• Proceedings of the KSME Conference
• /
• 2001.06b
• /
• pp.253-258
• /
• 2001

This paper introduces a simple nonlinear adaptive control method for pipeline inspection gauge (PIG) flow in natural gas pipeline. The dynamic behavior of the PIG depends on the different pressure across its body and the bypass flow through it. The system dynamics includes: dynamics of driving gas flow behind the PIG, dynamics of expelled gas in front of the PIG, and dynamics of the PIG. The method of characteristics (MOC) and Runger-Kuta method are used to solve the dynamics of flow. The PIG velocity is controlled through the amount of bypass flow across its body. A simple nonlinear adaptive controller based on the backstepping method is introduced. To derive the controller, three system parameters should be measured: the PIG position, its velocity and the velocity of bypass flow across the PIG body. The simulation has been done with a pipeline segment in the KOGAS low pressure system, Ueijungboo-Sangye line to verify the effectiveness of the proposed controller. Three cases of interest are considered: the PIG starts to move at its launcher, the PIG arrives at its receiver and the PIG restarts after stopping in the pipeline by obstruction. The simulation results show that the proposed nonlinear adaptive controller attained good performance and can be used for controlling the PIG velocity.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.50 no.1
• /
• pp.14-21
• /
• 2001

This paper proposes an indirect adaptive fuzzy controller for general SISO nonlinear systems. In indirect adaptive fuzzy control, based on the proved approximation capability of fuzzy systems, they are used to capture the unknown nonlinearities of the plant. Until now, most of the papers in the field of controller design for nonlinear system considers the affine system using fuzzy systems which have fixed grid-rule structure. We proposes a dynamic fuzzy rule insertion scheme where fuzzy rule-base grows as time goes on. With this method, the dynamic order of the controller reduces dramatically and an appropriate number of fuzzy rules are found on-line. No a priori information on bounding constants of uncertainties including reconstruction errors and optimal fuzzy parameters is needed. The control law and the update laws for fuzzy rule structure and estimates of fuzzy parameters and bounding constants are determined so that the Lyapunov stability of the whole closed-loop system is guaranteed.

• Journal of Institute of Control, Robotics and Systems
• /
• v.15 no.4
• /
• pp.413-419
• /
• 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 the Korean Institute of Intelligent Systems
• /
• v.23 no.6
• /
• pp.494-499
• /
• 2013

Based on the state-feedback adaptive neuro-control algorithm for a SISO nonaffine pure-feedback nonlinear system proposed in [15], an output-feedback controller is proposed in this paper. The output-feedback adaptive neural-net controller for the considered nonlinear system has not been previously proposed in any other literatures yet. The proposed output-feedback controller inherits all the advantages of [15] such that it does not adopt backstepping and this results in relatively simple control and adapting laws. Only one neural network is required for the proposed adaptive controller. The proposed neural-net control scheme expands the applicable class of nonlinear systems.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.23 no.11
• /
• pp.9-21
• /
• 2009

This paper proposes the alternative observer and controller design scheme based on T-S fuzzy model. Nonlinear systems are represented by fuzzy models since fuzzy logic systems are universal approximators. In order to estimate the unmeasurable states of a given unknown nonlinear system, T-S fuzzy modeling method is applied to get the dynamics of an observation system. T-S fuzzy system uses the linear combination of the input state variables and the modeling applications of them to various kinds of nonlinear systems can be found. The proposed indirect adaptive fuzzy observer based on T-S fuzzy model can cope with not only unknown states but also unknown parameters. The proposed controller is based on a simple output feedback method. Therefore, it solves the singularity problem, without any additional algorithm, which occurs in the inverse dynamics based on the feedback linearization method. The adaptive fuzzy scheme estimates the parameters and the feedback gain comprising the fuzzy model representing the observation system. In the process of deriving adaptive law, the Lyapunov theory and Lipchitz condition are used. To show the performance of the proposed observer and controller, they are applied to an inverted pendulum on a cart.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.19 no.2
• /
• pp.241-250
• /
• 2010

A recurrent adaptive model-free intelligent control with a friction estimation law is proposed to enhance the positioning performance of the mover in PMLSM system. For the PMLSM with nonlinear friction and uncertainty, an adaptive recurrent fuzzy neural network(ARFNN) and compensated control law in $H_{\infty}$ performance criterion are designed to mimic a perfect control law and compensate the approximated error between ideal controller and ARFNN. Combined with friction observer to estimate nonlinear friction parameters of the LuGre model, on-line adaptive laws of the controller and observer are derived based on the Lyapunov stability criterion. To analyze the effectiveness our control scheme, some simulations for the PMLSM with nonlinear friction and uncertainty were executed.

• Proceedings of the Korean Society of Marine Engineers Conference
• /
• 2006.06a
• /
• pp.85-86
• /
• 2006

In this paper, a nonlinear controller based on adaptive sliding-mode method which has a sliding surface vector including new boundary function is proposed and applied to a two-wheeled voiding mobile robot (WMR). This controller makes the welding point of WMR achieve tracking a reference point which is moving on a smooth curved welding path with a desired constant velocity. The mobile robot is considered in view of a kinematic model and a dynamic model in Cartesian coordinates. The proposed controller can overcome uncertainties and external disturbances by adaptive sliding-mode technique. To design the controller, the tracking error vector is defined, and then the new sliding is proposed to guarantee that the error vector converges to zero asymptotically. The stability of the dynamic system will be shown through the Lyapunov method. The simulations is shown to prove the effectiveness of the proposed controller.

• Journal of Electrical Engineering and information Science
• /
• v.2 no.6
• /
• pp.82-89
• /
• 1997

In this paper, an adaptive speed controller with an FNN(Feedforward Neural Network) is proposed for servo motor drives. Generally, the motor system has nonlinearities in friction, load disturbance and magnetic saturation. It is necessary to treat the nonlinearities for improving performance in servo control. The FNN can be applied to control and identify a nonlinear dynamical system by learning capability. In this study, at first, a robust speed controller is developed by Lyapunov stability theory. However, the control input has discontinuity which generates an inherent chattering. To solve the problem and to improve the performances, the FNN is introduced to convert the discontinuous input to continuous one in error boundary. The FNN is applied to identify the inverse dynamics of the motor and to control the motor using coordination of feedforward control combined with inverse motor dynamics identification. The proposed controller is developed for an SR motor which has highly nonlinear characteristics and it is compared with an MRAC(Model Reference Adaptive Controller). Experiments on an SR motor illustrate te validity of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1267-1271
• /
• 2003

This paper proposes an indirect adaptive fuzzy controller for general SISO nonlinear systems. No a priori information on bounding constants of uncertainties including reconstruction errors and optimal fuzzy parameters is needed. The control law and the update laws for fuzzy rule structure and estimates of fuzzy parameters and bounding constants are determined so that the Lyapunov stability of the whole closed loop system is guaranteed. The computer simulation results for an inverted pendulum system show the performance of the proposed robust adaptive fuzzy controller.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.236-236
• /
• 2000

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC systems is their robustness with respect to a model mismatch and disturbances. But it was difficult to apply for nonlinear systems. Adaptive Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to identify a nonlinear dynamical systems. In this paper, we propose new IMC design method using adaptive neuro-fuzzy inference system for nonlinear plant. Numerical simulation results show that proposed IMC design method has good performance than classical PID controller.