• Journal of the Korean Mathematical Society
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• v.50 no.1
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• pp.17-39
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• 2013

The study of nonlinear discrete-time control dynamical systems with disturbance is an important topic in control theory. In this paper, we concentrate our efforts to multiple valued iterative dynamical systems, which model the nonlinear discrete-time control dynamical systems with disturbance. After establishing the validity of such modeling, we study the invariant set theory of the multiple valued iterative dynamical systems, including the controllability/reachablity problems of the maximal invariant sets.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.807-810
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• 1997

Digital control of robot manipulator employs discrete-time robot models. It is important to explore effective discrete-time robot models and to analyze their properties in control system designs. This paper presents a new type discrete-time robot model. The model is derived by using trapezoid rule to approximate the convolution integral term, then eliminating nonlinear force terms from robot dynamical equations. The new model obtained has very simple structure, and owns the properties of independence to the nonlinear force terms. According to evaluation criteria, three aspects of the model properties: model accuracy, model validity range and model simplicity are examined and compared with commonly used discrete-time robot models. The validity of the proposed model and its advantages to control system designs are verified by simulation results.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.56-60
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• 1992

This paper addresses the problem of designing a neural network based controller for a discrete-time nonlinear dynamical system. Using two multi-layered neural networks we first design an indirect controller the weights of which are updated by the informations obtained from system identification. The weight update is executed by parameter optimization method under Lagrangian formulation. For the nonlinear dynamical system, we define several cost functions and by computer simulations analyze the control performances of them and the effects of penalty-weighting values.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1537-1547
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• 2017

In this paper, a discrete-time robust current controller is proposed for PMSM drives. The structure of the proposed controller is quite simple and does not require high computational resource. The only difference of the proposed controller from the classical dead-beat controller is the integral term which can easily be implemented in a PMSM drive. The stability analysis of the proposed controller is performed accounting in parametric uncertainties, unmodelled dynamics and disturbances in the mathematical model. The boundedness of the dynamical system and asymptotic convergence of dq-axes currents to their reference values are provided under certain conditions. Various simulation and experimental studies are performed and the results taken at different operation conditions show the validity of the proposed controller.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.804-806
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• 1995

This paper discusses Self-organized Distributed Networks(SODN) as identifier of nonlinear dynamical systems. The structure of system identification employs series-parallel model. The identification procedure is based on a discrete-time formulation. The learning with the proposed SODN is fast and precise. Such properties arc caused from the local learning mechanism. Each local networks learns only data in a subregion. Large number of memory requirements and low generalization capability for the untrained region, which are drawbacks of conventional local network learning, are overcomed in the SODN. Through extensive simulation, SODN is shown to be effective for identification of nonlinear dynamical systems.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.290-292
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• 1993

This paper investigates the identification of discrete time nonlinear system using neural networks with two hidden layers. A New learning method of both NNI and NNC is proposed. For control of the dynamical system we use two neural networks, one for identification and the other for control, and proposed NN control system is based on a framework of MRC. We define a closed loop error. In the proposed learning method, the identification error and the closed loop error are utilized to train the NNI, whareas the control error and the closed loop error are used to train the NNC, The simulation results show that the identification and control schemes suggested are practically feasible and effective.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.169-174
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• 2000

In this paper, Haar wavelet-based neural network is described for the identification and control of discrete-time nonlinear dynamical systems. Wavelets are suited to depict functions with local nonlinearities and fast variations because of their intrinsic properties of finite support and self-similarity. Due to the orthonormal properties of Haar wavelet functions, wavelet neural networks result in a greatly simplified training problem. This wavelet-based scheme performs adaptively both the identification of nonlinear functions and the control of the overall system, while the multilayer neural network is applied to the control system just after its sufficient learning of the unknown functions. Simulation shows that the wavelet network can be a good alternative to a multilayer neural network with backpropagation.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.318-323
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• 2005

In this paper, we propose a systematic method of a fuzzy-model-based controller for continuous-time nonlinear dynamical systems which may contain uncertainties. The continuous-time uncertain TS fuzzy model is first constructed to represent the uncertain nonlinear system. A parallel distributed compensation (PDC) technique is then used to design a fuzzy model based controller for both stabilization and tracking. Finally, the designed continuous-time controller is converted to an equivalent discrete-time controller by using an intelligent digital redesign method. This new design technique provides a systematic and effective framework for integration of the fuzzy model based control theory and the advanced digital redesign technique for nonlinear dynamical systems with uncertainties. Finally, the single link flexible-joint robot arm is used as an illustrative example to show the effectiveness and the feasibility of the developed design method.