• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1981-1986
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• 2004

In this study, the basic motor control system had been investigated. The controller for this study consists of two main parts, a feedforward controller part and a feedback controller part. Each part will deals with different control problems. The feedback controller deals with robustness and stability, while the feedforward controller deals with response speed. The feedforward controller, used to solve the tracking control problem, is adaptable. To make such a tracking perfect, an adaptive law based on Feedback Error Learning (FEL) is designed so that the feedforward controller becomes an inverse system of the controlled plant. The novelty of FEL method lies in its use of feedback error as a teaching signal for learning the inverse model. The theory in $H^{\infty}$-Control is selected to be applied in the feedback part to guarantee the stability and solve the robust stabilization problems. The simulation of each individual part and the integrated one are taken to clarify the study.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.11
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• pp.891-895
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• 2001

An alternative inverse feedback structure for adaptive active control of periodic noise is introduced for systems with nonminimum phase cancellation path. To obtain the inverse model of the nonminimum phase cancellation path, the cancellation path model can be factorized into a minimum phase term and a maximum phase term. The maximum phase term containing unstable zeros makes the inverse model unstable. To avoid the instability, we alter the inverse model of the maximum phase system into an anti-causal FIR one. An LMS predictor estimates the future samples of the noise, which are necessary for causality of both anti-causal FIR approximation for the stable inverse of the maximum phase system and time-delay existing in the cancellation path. The proposed method has a faster convergence behavior and a better transient response than the conventional filtered-x LMS algorithms with the same internal model control structure since a filtered reference signal is not required. We compare the proposed methods with the conventional methods through simulation studies.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.437-442
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• 2000

An alternative inverse feedback structure for adaptive active control of periodic noise is introduced for systems with nonminimum phase cancellation path. To obtain the inverse model of the nonminimum phase cancellation path, the cancellation path model can be factorized into a minimum phase term and a maximum phase term. The maximum phase term containing unstable zeros makes the inverse model unstable. To avoid the instability, we alter the inverse model of the maximum phase system into an anti-causal FIR one. An LMS predictor estimates the future samples of the noise, which are necessary for causality of both anti-causal FIR approximation for the stable inverse of the maximum phase system and time-delay existing in the cancellation path. The proposed method has a faster convergence behavior and a better transient response than the conventional FX-LMS algorithms with the same internal model control structure since a filtered reference signal is not required. We compare the proposed methods with the conventional methods through simulation studies.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.2
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• pp.67-72
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• 1996

This paper presents a parameter adaptive control law that stabilizes and asymptotically regulates any single-input, linear time-invariant, controllable and observable, discrete-time system when only the upper bounds on the order of the system is given. The algorithm presented in this paper comprises basically a nonlinear state feedback law which is represented by functions of the state vector in the controllable subspace of the model, an adaptive identifier of plant parameters which uses inputs and outputs of a certain length, and an adaptive law for feedback gain adjustment. A new psedu-inverse algorithm is used for the adaptive feedback gain adjustment rather than a least-square algorithm. The proposed feedback law results in not only uniform boundedness of the state vector to zero. The superiority of the proposed algorithm over other algorithms is shown through some examples.

• Smart Structures and Systems
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• v.31 no.5
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• pp.437-454
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• 2023

Real-time hybrid simulation (RTHS), which has the advantages of a substructure pseudo-dynamic test, is widely used to investigate the rate-dependent mechanical response of structures under earthquake excitation. However, time delay in RTHS can cause inaccurate results and experimental instabilities. Thus, this study proposes a model-based adaptive control strategy using a Kalman filter (KF) to minimize the time delay and improve RTHS stability and accuracy. In this method, the adaptive control strategy consists of three parts-a feedforward controller based on the discrete inverse model of a servohydraulic actuator and physical specimen, a parameter estimator using the KF, and a feedback controller. The KF with the feedforward controller can significantly reduce the variable time delay due to its fast convergence and high sensitivity to the error between the desired displacement and the measured one. The feedback control can remedy the residual time delay and minimize the method's dependence on the inverse model, thereby improving the robustness of the proposed control method. The tracking performance and parametric studies are conducted using the benchmark problem in RTHS. The results reveal that better tracking performance can be obtained, and the KF's initial settings have limited influence on the proposed strategy. Virtual RTHSs are conducted with linear and nonlinear physical substructures, respectively, and the results indicate brilliant tracking performance and superb robustness of the proposed method.

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

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1911-1916
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• 2005

In this study, the basic motor control system had been investigated. The Discrete-Time Feedback Error Learning (DTFEL) method is used to control this system. This method is anologous to the original continuous-time version Feedback Error Learning(FEL) control which is proposed as a control model of cerebellum in the field of computational neuroscience. The DTFEL controller consists of two main parts, a feedforward controller part and a feedback controller part. Each part will deals with different control problems. The feedback controller deals with robustness and stability, while the feedforward controller deals with response speed. The feedforward controller, used to solve the tracking control problem, is adaptable. To make such the tracking perfect, the adaptive law is designed so that the feedforward controller becomes an inverse system of the controlled plant. The novelty of FEL method lies in its use of feedback error as a teaching signal for learning the inverse model. The PD control theory is selected to be applied in the feedback part to guarantee the stability and solve the robust stabilization problems. The simulation of each individual part and the integrated one are taken to clarify the study.

• Structural Engineering and Mechanics
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• v.52 no.2
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• pp.275-290
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• 2014

In this paper, Simple Adaptive Control (SAC) method is used to mitigate the detrimental effects of earthquakes on MR-damper equipped structures. Acceleration Feedback (AF) is utilized since measuring the acceleration response of structures is known to be reliable and inexpensive. The SAC is simple, fast and as an adaptive control scheme, is immune against the effects of plant and environmental uncertainties. In the present study, in order to translate the desired control force into an applicable MR damper command voltage, a neural network inverse model is trained, validated and used through the simulations. The effectiveness of the proposed AF-based SAC control system is compared with optimal H2/LQG controllers through numerical investigation of a three-story model building. The results indicate that the SAC controller is substantially effective and reliable in both undamaged and damaged structural states, specifically in reducing acceleration responses of seismically excited buildings.

• Proceedings of the KAIS Fall Conference
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• 2008.11a
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• pp.191-196
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• 2008

The intent of this paper is to describe a neural network structure called dynamic neural processor(DNP), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the DNP, are described. Computer simulations are provided to demonstrate the effectiveness of the proposed learning using the DNP.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.673-676
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• 2015

Digital Pre-Distortion (DPD) is a linearization technique for nonlinear power amplifiers (PAs) by implementing inverse function of the PA at baseband digital stage. To obtain proper DPD parameters, a feedback path is required to convert the PA output to a baseband signal, and a memory is also needed to store the feedback signals. DPD parameters are usually found by an adaptive algorithm from the feedback samples. However, for the adaptive algorithm to converge to a reliable solution, long feedback samples are required, which increases convergence time and hardware complexity. In this paper, we propose a DPD technique that requires relatively short feedback samples. From the observation that the convergence time of the adaptive algorithm highly depends on the initial condition, this paper iteratively utilizes the feedback samples while keeping and using the converged DPD parameters at the former iteration as the initial condition at the current iteration. Computer simulation results show that the proposed method performs better than the conventional technique while the former requires much shorter feedback samples than the latter.