• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.990-993
• /
• 2005

This paper presents new results on designing a robust adaptive direct controller for a class of non-linear first order systems. The designing method based on the use of dead zone in the parameters' update law. It is shown that the size of the dead zone does not depend on the upper bounds of the disturbances. That means that even if the bounds are large, the tracking error will always converge to a set of the dead zone size. However, in the ideal case, when the exogenous signal functions and the function represents un-modeled dynamics of the systems equal to zero, the proposed controller does nt mean the convergence to zero of the tracking error. Computer simulation results show the effectiveness of the controller in dealing with the stated problems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.05a
• /
• pp.144-147
• /
• 2001

This paper presents adaptive fuzzy controller which is uncertainty or unknown variation in different parameters with nonlinear system of helicopter. The proposed adaptive fuzzy controller applied TSK(Takagi-Sugeno-Kang) fuzzy system which is not only low number of fuzzy rule, and a linear input-output equation with a constant term, but also can represent a large class of nonlinear system with good accuracy. The adaptive law was designed by using Lyapunov stability theory. The adaptive fuzzy controller is a model reference adaptive controller which can adjust the parameter $\theta$ so that the plant output tracks the reference model output. First of all, system of helicopter was considered as stopping state, and design of controller was simulated from dynamics equation with stopping state. Results show that it is controlled more successfully with a model reference adaptive controller than with a non-adaptive fuzzy controller when there is a modelling error between system and model or a continuous added noise in such unstable system.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1955-1959
• /
• 2005

This paper presents a systematic analysis and a simple design of a robust adaptive control law for a class of non linear systems with modeling errors and a time-delay input. The theory for designing a robust adaptive control law based on input- output feedback linearization of non linear systems with uncertainties and a time-delay in the manipulated input by the approach of parameterized state feedback control is presented. The main advantage of this method is that the parameterized state feedback control law can effectively suppress the effect of the most parts of nonlinearities, including system uncertainties and time-delay input in the pp-coupling perturbation form and the relative order of non linear systems is not limited.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.765-768
• /
• 1993

Adaptive Control is used in order to improve close loop dynamics with a fuzzy controller when process parameters are unknown or fluctuate form an initial value. The way in which the adaptive control environment may be applied is the following. First we obtain a linear fuzzy controller. Second, we apply the adaptive rules by means of actuating directly over fuzzy variables which change their value. The techniques are based on Lyapunov functions. Third, we comment about extending this method to non-piecewise linear controllers using the contrast definition for a fuzzy controller.

• Transactions on Control, Automation and Systems Engineering
• /
• v.4 no.4
• /
• pp.277-282
• /
• 2002

This paper proposes a nonlinear adaptive controller based on back-stepping method for tracking reference substrate concentration by manipulating dilution rate in a continuous baker's yeast cultivating process in stirred tank bioreactor. Control law is obtained from Lyapunov control function to ensure asymptotical stability of the system. The Haldane model for the specific growth rate depending on only substrate concentration is used in this paper. Due to the uncertainty of specific growth rate, it has been modified as a function including the unknown parameter with known bounded values. The substrate concentration in the bioreactor and feed line are measured. The deviation from the reference is observed when the external disturbance such as the change of the feed is introduced to the system. The effectiveness of the proposed controller is shown through simulation results in continuous system.

• Transactions on Control, Automation and Systems Engineering
• /
• v.3 no.2
• /
• pp.117-123
• /
• 2001

To improve control performance of a non-linear system, many other reserches have used the sliding model control algorithm. The sliding mode controller is known to be robust against nonlinear and unmodeled dynamic terms. However, this algorithm raises the inherent chattering caused by excessive switching inputs around the sliding surface. Therefore, in order to solve the chattering problem and improve control performance, this study has developed the sliding mode controller with a perturbation estimator using the observer-based fuzzy adaptive network. The perturbation estimator based on the fuzzy adaptive network generates the control input of compensating unmodeled dynamics terms and disturbance. And the weighting parameters of the fuzzy adaptive network are updated on-line by adaptive law in order to force the estimation errors converge to zero. Therefore, the combination of sliding mode control and fuzzy adaptive network gives rise to the robust and intelligent routine. For evaluation control performance of the proposed approach, tracking control simulation is carried is carried out for the hydraulic motion simulator which is a 6-degree of freedom parallel manipulator.

• Proceedings of the KIEE Conference
• /
• 1995.07b
• /
• pp.859-862
• /
• 1995

In this paper, an adaptive nonlinear observer using new Off-line algorithm is proposed to reduce the computing time. The estimated velocity data obtained from the control scheme is more accurate than that by the normal interpolation method when the velocity to be estimated is at the low speed or the fast speed. It is also shown that the adaptive controller based on AC100/C30 is useful for implementing the real-time controller.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.506-506
• /
• 2000

To improve control performance of a non-linear system, many other researches have used the sliding mode control algorithm. The sliding mode controller is known to be robust against nonlinear and unmodeled dynamic terms. However. this algorithm raises the inherent chattering caused by excessive switching inputs around the sliding surface. Therefore, in order to solve the chattering problem and improve control performance, this study has developed the sliding mode controller with a perturbation estimator using the observer-based fuzzy adaptive network generates the control input for compensating unmodeled dynamics terms and disturbance. And, the weighting parameters of the fuzzy adaptive network are updated on-line by adaptive law in order to force the estimation errors to converge to zero. Therefore, the combination of sliding mode control and fuzzy adaptive network gives rise to the robust and intelligent routine. For evaluating control performance of the proposed approach. tracking control simulation is carried out for the hydraulic motion simulator which is a 6-degree of freedom parallel manipulator.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.37 no.12
• /
• pp.879-883
• /
• 1988

In this paper, self tuning control of interconnected systems are dealt in view point of large scale system control. The plant model is given in MIMO ARMA procss. This process is simlified as independent SISO ARMA processes having offset terma, which are considered as effects of interconnections. In each decentralized system, self tuning controller with instrumental variable method is adopted. As a result, this algorithm enables the paramter estimation to be unbiased and non-drift. This controller contains a new implicit offset rejection technique. Simulation results consider well with the analysis in case of linear interconnection.

• International journal of advanced smart convergence
• /
• v.10 no.2
• /
• pp.175-186
• /
• 2021

We have studied the speed regulation of the permanent magnet synchronous motor (PMSM) servo system in this paper. To optimize the PMSM servo system's speed-control performance with disturbances, a non-linear speed-control technique using a back-propagation neural network (BP-NN) algorithm forthe controller design of the PMSM speed loop is introduced. To solve the slow convergence speed and easy to fall into the local minimum problem of BP-NN, we develope an improved BP-NN control algorithm by limiting the range of neural network outputs of the proportional coefficient Kp, integral coefficient Ki of the controller, and add adaptive gain factor β, that is the internal gain correction ratio. Compared with the conventional PI control method, our improved BP-NN control algorithm makes the settling time faster without static error, overshoot or oscillation. Simulation comparisons have been made for our improved BP-NN control method and the conventional PI control method to verify the proposed method's effectiveness.