• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.75.1-75
• /
• 2001

In this paper, we provide the synthesis of non-fragile H$\infty$ output feedback controllers for linear systems with affine parameter uncertainties, and dynamic output feedback controller with structural uncertainty. The sufficient condition of controller existence, the design method of robust and non-fragile H$\infty$ output feedback controller, and the region of controllers which satisfies non-fragility are presented. Also using some change of variables and Schur complements, the obtained condition to a compact set. We show that the resulting controller guarantees the asymptotic stability and disturbance attenuation of the closed ...

• International Journal of Control, Automation, and Systems
• /
• v.2 no.4
• /
• pp.475-484
• /
• 2004

In this paper, we present a dynamic output-feedback receding horizon $H_{\infty}$controller for linear discrete-time systems with disturbance. The controller is obtained numerically from the finite horizon output-feedback $H_{\infty}$optimization problem, which is, in fact, hardly solved analytically. Under a matrix inequality condition on the terminal weighting matrix, the monotonic decreasing property of the cost is shown. This property guarantees both the closed-loop stability and the $H_{\infty}$norm bound. Then, we extend the proposed design method to a reference tracking problem and a problem for time-varying systems. Numerical examples are given to illustrate the performance of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.898-902
• /
• 2003

For linear descriptor systems, we consider the $H_{INFTY}$ controller design problem via output feedback. Both static output feedback and dynamic one are discussed. First, in the case of static output feedback, we reduce our control problem to solving a bilinear matrix inequality (BMI) with respect to the controller coefficient matrix, a Lyapunov matrix and a matrix related to the descriptor matrix. Under a matching condition between the descriptor matrix and the measured output matrix (or the control input matrix), we propose setting the Lyapunov matrix in the BMI as being block diagonal appropriately so that the BMI is reduced to LMIs. For fixed-order dynamic $H_{INFTY}$ output feedback, we formulate the control problem equivalently as the one of static output feedback design, and thus the same approach can be applied.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2002.04a
• /
• pp.543-550
• /
• 2002

This work is concerned with the estimation of output derivatives and their use for the design of robust controller for linear systems with systems uncertainties due to modeling errors and disturbance. It is assumed that a nominal transfer function model and Quantitative bounds for system uncertainties are known. The developed control schemes are shown to achieve regulation of the system output and ensures boundedness of the system states without imposing any structural conditions on system uncertainties and disturbances. Output derivative estimation is first conducted trough restructuring of the plant in a specific parameterization. They are utilized for constructing robust nonlinear high-gain feedback controller of a SMC(Sliding Mode Controller) Type. The performances of the developed controller are evaluated and shown to be effective and useful through simulation study.

• Proceedings of the KIPE Conference
• /
• 2011.07a
• /
• pp.171-172
• /
• 2011

The paper proposes a close loop control algorithm for Z-source inverter. The algorithm is realized by PWM duty ratio control in order to improve the output voltage to it's desired level. The controller consist of the output voltage PID controller and DC input voltage P controller. Using the DQ coordinate transformation simplify the controller design. The PSIM simulation results verify the validity by means of comparing the system with or without compensation and estimating if the system has output consistency function when ZSI's load and input voltage value changing.

• Journal of Power System Engineering
• /
• v.6 no.3
• /
• pp.49-56
• /
• 2002

This study suggests a trajectory tracking controller composed of an input output linearization compensator and a linear controller. The input output linearization compensator is derived from the nonlinear equations of a pneumatic control system and it algebraically transforms a nonlinear system dynamics into a linear one, so that input output characteristics of the control system is linearized regardless of the variation of the operating point and linear control techniques can be applied. The results of nonlinear simulations show that the proposed controller tracks the given trajectories more accurately than a state feedback controller does.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.35C no.8
• /
• pp.68-74
• /
• 1998

The neural network predictiv econtroller (NNPC) is proposed for the attempt to mimic the function of brain that forecasts the future. It consists of two loops, one is for the prediction of output (NNP:neural network predictor) and the other one is for control the plant(NNC: neural network controller). The output of NNC makes the control input of plant, which is followed by the variation of both plant error and predictin error. The NNP forecasts the future output based upon the current control input and the estimated control output. The input and the output data of a system and a new method using evolution strategy are used to train the NNP. A two-step NNPC is applied to control the temeprature in boiler systems. It was compared with PI controller and auto-tuning PID controller. The computer simulaton and experimental results show that the proposed method has better performances than the other method.

• Journal of Institute of Control, Robotics and Systems
• /
• v.6 no.8
• /
• pp.689-696
• /
• 2000

In this paper we propose an eminent controller for wheeled mobile robots. This controller consists of an input-output linearization controller trying to stabilize the system and a neural network controller to compensate for uncertainties. The uncertainties are divided into two parts. First unstructured uncertainties include the elements related with system order such as friction disturbance. Second structure uncertainties are the incorrect system parameters A neural network structure of the proposed overall controller learns structural errors of the wheeled mobile robots with uncertainties and includes the neural network output. This controller learns quickly the model and has good tracking performance Simulation results show that the proposed controller is more efficient than analog controllers.

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.53 no.9
• /
• pp.569-575
• /
• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for high performance of induction motor drive. The design of this algorithm based on FNN controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control Performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation. and steady- state accuracy and transient response.

• Proceedings of the KIEE Conference
• /
• 2005.10b
• /
• pp.570-572
• /
• 2005

This paper is proposed adaptive hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.