서 구현하고자 하는 선박의 회두각 제어의 경우 파도, 바람, 조류 등의 외란의 영향을 많이 받고 있을 뿐만 아니라 그 운동 특성 역시 비선형이므로 적절한 파라미터의 선정과 제어기 구성에 어려움이 따른다. 이의 해결을 위해 K. M. Passino 등에 의해 비선형 특성을 지닌 기준 모델 적응 퍼지 알고리즘을 적용하여 제어기 구성을 시도한바 있고, 국내에서도 김종화 등에 의해 유사한 방법이 시도되어졌다. 본 연구에서는 이상의 시도에서 기준 모델에 의한 제어기 파라미터의 동정의 방법으로 사용한 M.I.T 룰 대신 일반적인 유전 알고리즘에 의해 퍼지 제어기의 파라미터를 동정하고자 한다. 유전 알고리즘에 기반한 기준 모델 적응 퍼지 제어기(MRGAFC) 알고리즘을 제안하며, 이의 검증을 위하여 화물선 회두각의 조향 문제에 이를 적용하여 종래의 방법들과 비교를 수행할 것이다.

본 연구에서 구현하고자 하는 선박의 회두각 제어의 경우 파도, 바람, 조류 등의 외란의 영향을 많이 받고 있을 뿐만 아니라 그 운동 특성 역시 비선형이므로 적절한 파라미터의 선정과 제어기 구성에 어려움이 따른다. 이의 해결을 위해 K. M Passino 등에 의해 비선형 특성을 지닌 기준 모델 적응 퍼지 알고리즘을 적용하여 제어기 구성을 시도한바 있고, 국내에서도 김종화 등에 의해 유사한 방법이 시도되어졌다. 본 연구에서는 이상의 시도에서 기준 모델에 의한 제어기 파라미터의 동정의 방법으로 사용한 M.I.T 룰 대신 일반적인 유전 알고리즘에 의해 퍼지 제어기의 파라미터를 동정하고자 한다. 유전 알고리즘에 기반한 기준 모델 적응 퍼지 제어기(MRGAFC: Model Reference Genetic Adaptive Fuzzy Controller) 알고리즘을 제안하며, 이의 검증을 위하여 화물선 회두각의 조향문제에 이를 적용하여 종래의 방법들과 비교를 수행할 것이다.

In this paper, a model reference adaptive control scheme is applied to the normal acceleration controller for missiles with nonminimum-phase characteristics. The proposed scheme has an auxiliary compensator, an identifier of plant parameters and a feedback control law. First, plant parameters are estimated by the identifier and based the parameter estimates the coefficients of the compensator are calculated so that the estimated plant model with the compensator becomes minimum-phase. In this calculation, Nehari Algorithm is used. Parameters of the control law are then updated so that the extended plant model follows the given reference model. It is shown that the performance of the designed controller is satisfied via computer simulations.

Decentralized model reference adaptive controller is used to control interconnected system. Influences caused by interactions between each subsystem are regarded as unmodeled dynamics or disturbances, thus decentralized adaptive controller is designed using MRAC algorithms which guarantees robustness. To expand the stability regions of over all system and to improve control performances, higher level controller is introduced to adjust the control factors such as filter band, size of deadzone or maximum norm of parameter. Local controllers for each subsystem are realized in real time and higher level controller has an ability of detecting the instability phenomena and adjusts the local controller by analysis of power spectrum or square sum of tracking errors.

A direct model reference adaptive control (DMRAC) is applied to the speed control of brushless do(BLDC) motor. The main objective is to achieve precise speed control in the face of varying motor parameters and load. The control is described as an outer loop speed control and an inner current loop control which has raster dynamics than the speed loop. The adaptive control is applied to the outer speed control loop. DMRAC is compared to an indirect adaptive controller(IMRAC) and a PI controller. Simulation results show that the two adaptive controllers give similar respose and are superior to the PI controller. However, the DMRAC algorithm is simpler to implement.

본 논문에서는 FLC의 적응능력에 대한 단점과 제어규칙 도출의 어려움을 극복하기 위하여 모델기준 적응 퍼지제어기(MRAFC)를 제안하였다. MRAFC는 단순퍼지제어기와 플랜트로 구성된 내부 피드백 루프와 단순 퍼지제어기의 제어규칙을 동조시키는 외부 루프로 구성된다. 기준모델은 기준입력에 대해 전체 제어시스템에 요구되는 제어성능을 특성화하고 수량화하는 퍼지제어기 설계기준으로 사용되었다. 그리고 적응 매카니즘은 FLC 제어규칙을 동조하는 역할을 수행한다. 제안한 알고리즘의 성능은 DC 서보 전동기에 대한 실험에 의해 검증되었다.

In this paper, model reference adaptive fuzzy controller (MRAFC) was proposed in order to overcome the difficulty of extracting rules and defects of the adaptation performance in the FLC. MRAFC comprised inner feedback loop consisting of the FLC and plant, and outer loop consisting of an adaptation mechanism which was designed for tuning a control rule of the FLC. A reference-model was used for design criteria of a fuzzy controller which characterizes and quantizes the control performance required in the overall control system. Tuning control rules of FLC is performed by the adaptation mechanism. The performance of proposed algorithm was verified through experiment for the DC servo motor.

This paper presents an adaptive fuzzy controller using an neural network and adaptation algorithm. Reference-model following neuro-fuzzy controller(RMFNFC) is invesgated in order to overcome the difficulty of rule selecting and defects of the membership function in the general fuzzy logic controller(FLC). RMFNFC is developed to tune various parameter of the fuzzy controller which is used for the discrete nonlinear system control. RMFNFC is trained with the identification information and control closed loop error. A closed loop error is used for design criteria of a fuzzy controller which characterizes and quantize the control performance required in the overall control system. A control system is trained up the controller with the variation of the system obtained from the identifier and closed loop error. Numerical examples are presented to control of the discrete nonlinear system. Simulation results show the effectiveness of the proposed controller.

고전적인 제어 기법들을 이용한 전동기의 속도 제어기는 하나의 고정된 동작점에 대해서 대개 양호한 동작 특성을 얻을 수 있으나 전동기 매개변수의 섭동 및 부하 외란의 존재시 규정된 제어 동작을 유지하기가 어렵다는 단점을 갖고 있다. 따라서 본 연구에서는 이러한 단점을 극복하기 위하여 적응 제어 기법중의 하나인 기준 모델 적응 제어 (Model Reference Adaptive Control : MRAC) 방식을 직류 전동기의 속도 제어에 적용하였으며 또한, 2차 이상인 전동기의 속도 제어 시스템을 1차로 저차화시켜 제어 알고리즘의 계산에 소요되는 시간을 줄임으로써 실시간 제어가 가능토록 하였다. 제시된 기준 모델 제어 기법과 PI 제어 기법을 직류 전동기의 속도 제어에 각각 적용하고 부하의 관성변화에 다른 속도 응답 특성을 실험을 통하여 비교 검토하였다.

In this paper, the design of PID controller using Neural networks for the control of non-linear system is presented. First, non-linear system is identified using BPN(Backpropagation Network) algorithm. This identified model is connected to the PID controller and the parameters of PID controller are updated to the direction of reducing the difference between the identified model output and model reference output in arbitrary input signal. Therefore, identified model output tracks the model reference output in an acceptable error range and the parameters of controller are updated adaptively. The output of the system has a good performance in case of both noisy and noiseless model reference and we can control the system stable in off-line when the dynamics of the system is changed.