• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.47.1-47
• /
• 2002

$\textbullet$ Introduction $\textbullet$ Genetic Algorithm $\textbullet$ Optimal Control $\textbullet$ Simulation $\textbullet$ Conclusion

• Bulletin of the Korean Space Science Society
• /
• 2004.10b
• /
• pp.74-74
• /
• 2004

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.10
• /
• pp.1879-1885
• /
• 2017

This paper discusses the trajectory tracking system of unmanned ground vehicles based on predictive control. Because the unmanned ground vehicles can not satisfactorily complete the path tracking task, highly efficient and stable trajectory control system is necessary for unmanned ground vehicle to be realized intelligent and practical. According to the characteristics of unmanned vehicle, this paper built the kinematics tracking models firstly. Then studied algorithm solution with the tools of the optimal stability analysis method and proposed a tracking control method based on the model predictive control. The controller used a kinematics-based prediction model to calculate the predictive error. This controller helps the unmanned vehicle drive along the target trajectory quickly and accurately. The designed control strategy has the true robustness, simplicity as well as generality for kinematics model of the unmanned vehicle. Furthermore, the computer Simulink/Carsim results verified the validity of the proposed control method.

• KIPS Transactions on Software and Data Engineering
• /
• v.3 no.9
• /
• pp.369-374
• /
• 2014

Path generation methods are required for safe and efficient driving in unmanned autonomous vehicles. There are two kinds of paths: global and local. A global path consists of all the way points including the source and the destination. A local path is the trajectory that a vehicle needs to follow from a way point to the next in the global path. In this paper, we propose a novel method for local path generation through machine learning, with an effective curve function used for initializing the trajectory. First, reinforcement learning is applied to a set of candidate paths to produce the best trajectory with maximal reward. Then the optimal steering angle with respect to the trajectory is determined by training an artificial neural network. Our method outperformed existing approaches and successfully found quality paths in various experimental settings, including the cases with obstacles.

• Journal of the Korea Computer Graphics Society
• /
• v.25 no.3
• /
• pp.133-142
• /
• 2019

In physics-based character animation, trajectory optimization has been widely adopted for automatic motion synthesis, through the prediction of an optimal sequence of future states of the character based on its system dynamics model. In general, the system dynamics model is neither in a closed form nor differentiable when it handles the contact dynamics between a character and the environment with rigid body collisions. Employing smoothed contact dynamics, researchers have suggested efficient trajectory optimization techniques based on numerical differentiation of the resulting system dynamics. However, the numerical derivative of the system dynamics model could be inaccurate unlike its analytical counterpart, which may affect the stability of trajectory optimization. In this paper, we propose a novel method to derive the closed-form derivative for the system dynamics by properly approximating the contact model. Based on the resulting derivatives of the system dynamics model, we also present a model predictive control (MPC)-based motion synthesis framework to robustly control the motion of a biped character according to on-line user input without any example motion data.

• Journal of Mechanical Science and Technology
• /
• v.15 no.5
• /
• pp.580-591
• /
• 2001

This paper proposes a fuzzy-sliding mode control which is designed by a self tuning fuzzy inference method based on a genetic algorithm. Using the method, the number of inference rules and the shape of the membership functions of the proposed fuzzy-sliding mode control are optimized without the aid of an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. It is further guaranteed that the selected solution becomes the global optimal solution by optimizing Akaikes information criterion expressing the quality of the inference rules. In order to evaluate the learning performance of the proposed fuzzy-sliding mode control based on a genetic algorithm, a trajectory tracking simulation of the polishing robot is carried out. Simulation results show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the trajectory control result is similar to the result of the fuzzy-sliding mode control which is selected through trial error by an expert. Therefore, a designer who does not have expert knowledge of robot systems can design the fuzzy-sliding mode controller using the proposed self tuning fuzzy inference method based on the genetic algorithm.

• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.9
• /
• pp.936-941
• /
• 2014

Walking mechanisms are very important for legged robots to ensure their stable locomotion. In this research, Klann-linkage is suggested as a walking mechanism for a water-running robot and is optimized using level average analysis. The structure of the Klann-linkage is introduced first and design variables for the Klann-linkage are identified considering the kinematic task of the walking mechanism. Next, the design problem is formulated as a path generation optimization problem. Specifically, the desired path for the foot-pad is defined and the objective function is defined as the structural error between the desired and the generated paths. A process for solving the optimization problem is suggested utilizing the sensitivity analysis of the design variables. As a result, optimized lengths of Klann-linkage are obtained and the optimum trajectory is obtained. It is found that the optimized trajectory improves the cost function by about 62% from the initial one. It is expected that the results from this research can be used as a good example for designing legged robots.

• 제어로봇시스템학회:학술대회논문집
• /
• 1999.10a
• /
• pp.238-243
• /
• 1999

Minimum-fuel and -time orbit transfer are two major goals of the satellite trajectory optimization. In this paper, we consider satellites in two coplanar elliptic orbits when the apsidal lines coincide, and analytically find the conditions for the two-impulse minimum-time transfer orbit using Lambert's theorem. The transfer time is a decreasing function of a variable related to the transfer orbit's semimajor axis in the minimum-time case. In the minimum-time case, there is no unique minimum-time solution, but there is a limiting solution. However, there exists a unique solution in the case of minimum-fuel transfer, fur which we find analytically the necessary and sufficient conditions. As a special case, we consider when the transfer angle is one hundred and eighty degrees. In this case, we show that we obtain the classical fuel-optimal Hohmann transfer orbit. We also derive the Hohmann transfer rime and delta-velocity equations from more general equations, which are obtained using Lambert's theorem. We note the tradeoff between minimum-time and - fuel transfer. An optimal coplanar orbit maneuver algorithm to trade off the minimum-time goal against the minimum-fuel goal is proposed. Finally, the numerical simulation results are given to demonstrate the derived theory and the algorithm.

• Korean Journal of Social Welfare Studies
• /
• v.49 no.3
• /
• pp.209-245
• /
• 2018

In this study, various performance types, the combinations of the performance types for growth were investigated to suggest viable policy recommendations for the sustainable growth of social enterprises. The data of the economic and social performance of social enterprises from 2011 to 2016 were obtained and the changes were investigated. Among total of 235 social enterprises that participated in Cho et al, 2011, the research subjects were 164 social enterprises, which were still being operated in March, 2018. The performance of 6 years, since 2011, was surveyed, and total of 104(recovery factor: 69.8%) of social enterprises were analyzed using the growth mixture model, cross tabulation. First of the results, the latent trajectory classes of sales, which are of economic performance, were investigated through the analysis of growth mixture model. The optimal model including three latent classes was adopted. The three latent classes were named as 'mature sales type', 'growing sales type', and 'average sales type'. Second, the latent trajectory classes of employment rate, which are of social performance, were investigated. The optimal model including three latent classes was adopted. The three latent classes were named as 'average employment type', 'declining employment type', and 'increasing employment type'. Third, cluster in $3{\times}3$ tabulation, which is a distribution of the latent trajectory classes of social performance based on the latent trajectory classes of economic performance of social enterprises, was looked into.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.7 no.2
• /
• pp.25-33
• /
• 1997

A DEDS is a system whose states change in response to the occurence of events from a predefined event set. In this paper, we consider the optimal control and reasoning problem for Discrete Event Systems(DES) in the Temporal Logic Framework(TEL) which have been recnetly defined. The TLE is enhanced with objective functions(event cost indices) and a measurement space is alos deined. A sequence of event which drive the system form a give initial state to a given final state is generated by minimizing a cost functioin index. Our research goal is the reasoning of optimal trajectory and the design of the optimal controller for DESs. This procedure could be guided by the heuristic search methods. For the heuristic search, we suggested the Stochastic Ruler algorithm, instead of the A algorithm with difficulties as following ; the uniqueness of solutions, the computational complexity and how to select a heuristic function. This SR algorithm is used for solving the optimal problem. An example is shown to illustrate our results.