• Journal of the Korean Society of Industry Convergence
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• v.18 no.3
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• pp.181-190
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• 2015

This paper presents a new approach to control the position of robot arm in workspace a robot manipulator under unknown system parameters and bounded disturbance inputs. To control the motion of the manipulator, an inverse dynamics control scheme was applied. Since parameters of the robot arm such as mass and inertia are not perfectly known, the difference between the actual and estimated parameters was considered as a external disturbance force. To identify the known parameters, an improved robust control algorithm is directly derived from the Lyapunov's Second Method. A robust control algorithm is devised to counteract the bounded disturbance inputs such as contact forces and disturbing forces coming from the difference between the actual and the estimated system parameters. Numerical examples are shown using SCARA arm with four joints.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.4
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• pp.297-306
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• 2007

Model reference control is control method such that overall response of a plant plus controller approaches that of a given reference model. The reference model provides desired trajectory the plant should follow. There are many kinds of control methods in MRC. However, this paper focuses on Model Reference Sliding Mode Control. The plant of these controls is an uncertain and linear system varying in time, of second order, and with SISO. In this paper, a design scheme of reference model is proposed for MRSMC. The scheme determines reference model based on the information on bounded control inputs, reference inputs and system parameters. It is used to choose a Fixed Reference Model in the process of controller design, to update Variable Reference Model when stepwise reference inputs change and to update Instant Reference Model at every sampling time. The simulation results show that the proposed method yields better control performance than the conventional MRC subject to the stepwise reference input when applied to the position control system for motor system.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.413-419
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• 2009

A decentralized adaptive control method is proposed for large-scale systems with unknown time-delayed nonlinear interconnections unmatched in control inputs. It is assumed that the time-delayed interaction terms are bounded by unknown nonlinear bounding functions. The nonlinear bounding functions and uncertain nonlinear functions of large-scale systems are compensated by the function approximation technique using neural networks. The dynamic surface control method is extended to design the proposed memoryless local controller for each subsystem of uncertain nonlinear large-scale time delay systems. Therefore, although the interconnected systems consist of a large number of subsystems, the proposed controller can be designed simply. We prove that all the signals in the total closed-loop system are semiglobally uniformly bounded and the control errors converge to an adjustable neighborhood of the origin. Finally, an example is given to demonstrate the effectiveness and applicability of the proposed scheme.

• Journal of Mechanical Science and Technology
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• v.20 no.6
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• pp.759-769
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• 2006

This paper proposes a new method of discretization for nonlinear systems using a Taylor series expansion and the zero-order hold assumption. The method is applied to sampled-data representations of nonlinear systems with input time delays. The delayed input varies in time and its amplitude is bounded. The maximum time-delayed input is assumed to be two sampling periods. The mathematical expressions of the discretization method are presented and the ability of the algorithm is tested using several examples. A computer simulation is used to demonstrate that the proposed algorithm accurately discretizes nonlinear systems with variable time-delayed inputs.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.172.1-172
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• 2001

In this paper we show the stability analysis of the discrete nonlinear system with average bounded variation of the input. This is the discrete counterpart of that continuous one. We use the Lyapunov stability to prove the boundedness of the steady-state error. Also the allowable maximum variation bounds and the region of attraction are given as the function of the system parameters. Moreover, we prove the uniform convergence for the constant input.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.7
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• pp.87-97
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• 1992

Fuzzy controllers have been successfully applied to many cases to which conventional control algorithms are difficult to be applied. Even though the representations and the processings of data and information in the fuzzy controller are quite different from those in other control algorithms, the information processing operation that it caries out is basically a function ∫: $A{\subset}R^n{\to}R^m$, from a bounded subset A of an n-dimensional Euclidean space to a bounded subset f[A] of an m-dimensional Euclidean space, where n and m are the number of measured states and the number of control inputs of the controlled system, respectively. Under the assumptions of Mamdani's direct reasoning method and C.O.G.(center of gravity) defuzzification method, the fuzzy controllers are proven to perform the mapping of any given functions f with appropriately defined fuzzy sets. The mapping capabilities of fuzzy controllers are analyzed in detail for two cases, ∫: $R^{1}{\to}R^{1}$ and g: $R^{2}{\to}R^{1}$. Also, it will be shown that the results can be extended to multiple dimensional cases.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.181-184
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• 1996

This paper deals with the design problem of model reference adaptive controllers for MIMO plants with unknown orders. A design scheme for an adaptive control system based on CGT theorem, which has hierarchical structures derived from backstepping strategies, is proposed for MIMO plants with unknown orders but with known relative MacMillan degrees(relative degrees for SISO plants). It is also shown that all the signals in the resulting control system are bounded, and that the asymptotic tracking is achieved in the case where reference inputs are step.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.1
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• pp.42-47
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• 2014

This paper investigates an adaptive approximation design problem for the tracking control of output-constrained non-affine pure-feedback systems. To satisfy the desired performance without constraint violation, we employ a barrier Lyapunov function which grows to infinity whenever its argument approaches some limits. The main difficulty in dealing with pure-feedback systems considering output constraints is that the system has a non-affine appearance of the constrained variable to be used as a virtual control. To overcome this difficulty, the implicit function theorem and mean value theorem are exploited to assert the existence of the desired virtual and actual controls. The function approximation technique based on adaptive neural networks is used to estimate the desired control inputs. It is shown that all signals in the closed-loop system are uniformly ultimately bounded.

• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.265-277
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• 1999

Classical and inverse estimation methods are two well known methods in statistical calibration problems. When there are outliers, both methods have large MSE's and could not estimate the input value correctly. We suggest median calibration estimation based on the LD-statistics. To investigate the robust performances, the influence function of the median calibration estimator is calculated and compared with other methods. When there are outliers in the response variables, the influence function is found to be bounded. In simulation studies, the MSE's for each calibration methods are compared. The estimated inputs as well as the performance of the influence functions are calculated.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.8
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• pp.1-14
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• 1996

Design of a reliable control systems is investigated for a class of uncertain linear plants. The uncertainty considered here is for the ase of uncertainty in the system matrix. A decentralized control scheme with two observer-based feedback controllers is developed, and it is shown that the resulting closed-loop system is reliable in the sense that the control scheme provides guaranteed stability and $H_{\infty}$-norm bounded performance in the event of sensor and/or actuator failures as well as in the presence of parameter uncertainties. We observed that soft-type failures were additional exogenous inputs to the closed-loop system. As a results, the sensor and/or actuator failures can be tolerated in the design, which is achieved by extending the methodology developed in.