• 한국정밀공학회지
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• 제14권6호
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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 Advanced Marine Engineering and Technology
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• 제24권6호
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• pp.53-60
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• 2000

In this paper, we design a GA-fuzzy controller for position control and anti-swing at the destination point. A genetic algorithm is used to complement the demerits such as the difficulty of the component selection of the fuzzy controller, namely, scaling factors, membership functions and control rules. Lagrange equation is used to represent the motion equation of trolley and load in order to obtain mathematical modelling. Simulation results show that the proposed control technique is superior to a conventional optimal control in destination point moving and modification.

• EDISON SW 활용 경진대회 논문집
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• 제4회(2015년)
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• pp.527-529
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• 2015

The control delays at signal intersections have proved the source of numerous vehicular and environmental complications. Control delays both directly and indirectly hinder time- and cost-effective driving by extending the duration of time spent on the road and exhausting excessive amounts of fuel. They furthermore cause traffic congestion, thereby raising overall emission levels. It is therefore imperative to reduce control delays in order to achieve time and fuel economy and reduce vehicle-related pollution. The following study accordingly uses genetic algorithms to optimize traffic signals in congested environments.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.10-14
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• 2004

When providing flexible data transmission for future CDMA(Code Division Multiple Access) cellular networks, problems arise in two aspects: transmission rate. This paper has proposed an approach to maximize the cellular network capacity by combining the genetic transmission rate allocation and a rapid power control algorithm. We present a genetic chromosome representation to express call drop numbers and transmission rate to control mobile's transmission power levels while handling their flexible transmission rates. We suggest a rapid power control algorithm, which is based on optimal control theory and Steffenson acceleration technique comparing with the existing algorithms. Computer simulation results showed effectiveness and efficiency of the proposed algorithm Conclusively, our proposed scheme showed high potential for increasing the cellular network capacity and it can be the fundamental basis of future research.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2545-2547
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• 2000

In this paper, a hybrid fuzzy controller using genetic algorithm based on parameter estimation mode to obtain optimal control parameter is presented. First, The control input for the system in the HFC is a convex combination of the FLC's output in transient state and PID's output in steady state by a fuzzy variable, namely, membership function of weighting coefficient. Second, genetic algorithms is presented to automatically improve the performance of hybrid fuzzy controller utilizing the conventional methods for finding PID parameters and estimation mode of scaling factor. The algorithms estimates automatically the optimal values of scaling factors, PID parameters and membership function parameters of fuzzy control rules according to the rate of change and limitation condition of control input. Computer simulations are conducted to evaluate the performance of proposed hybrid fuzzy controller. ITAE, overshoot and rising time are used as a performance index of controller.

• 국제강구조저널
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• 제18권5호
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• pp.1598-1606
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• 2018

An integrated optimal structural design method for a diagrid structure and control device was developed. A multi-objective genetic algorithm was used and a 60-story diagrid building structure was developed as an example structure. Artificial wind and earthquake loads were generated to assess the wind-induced and seismic responses. A smart tuned mass damper (TMD) was used as a structural control system and an MR (magnetorheological) damper was employed to develop a smart TMD (STMD). The multi-objective genetic algorithm used five objectives including a reduction of the dynamic responses, additional stiffness and damping, mass of STMD, capacity of the MR damper for the integrated optimization of a diagrid structure and a STMD. From the proposed method, integrated optimal designs for the diagrid structure and STMD were obtained. The numerical simulation also showed that the STMD provided good control performance for reducing the wind-induced and seismic responses of a tall diagrid building structure.

• 제어로봇시스템학회논문지
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• 제6권5호
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• pp.376-382
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• 2000

A problem of relevant interest to some industries is that of the optimum two-dimensional layout. In this problem, one is given a number of rectangular sheets and an order for a specified number of each of certain types of two-dimensional regular and irregular shapes. The aim is to cut the shapes out of the sheets in such a way as to minimize the amount of waste produced. In this paper, we propose a genetic algorithms using rotation parameters by which the best pattern of layout is found.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.581-584
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• 1998

This paper presents a scheme for fuzzy modelling and control of continuous-time nonlinear systems using a genetic algorithm. A fuzzy model is characterized by fuzzy "if-then" rules whose consequence part has a linear dynamic equation as subsystem of the system. The parameters of the fuzzy model are adjusted by a genetic algorithm. Then a tracking controller which guarantees stability of the overall system is designed. The simulation result demonstrates the effectiveness of the proposed method.

• 제어로봇시스템학회논문지
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• 제11권9호
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• pp.802-809
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• 2005

A hybrid genetic algorithm is proposed for the problem of generating laser torch paths to cut a stock plate nested with free-formed parts each having a set of sub-parts. In the problem, the total unproductive travel distance of the torch is minimized. The problem is shown to be formulated as a special case of the standard travelling salesman problem. The hybrid genetic algorithm for solving the problem is hierarchically structured: First, it uses a genetic algorithm to find the cutting path f3r the parts and then, based on the obtained cutting path, sequence of sub-parts and their piercing locations are optimally determined by using a combined genetic and heuristic algorithms. This process is repeated until any progress in the total unproductive travel distance is not achieved. Computational results are provided to illustrate the validity of the proposed algorithm.

• 제어로봇시스템학회논문지
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• 제8권12호
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• pp.1056-1060
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• 2002

Model-based tuning rules of the PID controller are proposed incorporating with real-coded genetic algorithms. The optimal parameter sets of the PID controller for step set-point tracking are obtained based on the first-order time delay model and a real-coded genetic algorithm as an optimization tool. As for assessing the performance of the controllers, performance indices(ISE, IAE and ITAE) are adopted. Then tuning rules are derived using the tuned parameter sets, potential rule models and another real-coded genetic algorithm A set of simulation works is carried out to verify the effectiveness of the proposed rules.