• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.108-114
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• 2002

This paper describes the design of a robust adaptive controller for a nonlinear dynamical system with unknown nonlinearities. These unknown nonlinearities are approximated by multilayered neural networks (MNNs) whose parameters are adjusted on-line, according to some adaptive laws far controlling the output of the nonlinear system, to track a given trajectory. The main contribution of this paper is a method for considering input constraint with a rigorous stability proof. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control algorithm based on the adaptive MNN model. An overall control system guarantees that the tracking error converges at about zero and that all signals involved are uniformly bounded even in the presence of input saturation. Theoretical results are illustrated through a simulation example.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.27-33
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• 1999

In order to regulate cutting force at a desired level during NC lathe process, a feedrate override Adaptive Control Constraint system was developed. Nonlinear model of the cutting process was linearized as an adaptive model with a time varing process parameter. Performance of the ACC system was confirmed on the NC lathe equipped with the developed NC system through a large amount of experiment.

• Transactions of the Korean Society of Mechanical Engineers
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• v.10 no.3
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• pp.376-382
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• 1986

Adaptive control constraint (ACC) is applied to a turning process to keep the cutting force constant while the cutting conditions vary. In this system, a given reference force is compared with the measured cutting force and difference is input to the controller to adjust the feed. Since it is found that the effective ACC loop gain depends on both depth-of-cut and spindle speed and thereby influence the system stability, a simple computer algorithm is built in the controller to maintain the stability of the whole system by on-line estimation of the process parameters during cutting.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.5 no.2
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• pp.63-72
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• 1996

In order to regulate the cutting force at a desired level during peripheral end milling processes a feedrate override Adaptive Control Constraint (ACC) system was developed. The feedrate override function was accomplished through a development of programmable machine controller (PMC) interface technique on the NC controller, Nonlinear model of the cutting process was linearized as an adaptive model with a time varying process parameter. An integral type estimator was introduced for on-line estimation of the cutting process parameter, Zero order hold digital control methodology which uses pole-assignment concept for tuning of PI controllers was applied for the ACC system. Performance of the ACC system wsa confirmed on the vertical machining center equipped with fanuc OMC through a large amount of experiment.

• Transactions of the Korean Society of Mechanical Engineers
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• v.10 no.6
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• pp.889-897
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• 1986

The purpose of this study is to describe the strategy of machining process suitable for developing adaptive control with constraint of NC-machine tool. The algorithm that controls machining process parameters of every sampling time is established for the constraint of torque in machinig center. To prove this AC algorithm, manual AC-unit control test is used for simulating the on-line AC strategy control. Also machining tests are carried out on a CNC-machining center fitted with the ACC system and compared with the simulated results. The practical effectiveness of the ACC systems so discussed and the reduction of machining time are demonstrated with reference to typical models of cutting workpieces. As a typical model, taper and step geometry model are selected. The computer simulation results have a good agreement with the experimental observation and make it possible to develope a NC-machine tool with an on-line ACC system.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.22-25
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• 1995

This paper shows adaptive control using RHPC(Receding Horizon Predictive Control) with equality constraint which applied to Electric Furnace. The control strategy includes monotonic weighting (improving transient response) and pre-filtering (enhancing robustness), which is effective on real process. We can observe the performance of RHPC and confirm the practical aspect of RHPC with unmodelled dynamics through the experiment of Electric Furnace. Finally, this paper verifies the feasibility of RHPC to real process.

• Journal of the Korea Society for Simulation
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• v.5 no.1
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• pp.91-106
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• 1996

Control Variate (CV) is very useful technique for variance reduction in a wide class of queueing network simulations. However, the loss in variance reduction caused by the estimation of the optimum control coefficients is an increasing function of the number of control variables. Therefore, in some situations, it is required to select an optimal set of control variables to maximize the variance reduction . In this paper, we develop the Adaptive Control Variates (ACV) method which selects an optimal set of control variates during the simulation adatively. ACV is useful to maximize the simulation efficiency when we need iterated simulations to find an optimal solution. One such an example is the Simulated Annealing (SA) because, in SA algorithm, we have to repeat in calculating the objective function values at each temperature, The ACV can also be applied to the queueing network optimization problems to find an optimal input parameters (such as service rates) to maximize the throughput rate with a certain cost constraint.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.10
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• pp.902-911
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• 2012

This paper proposes a robust adaptive fuzzy backstepping control scheme for trajectory tracking of an electrically driven nonholonomic mobile robot with uncertainties and actuator dynamics. A complete model of an electrically driven nonholonomic mobile robot described in this work includes all models of the uncertain robot kinematics with a nonholonomic constraint, the uncertain robot body dynamics with uncertain frictions and unmodeled disturbances, and the uncertain actuator dynamics with disturbances. The proposed control scheme uses the backstepping control approach through a kinematic controller and a robust adaptive fuzzy velocity tracking controller. The presented control scheme has a voltage control input with an auxiliary current control input rather than a torque control input. It has two FBFNs(Fuzzy Basis Function Networks) to approximate two unknown nonlinear robot dynamic functions and a robust adaptive control input with the proposed adaptive laws to overcome the uncertainties such as parameter uncertainties and external disturbances. The proposed control scheme does not a priori require the accurate knowledge of all parameters in the robot kinematics, robot dynamics and actuator dynamics. It can also alleviate the chattering of the control input. Using the Lyapunov stability theory, the stability of the closed-loop robot control system is guaranteed. Simulation results show the validity and robustness of the proposed control scheme.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1655-1658
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• 1991

This paper considers the design and analysis of one-step ahead optimal and adaptive controllers, under the restriction that a known constraint on the input amplitude is imposed. It is assumed that the discrete-time single-input, single-output system to be controlled is linear, except for inequality constraints on the input. The objective function to be minimized is an one-step quadratic function, where polynomial weights on the input and output are included. Both the known parameter and unknown parameter (indirect adaptive controller) cases are examined.