• Journal of Power Electronics
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• v.15 no.6
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• pp.1619-1627
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• 2015

This paper presents a power system harmonic elimination using the mixed adaptive linear neural network and variable step-size leaky least mean square (ADALINE-VSSLLMS) control algorithm based active power filter (APF). The weight vector of ADALINE along with the variable step-size parameter and leakage coefficient of the VSSLLMS algorithm are automatically adjusted to eliminate harmonics from the distorted load current. For all iteration, the VSSLLMS algorithm selects a new rate of convergence for searching and runs the computations. The adopted shunt-hybrid APF (SHAPF) consists of an APF and a series of 7^th tuned passive filter connected to each phase. The performance of the proposed ADALINE-VSSLLMS control algorithm employed for SHAPF is analyzed through a simulation in a MATLAB/Simulink environment. Experimental results of a real-time prototype validate the efficacy of the proposed control algorithm.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.79-82
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• 2002

In this paper, we propose an adaptive repetitive control algorithm for the system the task of which is repetitive. The feedforward controller in the repetitive control system is modified by using the system parameter identifier in order to improve the convergence characteristics. The proposed algorithm is applied to the tracking control of a linear BLDC motor to which a periodic reference input is applied. It is illustrated by simulation results that the proposed adaptive repetitive control method yields better control performance than existing repetitive control even when modeling errors exist.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.148-157
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• 1996

Availability of input trajectories corresponding to desired output trajectories is often important in designing control systems for batch and other transient processes. In this paper, we propose a predictive control-type model-based iterative learning algorithm which is applicable to finding the nominal input trajectories of a linear time-invariant batch process. Unlike the other existing learning control algorithms, the proposed algorithm can be applied to nonsquare systems and has an ability to adjust noise sensitivity as well as convergence rate. A simple model identification technique with which performance of the proposed learning algorithm can be significantly enhanced is also proposed. Performance of the proposed learning algorithm is demonstrated through numerical simulations.

• Nuclear Engineering and Technology
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• v.52 no.3
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• pp.552-559
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• 2020

This paper presents an observer-based chattering free robust optimal control scheme to regulate the total power of a nuclear reactor. The non-linear model of nuclear reactor is linearized around a steady state operating point to obtain a linear model for which an optimal second order integral sliding mode controller is designed. A second order integral sliding mode observer is also designed to estimate the unmeasurable states. In order to avoid the chattering effect, the discontinuous input of both observer and controller are designed using the super-twisting algorithm. The proposed controller is realized by combining an optimal linear tracking controller with a second order integral sliding mode controller to ensure minimum control effort and robustness of the closed-loop system in the presence of uncertainties. The condition for the selection of gains of discontinuous control based on the super-twisting algorithm is derived using a strict Lyapunov function. Performance of the proposed observer based control scheme is demonstrated through non-linear simulation studies.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.4
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• pp.730-738
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• 2007

As well known when the linear machine is operated between two points repeatedly under positioning control, there are various positioning error at the moment of zero speed owing to the non-linear disturbance like as unpredictable friction force. To remove this positioning error, a simple least order disturbance observer is introduced and is actually implemented in this study. Due to this simple algorithm the over-all machine system can be modified to simple arbitrary given one-mass load without any disturbance. So, the total construction process for positioning control system is much easier than old one. Moreover, to generate a proper effective position profile with the limited actual machine force, a very powerful on-line mass identification algorithm using the load force estimator is presented. In the proposed mass identification algorithm, the exact load mass can be calculated during only one moving stage under a normally generated position profile. All presented algorithm is verified with experimental result with commercial linear servo machine system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.697-702
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• 2009

Filtered-x LMS algorithm maybe the most popular control algorithm used in DSP implementations of active noise and vibration control system. The algorithm converges on a timescale comparable to the response time of the system to be controlled, and is found to be very robust. If the pure tone reference signal is synchronously sampled, it is found that the behavior of the adaptive system can be completely described by a matrix of linear, time invariant, transfer functions. This is used to explain the behavior observed in simulations of a simplified single input, single output adaptive system, which retains many of the properties of the multichannel algorithm.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.3
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• pp.301-309
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• 2003

The non linear control algorithm with actuator saturation for a randomly excited oscillator has been widely explored and has shown promising results, but the probabilistic analysis of the algorithm has been rarely made due to its non-linear nature and the fact that the analytical solution of probability density function (PDF) for controlled responses does not exist. In this paper, a method for the probabilistic analysis on the non linear control algorithm with actuator saturation is proposed based on the equivalent non linear system method. Numerical examples are given to verify the approximation solution of PDF comparing to a statistically obtained PDF using a Gaussian white noise and a Kanai - Tagimi filtered Gaussian white noise.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.56.5-56
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• 2002

This paper studies on the feedback linearization of the looper system for hot strip mills, where the looper system plays an important role in regulating the strip tension. Firstly, nonlinear dynamic equations of the looper system are simply introduced. Secondly, using the static feedback linearization algorithm, a linear model of the looper system is obtained, of which usefulness is validated from comparison between the linear model and the nonlinear model, and design of LQI(Linear Ouadratic Integral optimal control) and ILQ (Inverse Linear Quadratic optimal control) looper control systems. In result, it is shown that the linear looper model by the feedback linearization well describes nonlin...

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.7
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• pp.1411-1417
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• 2009

In this paper, we consider the design of $H_{\infty}$ control of linear discrete-time systems having no mathematical model. The basic approach is to use Q-learning which is a reinforcement learning method based on actor-critic structure. The model-free control design is to use not the mathematical model of the system but the informations on states and inputs. As a result, the derived iterative algorithm is expressed as linear matrix inequalities(LMI) of measured data from system states and inputs. It is shown that, for a sufficiently rich enough disturbance, this algorithm converges to the standard $H_{\infty}$ control solution obtained using the exact system model. A simple numerical example is given to show the usefulness of our result on practical application.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.530-535
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• 2004

This paper considers a state set estimation (SSE) based model predictive control (MPC) for linear parameter- varying (LPV) systems with input constraint. We estimate, at each time instant, a feasible set of all states which are consistent with system model, measurements and a priori information, rather than the state itself. By combining a state-feedback MPC and an SSE, we design an SSE-based MPC algorithm that stabilizes the closed-loop system. The proposed algorithm is solved by semi-de�nite program involving linear matrix inequalities. A numerical example is included to illustrate the performance of the proposed algorithm.