• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.364-367
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• 1995

In this paper, a design method of learning flight control system via input matching is proposed. The proposed learning control system is a simple structure which has an artificial neural network and feedback mechanism, and it is a useful method to control nonlinear systems.

• 제어로봇시스템학회논문지
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• 제20권10호
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• pp.1002-1007
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• 2014

This paper deals with the adaptive backstepping hovering control for a quadrotor with model parameter uncertainties. In this paper, the backstepping based technique is utilized to design a nonlinear adaptive controller which can compensate for the motor thrust factor and the drag coefficient of a quadrotor. First, the quadrotor nonlinear dynamics is derived using Newton-Euler formulation. In particular, we use the ${\pi}/4$ shifted coordinate for x- and y-axis of a quadrotor. Second, an adaptive backstepping based attitude and altitude tracking control method is presented. The system stability and the convergence of tracking errors are proven using the Lyapunov stability theory. Finally, the simulation results are given to verify the effectiveness of the proposed control method.

• 한국통신학회논문지
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• 제23권9A호
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• pp.2211-2220
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• 1998

This paper presetns an adaptive critic self-learning control system with cerebellar model articulation controller (CMAC)-based decoder integrated with the associative search element (ASE) and adatpive critic element(ACE)- based scheme. The tast of the system is to balance a pole that is hinged to a movable cart by applying forces to the cart's base. The problem is that error feedback information is limited. This problem can be sloved when some adaptive control devices are involved. The ASE incorporates prediction information for reinforrcement from a critic to produce evaluative information for the plant. The CMAC-based decoder interprets one state to a set of patways into the ASE/ACE. These signals correspond to te current state and its possible preceding action states. The CMAC's information interpolation improves the learning speed. And design inverted pendulum hardware system to show control capability with neural network.