• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.41 no.4
• /
• pp.21-28
• /
• 2004

This paper presents a direct multivariable self-tuning controller using neural network which adapts to the changing parameters of the higher order multivariable nonlinear system with nonminimum phase behavior, mutual interactions and time delays. The nonlinearities are assumed to be globally bounded, and a multivariable nonlinear system is divided linear part and nonlinear part. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm the computer simulation is done to adapt the multivariable nonlinear nonminimm phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct multivariable adaptive controller using neural network.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.44 no.6
• /
• pp.1-10
• /
• 2007

This paper presents a direct nonlinear multivariable self-tuning PID controller using neural network which adapts to the changing parameters of the nonlinear multivariable system with noises and time delays. The nonlinear multivariable system is divided linear part and nonlinear part. The linear controller are used the self-tuning PID controller that can combine the simple structure of a PID controllers with the characteristics of a self-tuning controller, which can adapt to changes in the environment. The linear controller parameters are obtained by the recursive least square. And the nonlinear controller parameters are achieved the through the Back-propagation neural network. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt the nonlinear multivariable system with noises and time delays and with changed system parameter after a constant time. The proposed PID type nonlinear multivariable self-tuning method using neural network is effective compared with the conventional direct multivariable adaptive controller using neural network.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.490-493
• /
• 1996

This paper presents a control scheme for the motion of a 2 DOF robot manipulator. Robot manipulators are multivariable nonlinear systems. Fuzzy logic is avaliable human-like control without complex mathematical operation and is suitable to nonlinear system control. In this paper, Implementation of fuzzy logic control of robotic manipulators shows. Algorithm has been performed with simulation packages MATRIXx and SystemBuild.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.60 no.9
• /
• pp.1754-1760
• /
• 2011

In this paper, a systematic design of a robust nonlinear multivariable variable structure controller based on state dependent nonlinear form is presented for the control of MIMO uncertain affine nonlinear systems with mismatched uncertainties and matched disturbance. After a MIMO uncertain affine nonlinear system is represented in the form of state dependent nonlinear system, a systematic design of a robust nonlinear variable structure controller is presented. To be linear in the closed loop resultant dynamics, the linear sliding surface is applied. A corresponding diagonalized control input is proposed to satisfy the closed loop global exponential stability and the existence condition of the sliding mode on the linear sliding surface, which will be investigated in Theorem 1. Through a design example and simulation study, the usefulness of the proposed controller is verified.

• Journal of the Korean Society for Precision Engineering
• /
• v.9 no.4
• /
• pp.154-166
• /
• 1992

A kinematic nonlinear multivariable launcher is modeled of which the azimuth and elevation axes are drived simultaneously and position control systems are designed for this system by the PD and LQG/LTR control methods. Also, the suitable command input fonction is suggested for the desired command following performance and the two control systems with disturbances and load variation are evaluated for the entire operating range by computer simulation. It is found that the two linear controllers can be used for the kinematic nonlinear multivariable launcher in the entire operating range and LQG/LTR controller is more effective for disturbance rejection.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1992.04a
• /
• pp.133-142
• /
• 1992

A kineamatic nonlinear multivariable laundher is modeled of which the azimoth and elevation axes are drived simultaneously and SISO and MIMO LQG/LTR controllers are designed and evaluated for this system. Also, the suitable command input function is suggested for the desired command following performance and the LQG/LTR control system with disturbances and load variation is evaluated for the entire operating range by computer simulation. It is found that the linear SISO LQG/LTR controller can be used for the kinematic nonlinear multivariable launder in the entire operating range and is effective for disturbance rejection and load variation.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.179-184
• /
• 1993

In general, for underwater vehicles in low speed, depthkeeping operations are carried out by using the variation of the weight in the seaway tank. The depthkeeping control of underwater vehicles is difficult because of the deadzone effect in the flow rate control valve. In this paper, the nonlinear multivariable QLQG/LTR control system using a seaway tank and bow planes is synthesized in order to improve the performance of the depth control system. The computer simulation results show the multivariable QLQG/LTR control system has good depth control performance under the deadzone effect.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.81-84
• /
• 1996

We propose the robust nonlinear controller design methodology, the $H_{\infty}$ constrained quasi - linear quadratic Gaussian control (QLQG/ $H_{\infty}$), for the statistically-linearized multivariable system with hard nonlinearties such as Coulomb friction, deadzone, etc. The $H_{\infty}$ performance constraint is involved in the optimization process by replacing the covariance Lyapunov equation with the Riccati equation whose solution leads to an upper bound of the QLQG performance. Because of the system's nonlinearity, however, one equation among three Riccati equations contain the nonlinear correction terms that are very difficult to solve numerically. To treat this problem, we use simple algebraic techniques. With some analytic transformation for Riccati equations, the nonlinear correction terms can be so eliminated that the set of a linear controller to the different operating points are designed. Synthesizing these via inverse random input describing function (IRIDF) technique, the final nonlinear controller can be designed.

• 제어로봇시스템학회:학술대회논문집
• /
• 1988.10a
• /
• pp.192-196
• /
• 1988

This paper presents a feasibility study related to the design of a linear multivariable compensator for a model of boiler-turbine system. The nonlinear dynamics are linearized at a operating condition. At the operating point an LQG/LTR compensator is designed. Simulations are included to illustrate the usefulness of this linear multivariable control law.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.12
• /
• pp.128-141
• /
• 1998

We propose the robust nonlinear controller design methodology for the multivariable system which has hard nonlinearities (Coulomb friction, dead-zone, etc) and the structured real parameter uncertainty. The hard nonlinearity can be linearized by the RIDF technique and structured real parameter uncertainty can be modelled as the sense of Peterson-Hollot's quadratic Lyapunov bound. For this system, we apply the robust QLQG/H$_{\infty}$ control and then can obtain four Riccati equations. Because of the system's nonlinearity, however, one Riccati equation contains the nonlinear correction term that is very difficult to solve numerically, In order to treat this problem, using some transformations to Riccati equations, the nonlinear correction term can be eliminated. Then, only two Riccati equations need to design a controller. Finally, the robust nonlinear controller is synthesized via IRIDF techniques. To test this proposed control method, we consider the direct-drive robot manipulator system that has Coulomb frictions and varying inertia.