• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
• /
• pp.1500-1503
• /
• 1996

This paper combines the disturbance accommodating control(DAC) and nonlinear model inversion control for a skid-to-turn(STT) missile. The missile autopilot may be designed to be robust with respect to a variety of uncertainties. We proposes the two step control design method. Nonlinear model inversion control is used as the main design method. Due to the model uncertainties and external disturbances, the exact nonlinear model inversion can not be achieved. DAC is designed to detect, to identify, and to compensate these uncertainties. DAC's disturbance observer is linear. Thus it is easy to implement. It does not cause the convergence problem due to coexistence between the modeling uncertainties and external disturbances. 6 DOF simulation results show that the proposed method may improve the missile tracking performance.

• 대한전기학회논문지:전력기술부문A
• /
• 제48권6호
• /
• pp.719-727
• /
• 1999

A robust position control method of the BLDC motor using a new sliding mode control strategy is presented. The main property of variable structure system(VSS) is that the system response is robust and insensitive to parameter variations and external disturbances in the sliding mode. When using the conventional VSS, generally the reaching phase problem occurs, which cause the system response to be sensitive to parameter variations and external disturbances. Furthermore, the speed of response is relatively slow because the swithching surface is a linear function. In order to overcome these problems, VSS with nonlinear sliding surface eliminating reaching phase is proposed. The validity of the proposed scheme is shown by results of simulations of simulations and experiments for the BLDC motor with variable load.

• 전자공학회논문지B
• /
• 제32B권12호
• /
• pp.1670-1679
• /
• 1995

In this paper, an optimal variable structure controller with a multilayer neural inverse identifier is proposed. A multilayer neural network with error back propagation learning algorithm is used for construction the neural inverse identifier which is an observer of the external disturbances and the parameter variations of the system. The variable structure controller with the multilayer neural inverse identifier not only needs a small part of a priori knowledge of the bounds of external disturbances and parameter variations but also alleviates the chattering magnitude of the control input. Also, an optimal sliding line is designed by the optimal linear regulator technique and an integrator is introduced for solving the reaching phase problem. Computer simulation results show that the proposed approach gives the effective control results by reducing the chattering magnitude of control input.

• 제어로봇시스템학회논문지
• /
• 제9권3호
• /
• pp.204-209
• /
• 2003

The line-of-sight stabilization system is an equipment which is loaded on a vehicle and stabilizes the direction of the line-of-sight of the vision sensor to obtain a not-swayed image in the existence of external disturbances. To obtain accurate Euler angles and angular velocities simultaneously we usually need a control system which uses high-price inertial sensors including Vertical Gyro(VG) or Rate Integrating Gyro(RIG). In this paper, we design and implement a control system of a gimbal, which is a line-of-sight stabilization system using a low-cost mixed algorithm of a rate gyro and an accelerometer instead of a VG and a RIG. In the experiment where we laid the implemented line-of-sight stabilization system on the rate table. we can see the stabilized performance to external disturbances.

• 제어로봇시스템학회논문지
• /
• 제14권3호
• /
• pp.254-262
• /
• 2008

In this paper, we propose an adaptive neural dynamic surface control (DSC) approach with $H_{\infty}$ tracking performance for full dynamics of nonlinear flight systems. It is assumed that the model uncertainties such as structured and unstrutured uncertainties, and external disturbances influence the nonlinear aircraft model. In our control system, self recurrent wavelet neural networks (SRWNNs) are used to compensate the model uncertainties of nonlinear flight systems, and an adaptive DSC technique is extended for the disturbance attenuation of nonlinear flight systems. All weights of SRWNNs are trained on-line by the smooth projection algorithm. From Lyapunov stability theorem, it is shown that $H_{\infty}$ performance nom external disturbances can be obtained. Finally, we present the simulation results for a nonlinear six-degree-of-freedom F-16 aircraft model to confirm the effectiveness of the proposed control system.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
• /
• pp.35-38
• /
• 1998

Abstracts In this paper, we propose a robust adaptive fuzzy control scheme using a sliding control input for tracking of a class of MISO nonlinear systems with unknown bounded external disturbances. In the proposed scheme, the nonlinearity is estimated adaptively via a fuzzy inference based on a fuzzy model. A sliding control input is introduced such that boundedness of all signals in the system is guaranteed even though the existence of a fuzzy approximation error and external disturbances. The controller parameters are updated by using a proposed adaptation law, which is similar 1-modification method. Computer simulation shows the effectiveness of the proposed control scheme.

• International Journal of Control, Automation, and Systems
• /
• 제5권3호
• /
• pp.269-282
• /
• 2007

This paper presents a robust control method via the intelligent adaptive backstepping design technique for stable walking of nine-link biped robots with unknown model uncertainties and external disturbances. In our control structure, the self recurrent wavelet neural network(SRWNN) which has the information storage ability is used to observe the uncertainties of the biped robots. The adaptation laws for all weights of the SRWNN are induced from the Lyapunov stability theorem, which are used for on-line controlling biped robots. Also, we prove that all signals in the closed-loop adaptive system are uniformly ultimately bounded. Through computer simulations of a nine-link biped robot with model uncertainties and external disturbances, we illustrate the effectiveness of the proposed control system.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
• /
• pp.71-77
• /
• 1995

In this paper are proposed robust iterative learning control(ILC) algorithms for both linear continuous time-invariant system and linear discrete-time system. In contrast to conventional methods, the proposed learning algorithms are constructed based on both time domain performance and iteration-domain performance. The convergence of the proposed learning algorithms is proved. Also, it is shown that the proposed method has robustness in the presence of external disturbances and the convergence accuracy can be improved. A numerical external disturbances and the convergence accuracy can be improved. A numerical example is provided to show the effectiveness of the proposed algorithm.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2007년도 제38회 하계학술대회
• /
• pp.1728-1729
• /
• 2007

This paper presents an adaptive neural dynamic surface control (DSC) approach with $H_{\infty}$ tracking performance for a full dynamics of a nonlinear flight system. It is assumed in this paper that model uncertainties such as structured and unstrutured uncertainties and external disturbances influence the nonlinear aircraft model. In our control system, self recurrent wavelet neural networks (SRWNNs) are used to compensate model uncertainties of the nonlinear flight system, and an adaptive DSC technique is extended for disturbance attenuation of the nonlinear flight system. From Lyapunov stability theorem, it is shown that $H_{\infty}$ performance from external disturbances can be obtained. Finally, we perform the simulation for the nonlinear six-degree-of-freedom F-16 aircraft model to confirm the effectiveness of the proposed control system.

• International Journal of Naval Architecture and Ocean Engineering
• /
• 제13권1호
• /
• pp.758-771
• /
• 2021

This paper presents a novel control scheme for the three-dimensional (3D) path following of underactuated Autonomous Underwater Vehicle (AUVs) subject to unknown internal and external disturbances, in term of the time scale decomposition method. As illustration, two-time scale motions are first artificially forced into the closed-loop control system, by appropriately selecting the control gain of the integrator. Using the singular perturbation theory, the integrator is considered as a fast dynamical control law that designed to shape the space configuration of fast variable. And then the stabilizing controller is designed in the reduced model independently, based on the time scale decomposition method, leading to a relatively simple control law. The stability of the resultant closed-loop system is demonstrated by constructing a composite Lyapunov function. Finally, simulation results are provided to prove the efficacy of the proposed controller for path following of underactuated AUVs under internal and external disturbances.