Earticle

In this research, Neuro-fuzzy fuzzy feedback linearization controller is recommended for sensitive three degrees of freedom dental actuator. To design stable high quality controller conventional feedback linearization controller is recommended. Conventional feedback linearization (FL) controller is a nonlinear, stable, and reliable controller. This controller is model-base and in uncertain situation, model-base is challenge. The nonlinear dynamic formulation problem in highly nonlinear system has been solved by fuzzy logic theorem. Fuzzy logic theory is used to estimate the system dynamics. This type of controller is free of mathematical dynamic parameters of plant. When system works in uncertainty, the nonlinearity term of system is not equal to equivalent term of FL controller. According to this technique, the number of rule base is reduced with respect to have PID like fuzzy logic controller. The simulation results show that the proposed controller works well in various situations. Based on result and discussion, proposed method can eliminate chattering in certain and uncertain condition. This controller reduces the level of energy due to the torque performance. This controller removed the fluctuation in presence of uncertainties.

Design a nonlinear controller for second order nonlinear uncertain dynamical systems (e.g., Internal Combustion Engine) is one of the most important challenging works. This paper focuses on the design of a robust backstepping adaptive feedback linearization controller (FLC) for internal combustion (IC) engine in presence of uncertainties. In order to provide high performance nonlinear methodology, feedback linearization controller is selected. Pure feedback linearization controller can be used to control of partly unknown nonlinear dynamic parameters of IC engine. In order to solve the uncertain nonlinear dynamic parameters, implement easily and avoid mathematical model base controller, Mamdani’s performance/error-based fuzzy logic methodology with two inputs and one output and 49 rules is applied to pure feedback linearization controller. The results demonstrate that the error-based fuzzy feedback linearization controller is a model-free controllers which works well in certain and partly uncertain system. Pure feedback linearization controller and error-based feedback linearization like controller with have difficulty in handling unstructured model uncertainties. To solve this problem applied backstepping-based tuning method to error-based fuzzy feedback linearization controller for adjusting the feedback linearization controller gain ( ). This controller has acceptable performance in presence of uncertainty (e.g., overshoot=1%, rise time=0.48 second, steady state error = 1.3e-9 and RMS error=1.8e-11).

In this paper, we consider a nonlinear control problem for an Electro-Magnetic Suspension(EMS) system with flexible rail. In controller design based on feedback linearization, we apply the feedback linearization technique to the part of the system which provides nonlinearities to the plant. The experimental results demonstrate that the feedback linearization controller shows good performance.

This paper proposes a feedback-linearization-based control algorithm for multirotor unmanned aerial vehicles (UAVs). The feedback linearization scheme is highly efficient for considering nonlinearity between the rotational and translational motion of multirotor UAVs. We also propose a dynamic equation that reflects the aerodynamic effects of the vehicles; the equation's parameters can be determined through curve fitting using actual flight data. We derive the feedback linearization controller from the proposed dynamic equation, and propose a Luenberger observer to attenuate measurement noises. The proposed algorithm is implemented using our in-house flight control computer, and we describe its implementation in detail. To investigate the performance of the proposed algorithm, we carry out two flight scenarios: the first scenario, an autonomous landing on a moving platform, is a test of maneuverability; the second, picking up and replacing an object, test the algorithm's accuracy. In these scenarios, the proposed algorithm precisely controls multirotor UAVs, and we confirm that it can be successfully applied to real flight environments.

In this paper, the standard Dole, Glover, Khargoneker, and Francis (abbr. : DGKF 1989) H\ulcorner controller (H\ulcornerC) is extended to the nonlinear feedback linearization-H\ulcorner/sliding mode controller (NFL-H\ulcorner/SMC), to tackle the problem of the unmeasurable state variables as in the conventional SMC, to obtain smooth control as the linearized controller in a linear system, and to improve the time-domain performance under a worst scenario. The proposed controller is obtained by combining the H\ulcorner estimator with the nonlinear feedback linearization-sliding mode controller (NFL-SMC) and it does not need to measure all the state variables as in the traditional SMC. The proposed controller is applied as a nonlinear power system stabilizer (PSS) for the improvement of the power system damping characteristics of an single machine infinite bus system (SMIBS) connected through a double circuit line. The effectiveness of the proposed controller is verified by nonlinear time-domain simulation in case of a 3-cycle line-to-ground fault and in case of the parameter variations for the AVR gain K\ulcorner and for the inertia moment M.

In this paper, we present a nonlinear feedback linearization-full order observer/sliding mode controller (NFL-FOO/SMC), to obtain smmoth control as a linearized controller in a linear system (or to cancel the nonlinearity in a nonlinear system), and to solve the problem of the unmeasurable state variables as in the conventional SMC. The proposed controller is obtained by combining the nonlinear feedback linearization-sliding mode control (NFL-SMC) with the full order observer (FOO)and eliminates the need to measure all the state variables in the traditional SMC. The proposed controller is applied to the nonlinear power system stabilizer (PSS) for damping oscillations in a power system. The effectiveness of the proposed controller is verified by the nonlinear time-domain simulations in case of a 3-cycle line-to-ground fault and in case of the parameter variation for the AVR gain K\ulcorner and for the inertia moment M.

