• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.742-748
• /
• 1993

The dynamics and control of two complex column configurations (sidestream column with stripper; prcfractionater/sidestream column configuration), which are multivariable interacting and nonlinear, have been studied. A new control scheme developed by Hanand Park(1993) to deal with the nonlinear and multivariable nature of distillation processes has been applied to these complex distillation configurations. The control scheme incorporates a nonlinear wave model into a generic model control framework. An observer based on the nonlinear wave model is used to determine the profile positions of distillation column sections. The control scheme enables tight control of the profile position of each column section that leads to fast stabilization of product compositions.

• International Journal of Control, Automation, and Systems
• /
• v.5 no.6
• /
• pp.630-642
• /
• 2007

In this paper, we propose a direct model reference adaptive fuzzy control (MRAFC) for MIMO nonlinear systems whose structure is represented by the Takagi-Sugeno fuzzy model. The adaptive law of the MRAFC estimates the approximation error of the fuzzy logic system so that it provides asymptotic tracking of the reference signal for the systems with uncertain or slowly time-varying parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal. To verify the validity and effectiveness of the MRAFC scheme, the suggested analysis and design techniques are applied to the tracking control of robot manipulator and simulation studies are carried out. In the control design, the MRAFC is combined with feedforward PD control to make the actual joint trajectories of the robot manipulator with system uncertainties track the desired reference joint position trajectories asymptotically stably.

• Journal of Institute of Control, Robotics and Systems
• /
• v.7 no.11
• /
• pp.918-926
• /
• 2001

In this paper, intelligent control method using neural network as a nonlinear controller is presented. Simulation studies for three link rotary robot are performed. Neural network controller is implemented on DSP board in PC to make real time computing possible. On-line training algorithms for neural network control are proposed. As a test-bed, a large x-y table was build and interface with PC has been implemented. Experiments such as inverted pendulum control and large x-y table position control are performed. The results for different PD controller gains with neural network show excellent position tracking for circular trajectory compared with those for PD controller only. Neural control scheme also works better for controlling inverted pendulum on x-y table.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.1000-1003
• /
• 1996

Nonlinear unified predictive control(UPC) algorithm was applied to the temperature control of a batch polymerization reactor for polymethylmethacrylate(PMMA). Before the polymerization reaction is initiated, the parameters of the process model are determined by the recursive least squares(RLS) method. During the reaction, nonlinearities due to generation of heat of reaction and variation of heat transfer coefficients are predicted through the nonlinear model developed. These nonlinearities are added to the process output from the linear process model. And then, the predicted process output is used to calculate the control output sequence. The performance of nonlinear control algorithm was verified by simulation and compared with that of the linear unified predictive control algorithm. In the experiment of a batch PMMA polymerization, nonlinear unified predictive control was implemented to regulate the temperature of the reactor, and the validity of the nonlinear model was verified through the experimental results. The performance of the nonlinear controller turned out to be superior to that of the linear controller for tracking abrupt changes in setpoint.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.412-415
• /
• 1995

Previously obtained results of L$_{2}$-gain and H$_{\infty}$ control via state feedback of nonlinear systems are extended to a class of nonlinear system with uncertainties. The required information about the uncertainties is that the uncertainties are bounded in Euclidian norm by known functions of the system state. The conditions are characterized in terms of the corresponding Hamilton-Jacobi equations or inequalities (HJEI). An algorithm for finding an approximate local solution of Hamilton-Jacobi equation is given. This results and algorithm are illustrated on a numerical example..

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.5 no.2
• /
• pp.145-150
• /
• 2005

We proposed the indirect adaptive fuzzy model based sliding mode controller to control nonaffine nonlinear systems. Takagi-Sugano fuzzy system is used to represent the nonaffine nonlinear system and then inverted to design the controller at each sampling time. Also sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. The proposed controller and adaptive laws guarantee that the closed-loop system is stable in the sense of Lyapunov and the output tracks a desired trajectory asymptotically.

• Proceedings of the KIEE Conference
• /
• 2008.11b
• /
• pp.65-68
• /
• 2008

This paper presents the new adaptive optimal scheme for the nonlinear systems, which is based on the Picard's iterative approximation and Fast Walsh transform. It is well known that the Walsh function approach method is very difficult to apply for the analysis and optimal control of nonlinear systems. However, these problems can be easily solved by the improvement of the previous adaptive optimal scheme. The proposed method is easily applicable to the analysis and optimal control of nonlinear systems.

• International Journal of Control, Automation, and Systems
• /
• v.6 no.3
• /
• pp.364-377
• /
• 2008

Due to the difficulty in solving the Hamilton-Jacobi-Isaacs equation, the nonlinear optimal control approach is not very practical in general. To overcome this problem, Ezal et al. (2000) first solved a linear optimal control problem for the linearized model of a nonlinear system given in the strict-feedback form. Then, using the backstepping procedure, a nonlinear feedback controller was designed where the linear part is same as the linear feedback obtained from the linear optimal control design. However, their construction is based on the cancellation of the high order nonlinearity, which limits the application to the smooth ($C^{\infty}$) vector fields. In this paper, we develop an alternative method for backstepping procedure, so that the vector field can be just $C^1$, which allows this approach to be applicable to much larger class of nonlinear systems.

• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10a
• /
• pp.105-107
• /
• 1991

A chemical process model represented by partial differential equations was studied as one of nonlinear distributed parameter control problems. Using an optimal control theory in the form of maximum principles based on nonlinear integral equations, an algorithm to solve the problem was developed and coded into a computer program.

• 제어로봇시스템학회:학술대회논문집
• /
• 1989.10a
• /
• pp.939-942
• /
• 1989

Most of systems are included nonlinear characteristics in practice. One might be faced with difficulties when problems of nonlinear systems are solved. In this paper we present a formal linearization method of nonlinear systems by using the trigonometric Fourier expansion on the state space considering easy inversion. An error bound, an application, and a compensation of this method are also investigated.