• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.628-633
• /
• 1994

A new Riemannian geometric model for the controlled plant is proposed by imbedding the control vector space in the state space, so as to reduce the dimension of the model. This geometric model is derived by replacing the orthogonal straight coordinate axes on the state space of a linear system with the curvilinear coordinate axes. Therefore the integral manifold of the geometric model becomes homeomorphic to that of fictitious linear system. For the lower dimensional Riemannian geometric model, a nonlinear optimal regulator with a quadratic form performance index which contains the Riemannian metric tensor is designed. Since the integral manifold of the nonlinear regulator is determined to be homeomorphic to that of the linear regulator, it is expected that the basic properties of the linear regulator such as feedback structure, stability and robustness are to be reflected in those of the nonlinear regulator. To apply the above regulator theory to a real nonlinear plant, it is discussed how to distort the curvilinear coordinate axes on which a nonlinear plant behaves as a linear system. Consequently, a partial differential equation with respect to the homeomorphism is derived. Finally, the computational algorithm for the nonlinear optimal regulator is discussed and a numerical example is shown.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.160-163
• /
• 1996

This paper deals with MCMBPC(Multivariable Constrained Model Based Predictive Controller) for nonlinear boiler system with noise and disturbance. MCMBPC is designed by linear state space model obtained from some operating point of nonlinear boiler system and Kalman filter is used to estimate the state with noise and disturbance. The solution of optimization of the cost function constrained on input and/or output variables is achieved using quadratic programming, viz. singular value decomposition (SVD). The controller designed is shown to have excellent tracking performance via simulation applied to nonlinear dynamic drum boiler turbine model for 16OMW unit.

• Journal of the Korean Society for Precision Engineering
• /
• v.19 no.5
• /
• pp.168-175
• /
• 2002

The hydraulic acoustic attenuator fur an automotive active suspension system is so highly nonlinear and of high order that the analysis in time-domain has been performed quite little. In this paper, a state-space representation of the dynamics for a hydraulic attenuator was presented utilizing the electrical analogy. And the results of experiment were compared with those of simulation to validate the state-space model proposed. The comparison revealed that the state-space model proposed is practically applicable to estimate the dynamic responses of the hydraulic attenuator in time-domain.

• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.6
• /
• pp.120-127
• /
• 1995

A nonlinear empirical state-space model of the Artificial Neural Network(ANN) has been developed. The nonlinear model structure incorporates characteristic, so as to enable identification of the transient response, as well as the steady-state response of a dynamic system. A hybrid feedfoward/feedback neural network, namely a Local Time Delayed Recurrent Multi-layer Perception(RMLP), is the model structure developed in this paper. RMLP is used to identify nonlinear dynamic system in an input/output sense. The feedfoward protion of the network architecture provides with the well-known curve fitting factor, while local recurrent and cross-talk connections provides the dynamics of the system. A dynamic learning algorithm is used to train the proposed network in a supervised manner. The derived dynamic learning algorithm exhibit a computationally desirable characteristic; both network sweep involved in the algorithm are performed forward, enhancing its parallel implementation. RMLP state-space and its associate learning algorithm is demonstrated through a simple examples. The simulation results are very encouraging.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.36 no.12
• /
• pp.1152-1162
• /
• 2008

This paper deals with the State-Dependent Riccati Equation (SDRE) technique for the design of helicopter nonlinear flight controllers. Since the SDRE controller requires a linear system-like structure for nonlinear motion equations, a state-dependent coefficient (SDC) factorization technique is developed in order to derive the conforming structure from a general nonlinear helicopter dynamic model. Also on-line numerical methods of solving the algebraic Riccati equation are investigated to improve the numerical efficiency in designing the SDRE controllers. The proposed method is applied to trajectory tracking problems of the helicopter and computational tips for a real time application are proposed using a high fidelity rotorcraft mathematical model.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.3 no.4
• /
• pp.895-905
• /
• 1999

This paper identifies a linear time-invariant mathematical model of each stand of a five-stand tandem cold mill to design a robust $H_\infty$ thickness controller by applying input and output data sets to N4SID (Numerical algorithms for Subspace State Space System Identification) method. The input-output data sets describe interstand interference in the process of tandem cold rolling and are obtained from a nonlinear simulator of the tandem cold mill. In result, it is shown that the identified model well approximates the nonlinear model than a Taylor linearized model. Furthermore, uncertainties including roll eccentricity and incoming strip variation are quantitatively analyzed from the plot of maximum singular values.

• Proceedings of the KIEE Conference
• /
• 2005.10b
• /
• pp.496-498
• /
• 2005

This paper presents intelligent digital redesign method of global approach for hybrid state space fuzzy-model-based controllers. For effectiveness and stabilization of continuous-time uncertain nonlinear systems under discrete-time controller, Takagi-Sugeno(TS) fuzzy model is used to represent the complex system. And global approach design problems viewed as a convex optimization problem that we minimize the error of the norm bounds between nonlinearly interpolated linear operators to be matched. Also by using the power series, we analyzed nonlinear system's uncertain parts more precisely. When a sampling period is sufficiently small, the conversion of a continuous-time structured uncertain nonlinear system to an equivalent discrete-time system have proper reason. Sufficiently conditions for the global state-matching of the digitally controlled system are formulated in terms of linear matrix inequalities (LMIs).

• International Journal of Control, Automation, and Systems
• /
• v.4 no.6
• /
• pp.782-787
• /
• 2006

The role of Unmanned Aircraft Vehicles(UAVs) has been increasing significantly in both military and civilian operations. Many complex systems, such as UAVs, are difficult to model accurately because they exhibit nonlinearity and show variations with time. Therefore, the control system must address the issues of uncertainty, nonlinearity, and complexity. Hence, identification of the mathematical model is an important process in controller design. In this paper, attitude dynamics identification of UAV is investigated. Using the flight data, nonlinear state space model for attitude dynamics of UAV is derived and verified. Real time simulation results show that the model dynamics match experimental data.

• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.5
• /
• pp.157-165
• /
• 1995

In this paper the comparison between the neural networks and traditional approaches as nonlinear system identification methods are considered. Two model structures of neural networks are the state space model and the input output model neural networks. The traditional methods are the AutoRegressive eXogeneous Input model and the Nonlinear AutoRegressive eXogeneous Input model. Computer simulation for an analytic dynamic model of a single input single output nonlinear system has been done for all the chosen models. Model validation for the obtained models also has been done with testing inputs of the sinusoidal, ramp and the noise ramp.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.58 no.2
• /
• pp.130-135
• /
• 2009

In recent years, researches on rotary inverted pendulum control systems have been significantly focused due their highly nonlinear dynamics and complicated geometric structures. This paper presents a novel control approach for such systems by means of similarity transformation theory. At first, we represent nonlinear system dynamics to the controllability-formed state space model including a time-varying parameter vector. We establish the state-feedback control configuration based on the transformed model and derive an adaptive control law for adjusting desired characteristic equation. Numerical analysis is achieved to evaluate our control method and demonstrate its superiority by comparing it to the traditional control strategy. Furthermore, real-time control experiment is carried out to test its practical reliability.