A robust controller is designed based on feedback linearization and sliding mode control to damp sub-synchronous control interaction (SSCI) in doubly fed induction generator (DFIG) wind power plants (WPPs) interfaced with the grid. A feedback-linearized sliding mode controller (FLSMC) is developed for the rotor-side converter (RSC) through feedback linearization, design of the sliding mode controller, and parameter tuning with the use of particle swarm optimization. A series-compensated 100-MW DFIG WPP is adopted in simulation to evaluate the effectiveness of the designed FLSMC at different compensation degrees and wind speeds. The performance of the designed controller in damping SSCI is compared with proportional-integral controller and conventional sub-synchronous resonance damping controller. Besides the better damping capability, the proposed FLSMC enhances robustness of the system under parameter variations.

In this Paper, the design of a feedback linearization controller using multilayer neural network is proposed. The Proposed feedback linearization control scheme is designed by finding Lie derivatives from an identified neural networks. Lie derivatives are expressed as a combination of weights and neuron outputs. The proposed method is applied to an antenna arm problem and the simulation results show performance comparisons between the ordinary feedback linearization and the Proposed method.

본 연구에서는 일단앞섬 상태추정기(one-step ahead state estimator)를 사용 하는 방법을 제안하였다. 이 방법은 비선형 시스템을 궤환 선형화하여 등가 선형모 델을 구한 후, 이 등가 선형 모델에 일단앞섬 상태추정기를 구성하여 디지틀 제어를 실현하는 방법으로서, 계측할 수 없는 상태를 추정할 수 있을 뿐 아니라, 선형시스템 에 대한 상태 추정기이므로 다음 단계의 상태를 비교적 정확하게 추정할 수 있어서 궤 환 선형화시 요구되는 연속적인 상태 궤환에 근사시킬 수 있고, 직접 궤환 선형화된 등가 선형 모델의 상태를 추정할 수 있으므로, 변환시의 연산량도 줄일 수 있다. 이 로서 Grizzle이나 Lee의 방법보다 간단하게 궤환 선형화에 의한 디지틀 상태 제어기를 구현할 수 있었다.

In this paper the feedback linearization of the valve-controlled nonlinear hydraulic velocity control system and the implementation of the digital state feedback controller is studied. The $C^{\infty}$ nonlinear transfomation to the electro-hydraulic velocity control system, which transforms nonlinear system to linear equivalent one, is obtained. It is shown that this transformation is global one. The digital controller to this linearized model is obtained by using the one-step ahead state estimator and implemented to real plant. The proposed implementation method is easier than the other proposed methods and it is possible to control in real time. The experiment and simulation study show that the implementation of the digital state feedback controller based on the feedback linearized model is successful..

The spherical CVT, intended to overcome some of the limitations of existing CVT designs, is marked by its simple kinematic design, improved efficiency of the shift actuator, and IVT characteristics, i.e., the ability of smooth transition between the forward, neutral, and reverse states without the need for any brakes or clutches. And it has been promised much possibility of energy savings and various applications for small power capacity machinery. Due to the nonlinearity of the spherical CVT shifting dynamics, however the original open-loop system is inherently unstable. Hence a feedback controller is necessary to make the system stable and to achieve effective tracking performance. To do this, we designed a feedback controller that cancels nonlinearities and transforms the original nonlinear system dynamics into a stable and controllable linear one, based on the input-state linearization method.

A nonlinear feedback linearizing control method for a EMS(Electro Magnetic Suspension) system is proposed. After linearizing the system using the exact linearizing method, conventional linear system control theory has been applied. Computer simulations are carried out in order to compare the performance of the proposed controller with that of the existing controller designed by using Taylor series expansion around nominal points.

It is known that Feedback linearization has important limitations-the full state has to be measured; no robustness is guaranteed with respect to parameter uncertainty and unmodeled dynamics. In this paper, we construct a nonlinear feedback linearization controller for the system containing uncertain parameters and unknown states, in the case of EMS system with rail vibration. Performance of this controller is demonstrated by computer simulation.

Asymmetric cylinders are usually used as an actuator of active suspensions. The conventional optimal controller design does not include actuator dynamics as a state and force controller is needed to track the desired force. But the actuator is not ideal, so performance of an active suspension system is degraded. In this paper, we take account nonlinear actuator dynamics and obtain a linear model using a feedback linearization technique then apply optimal control method. Effectiveness of proposed method is demonstrated by numerical simulation of 1/4 car model.

The implementation of Nonlinear Feedback Linearization control for Electro-Magnetic Suspension system is presented. The controller using TMS320C31 DSP chip was proposed and the experiments were performed Control law for EMS system using feedback linearization is derived and implemented in the DSP. Some tests were constructed far experimental comparison between feedback linearization and classical state feedback The experimental results demonstrate that the feedback linearization controller shows bettor performance than that of the classical state feedback controller and it is robust with respect to disturbance and parameter variation, though some steady-state errors appear.

Asymmetric cylinders are usually used as an actuator of active suspensions. The conventional optimal controller design does not include actuator dynamics as a state. and force controller is needed to track the desired force. But the actuator is not ideal, so performance of an active suspension system is degraded. In this paper, we take account nonlinear actuator dynamics and obtain a linear model using a feedback linearization technique then apply optimal control method. For real time application, gain-scheduling method is used. Effectiveness of proposed method is demonstrated by numerical simulation of 1/4 car model.

A nonlinear controller based on feedback linearization method is proposed for an electromagnetic suspension system. After exactly linearizing the system with nonlinear feedback, linear control technique is applied. Modeling of stagger typed magnet is introduced and controlled for not only levitation, but guidance. By the feedback linearization, the nonlinear, MIMO system is linearized and decoupled, so we can use linear control law. The simulation of this system control skim is demonstrated. Robustness properties of the proposed controller with respect to the load variations and external disturbance is also analyzed for a multi input multi output system. In this properties, the boundary of variation is proposed.