• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.36 no.9
• /
• pp.670-677
• /
• 1987

The robustness and stability properties for a model reference adaptive control system with plant uncertainty are considered in this paper, using input-output stability theory. An error model for a typical adaptive control structure is extended to unmodelled dynamics in the plant model and then, the strictly positive real condition for global stability is examined. In general, since this condition can be easily violated due to unmodelled dynamics, a modified compensator which can be guaranteed Hev e SPR is introduced in the plant model and the effectiveness for the given structure is also given.

• 제어로봇시스템학회:학술대회논문집
• /
• 1988.10a
• /
• pp.255-260
• /
• 1988

A robust adaptive control method for a stable nonminimum phase SISO system with unmodelled dynamics is proposed. The robust parameter estimation method of the system with bounded output noise and unmodelled dynamics is employed and a parallel structure is proposed to improve the robustness of adaptive control system. The local stability of the proposed system is shown. Computer simulations are done in order to compare the performance of the proposed structure with the basic structure on various circumstance.

• Journal of Institute of Control, Robotics and Systems
• /
• v.2 no.1
• /
• pp.60-66
• /
• 1996

Model Based Predictive Control(MBPC) has been widely used in predictive control since 80's. GPC[1] which is the superset of many MBPC strategies a popular method, but GPC has some weakness, such as insufficient stability analysis, non-applicability to internally unstable systems. However, CRHPC[2] proposed in 1991 overcomes the above limitations. So we chose RHPC based on CRHPC for electric furnace control. An electric furnace which has nonlinear properties and large time delay is difficult to control by linear controller because it needs nearly perfect modelling and optimal gain in case of PID. As a result, those controls are very time-consuming. In this paper, we applied RHPC with equality constraint to electric furnace. The reults of experiments also include the case of RHPC with monotonic weighting improving the transient response and including unmodelled dynamics. So, This paper proved the practical aspect of RHPC for real processes.

• Journal of the Korean Society for Precision Engineering
• /
• v.23 no.12 s.189
• /
• pp.46-55
• /
• 2006

A robust motion controller based on self-organizing fuzzy control(SOFC) and feed-back tracking control technique is proposed for a two-wheel driven mobile robot. The feed-back control technique of the controller guarantees the robot follows a desired trajectory. The SOFC technique of the controller deals with unmodelled dynamics of the vehicle and uncertainties. The computer simulations are carried out to verify the tracking ability of the proposed controller with various driving situations. The results of the simulations reveal the effectiveness and stability of the proposed controller to compensate the unmodelled dynamics and uncertainties.

• Journal of Ocean Engineering and Technology
• /
• v.3 no.2
• /
• pp.559-559
• /
• 1989

This paper deals with the robust controller design of robot manipulator to track a desired trajectory in spite of the presence of unmodelled dynamics in cause of nonlinearity and parameter uncertainty. The approach follwed in this paper is based on model reference adaptive control technique and convergence on hyperstability theory but it does away with the assumption that process is characterized by a linear model remaining time invariant during adaptation process. The performance of controller is demonstrated by computed simulation about position and speed control of six link manipulator in case of disturbance and payload variation.

• Journal of Ocean Engineering and Technology
• /
• v.3 no.2
• /
• pp.59-69
• /
• 1989

This paper deals with the robust controller design of robot manipulator to track a desired trajectory in spite of the presence of unmodelled dynamics in cause of nonlinearity and parameter uncertainty. The approach follwed in this paper is based on model reference adaptive control technique and convergence on hyperstability theory but it does away with the assumption that process is characterized by a linear model remaining time invariant during adaptation process. The performance of controller is demonstrated by computed simulation about position and speed control of six link manipulator in case of disturbance and payload variation.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.36 no.10
• /
• pp.748-754
• /
• 1987

A robustness analysis for model reference adaptive control(MRAC) system with plant uncertainty is discussed in this paper. The adaptive control system is designed under assumptions that the controlled plant is represented by a lst order nominal model and that the system is drived by a constant reference signal. When using general gradient method(GGM), it is shown that unmodelled dynamics in plant model can cause the instability of the overall control loop during the adaptation process. However, as the algorithm of least square method(LSM) is introduced, the global stability of the system can be hold. And it is also given that the boundedness of adjustable parameters may be verified using the concept of an equilibrium point analysis.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.37 no.10
• /
• pp.740-747
• /
• 1988

This paper describes the parameter convergence property for an adaptive identifier and deals with the stability of the adaptive system in terms of the general error model. The Persistent Excitation (PE) condition to guarantee parameter convergence is derived using the Power Spectrum Analysis. In the adaptive identifier designed under the assumptions that the plant has not unmodelled dynamics, it can be shown that the equilibrium points of adjustable parameters are independent on the position or the number of input spectrums, if the adaptive signal is PE. When the plant contains unmodelled dynamics and the same controller is used, the PE condition can still hold but the parameter tuned values are changed with the spectrum.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.37 no.5
• /
• pp.311-318
• /
• 1988

An adaptive control problem for a plant with unmodelled dynamics is studied. The upper bounds of the output due to the unmodelled dynamics and measurement noise is assumed to be known. This problem was studied by Middleton et al. They used a modified form of the least square method in estimating the plant paramaters. In this paper, the bounds of the parameters are estimated by linear programming and the parameters are estimated by the projetion type algorithm incorporating the bounds of the parameters. The stability of the proposed adaptive control system can be proved in nearly the same way as the proof of Middleton. The performance of the proposed method is found to be much better than the method of Midlleton's with respect to the parameter convergence and plant output in computer simulations.

• Proceedings of the KIEE Conference
• /
• 1987.07a
• /
• pp.144-147
• /
• 1987

An adaptive control algorithm for a plant with unmodelled dynamics is proposed. The upper bounds of the output due to the unmodelled dynamics and measurement noise is assumed to be known. Linear programming is used in estimating the bounds of plant parameters. Projection type algorithm is used in estimating the plant parameter with these bounds. This algorithm is nearly the same as those proposed by Kreisselmeier or Middleton except that the bounds are computed by linear programming. The stability of the proposed algorithm Can be proved in nearly the same way as that of Middleton. Simulation results show that the proposed algorithm gives better parameter convergence and smaller overshoot in the plant output than the algorithm without computing the bounds of plant parameters by linear programming.