• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.255-255
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• 2000

Technical advance of electrical chart and cruising equipment make it possible to sail without a man. It is important to decide the cruising route in view of effectiveness and stability of a ship. So we need to study on the optimal trajectory planning. Genetic algorithm is a strong optimization algorithm with adaptational random search. It is a good choice to apply genetic algorithm to the trajectory planning of a ship. We modify a genetic algorithm to solve this problem. The effectiveness of the revised genetic algorithm is assured through computer simulations.

• Journal of Advanced Marine Engineering and Technology
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• 제22권4호
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• pp.498-504
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• 1998

This paper presents an optimal control algorithm for the overhead crane. To control the swing motion and the position tracking of the payload of the overhead crane a state feedback control algorithm is applied. by using a hybrid genetic algorithm the feedback gains of the state feedback is optimized to minimize the cost function composed of position errors and payload swing angle under unknown constant disturbances. Computer simulation is performed to demonstrate the effectiveness of the proposed control algorithm.

• 한국경영과학회지
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• 제14권1호
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• pp.27-35
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• 1989

An algorithm that searches the general AND-OR graph is proposed. The convergence and the efficiency of the algorithm is examined and compared with an existing algorithm for the AND-OR graph. It is proved that the proposed algorithm is superior to the existing method both in the quality of the solution and the number of node expansions.

• 한국소음진동공학회논문집
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• 제18권3호
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• pp.284-292
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• 2008

The active control technique mostly uses the least-mean-square(LMS) algorithm, because the LMS algorithm can easily obtain the complex transfer function in real-time, particularly when the Filtered-X LMS(FXLMS) algorithm is applied to an active noise control(ANC) system. However, FXLMS algorithm has the demerit that stability of the control is decreased when the step size become larger but the convergence speed is faster because the step size of FXLMS algorithm is fixed. As a result, the system has higher probability which the divergence occurs. Thus the Co-FXLMS algorithm was developed to solve this problem. The Co-FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. In this paper, the performance of the Co-FXLMS algorithm is presented in comparison with the FXLMS algorithm. Simulation and experimental results show that active noise control using Co-FXLMS is effective in reducing the noise in duct system.

• 제어로봇시스템학회논문지
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• 제17권8호
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• pp.843-850
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• 2011

In this paper, an effective path generation algorithm for obstacle avoidance producing small amount of steering action as possible is proposed. The proposed path generation algorithm can reduce unnecessary steering because of the small lateral changes in generated waypoints when UGV (Unmanned Ground Vehicle) encounters obstacles during its waypoint navigation. To verify this, the proposed algorithm and $A^*$ algorithm are analyzed through the simulation. The proposed algorithm shows good performance in terms of lateral changes in the generated waypoint, steering changes of the vehicle while driving and execution speed of the algorithm. Especially, due to the fast execution speed of the algorithm, the obstacles that encounter suddenly in front of the vehicle within short range can be avoided. This algorithm consider the waypoint navigation only. Therefore, in certain situations, the algorithm may generate the wrong path. In this case, a general path generation algorithm like $A^*$ is used instead. However, these special cases happen very rare during the vehicle waypoint navigation, so the proposed algorithm can be applied to most of the waypoint navigation for the unmanned ground vehicle.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권2호
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• pp.600-619
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• 2015

We propose a cost-efficient hybrid algorithm for online location updates that efficiently combines feature point detection with the online trajectory-based sampling algorithm. Our algorithm is designed to minimize the average trajectory error with the minimal number of sample points. The algorithm is composed of 3 steps. First, we choose corner points from the map as sample points because they will most likely cause fewer trajectory errors. By employing the online trajectory sampling algorithm as the second step, our algorithm detects several missing and important sample points to prevent unwanted trajectory errors. The final step improves cost efficiency by eliminating redundant sample points on straight paths. We evaluate the proposed algorithm with real GPS trajectory data for various bus routes and compare our algorithm with the existing one. Simulation results show that our algorithm decreases the average trajectory error 28% compared to the existing one. In terms of cost efficiency, simulation results show that our algorithm is 29% more cost efficient than the existing one with real GPS trajectory data.

• Structural Engineering and Mechanics
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• 제25권2호
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• pp.239-268
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• 2007

This paper deals with optimal stacking sequence design of laminate composite structures. The stacking sequence optimisation of laminate composites is formulated as a combinatorial problem and is solved using Simulated Annealing (SA), an algorithm devised based on inspiration of physical process of annealing of solids. The combinatorial constraints are handled using a correction strategy. The SA algorithm is strengthened by embedding Tabu search in order to prevent recycling of recently visited solutions and the resulting algorithm is referred to as tabu embedded simulated Annealing (TSA) algorithm. Computational performance of the proposed TSA algorithm is enhanced through cache-fetch implementation. Numerical experiments have been conducted by considering rectangular composite panels and composite cylindrical shell with different ply numbers and orientations. Numerical studies indicate that the TSA algorithm is quite effective in providing practical designs for lay-up sequence optimisation of laminate composites. The effect of various neighbourhood search algorithms on the convergence characteristics of TSA algorithm is investigated. The sensitiveness of the proposed optimisation algorithm for various parameter settings in simulated annealing is explored through parametric studies. Later, the TSA algorithm is employed for multi-criteria optimisation of hybrid composite cylinders for simultaneously optimising cost as well as weight with constraint on buckling load. The two objectives are initially considered individually and later collectively to solve as a multi-criteria optimisation problem. Finally, the computational efficiency of the TSA based stacking sequence optimisation algorithm has been compared with the genetic algorithm and found to be superior in performance.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 심포지엄 논문집 정보 및 제어부문
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• pp.77-78
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• 2008

This paper proposes a new algorithm for path planning and obstacles avoidance using the ant colony optimization algorithm and the particle swarm optimization. The proposed algorithm is a new hybrid algorithm that composes of the ant colony algorithm method and the particle swarm optimization method. At first, we produce paths of a mobile robot in the static environment. And then, we find midpoints of each path using the Maklink graph. Finally, the hybrid algorithm is adopted to get a shortest path. We prove the performance of the proposed algorithm is better than that of the path planning algorithm using the ant colony optimization only through simulation.

• 제어로봇시스템학회논문지
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• 제10권11호
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• pp.1071-1076
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• 2004

It is a fuzzy controller that it is the most used method in the control of non-linear system. The most important part in the fuzzy controller is a design of fuzzy rules. Many algorithm that design fuzzy rules have proposed. And attention to the evolutionary computation is increasing in the recent days. Among them, the co-evolutionary algorithm is used in the design of optimal fuzzy rule. This paper takes advantage of a schema co-evolutionary algorithm. In order to verify the efficiency of the schema co-evolutionary algorithm, a fuzzy controller for the mobile robot control is designed by the schema co-evolutionary algorithm and it is compared with other parasitic co-evolutionary algorithm such as a virus-evolutionary genetic algorithm and a co-evolutionary method of Handa.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 추계학술대회논문집A
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• pp.533-538
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• 2001

This paper describes a combinational method to compute the global and local solutions of optimization problems. The present hybrid algorithm uses both a genetic algorithm and a local concentrate search algorithm (e. g simplex method). The hybrid algorithm is not only faster than the standard genetic algorithm but also supplies a more accurate solution. In addition, this algorithm can find the global and local optimum solutions. The present algorithm can be supplied to minimize the resonance response (Q factor) and to yield the critical speeds as far from the operating speed as possible. These factors play very important roles in designing a rotor-bearing system under the dynamic behavior constraint. In the present work, the shaft diameter, the bearing length, and clearance are used as the design variables.