• Journal of the Korean Society for Precision Engineering
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• v.14 no.6
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• pp.90-98
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• 1997

A time-optimal control law for quick, strongly nonlinear systems has been developed and demonstrated. This procedure involves the utilzation of neural networks as state feedback controllers that learn the time-optimal control actions by means of an iterative minimization of both the final time and the final state error for the systems with constrained inputs and/or states. A neural identifier or a genetic algorithm identifier could be utilized for modeling the partially known systems and the unknown systems. The nature of neural networks as a parallel processor would circumvent the problem of "curwe of dimensionality". The control law has been demonstrated for both a torque input motor and a velocity input motor identified by a genetic algorithm called GENOCOPed GENOCOP.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.5
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• pp.1178-1183
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• 2008

The variable structure control(VSC) with sliding mode is an important and interesting topic in modern control of nonlinear systems. However, the discontinuous control law in VSC leads to undesirable chattering in practice. As a method solving this problem, in this paper, we propose a scheme of the VSC with neural network sliding surface. A neural network sliding surface with boundary layer is employed to solve discontinuous control law. The proposed controller can eliminate the chattering problem of the conventional VSC. The effectiveness of the proposed control scheme is verified by simulation results.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.739-742
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• 1997

This paper proposes the structure and parameter optimization technique of fuzzy neural networks using genetic algorithm. Fuzzy neural network has advantages of both the fuzzy inference system and neural network. The determination of the optimal parameters and structure of the fuzzy neural networks, however, requires special efforts. To solve these problems, we propose a new learning method for optimization of fuzzy neural networks using genetic algorithm. It can optimize the structure and parameters of the entire fuzzy neural network globally. Numerical example is provided to show the advantages of the proposed method.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.6 s.171
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• pp.207-212
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• 2005

The purpose of this work is to develop the FES controller that can cope with the muscle fatigue which is one of the most important problems of current FES (Functional Electrical Stimulation). The feasibility of the proposed FES controller was evaluated by simulation. We used a fitness function to describe the effect of muscle fatigue and recovery process. The FES control system was developed based on the biological neuronal system. Specifically, we used PD (Proportional and Derivative) and GC (Gravity Compensation) control, which was described by the neuronal feedback structure. It was possible to control of multiple joints and muscles by using the phase-based PD and GC control method and the static optimization. As a result, the proposed FES control system could maintain the cycling motion in spite of the muscle fatigue. It is expected that the proposed FES controller will play an important role in the rehabilitation of SCI patient.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.740-743
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• 1998

This paper presents an optimization algorithm for a stable Dynamic Neural Network (DNN) using genetic algorithm. Optimized DNN is applied to a problem of controlling nonlinear dynamical systems. DNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDNN has considerably fewer weights than DNN. The object of proposed algorithm is to the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling, a nonlinear dynamic system using the proposed optimized SDNN considering stability' is demonstrated by case studies.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.2 s.42
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• pp.37-46
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• 2005

To date, many viable smart base isolation systems have been proposed and investigated. In this study, a novel friction pendulum system (FPS) and an MR damper are employed as the isolator and supplemental damping device, respectively, of the smart base isolation system. A fuzzy logic controller (FLC) is used to modulate the MR damper because the FLC has an inherent robustness and ability to handle non linearities and uncertainties. A genetic algorithm (GA) is used for optimization of the FLC. The main purpose of employing a GA is to determine appropriate fuzzy control rules as well to adjust parameters of the membership functions. To this end, a GA with a local improvement mechanism is applied. This method is efficient in improving local portions of chromosomes. Neuro fuzzy models are used to represent dynamic behavior of the MR damper and FPS. Effectiveness of the proposed method for optimal design of the FLC is judged based on computed responses to several historical earthquakes. It has been shown that the proposed method can find optimal fuzzy rules and the GA optimized FLC outperforms not only a passive control strategy but also a human designed FLC and a conventional semi active control algorithm.