• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.5
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• pp.609-617
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• 2013

This paper presents a near-minimum time path planning algorithm for autonomous driving. The problem of near-minimum time path planning is an optimization problem in which it is necessary to take into account not only the geometry of the circuit but also the dynamics of the vehicle. The path planning algorithm consists of a candidate path generation and a velocity optimization algorithm. The candidate path generation algorithm calculates the compromises between the shortest path and the path that allows the highest speeds to be achieved. The velocity optimization algorithm calculates the lap time of each candidate considering the vehicle driving performance and tire friction limit. By using the calculated path and velocity of each candidate, we calculate the lap times and search for a near-minimum time path. The proposed algorithm was evaluated via computer simulation using CarSim and Matlab/Simulink.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.180.5-180
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• 2001

This paper presents A Design of WMR Controller that being composed of cooperative relation between PID controller and optimized neural network algorithm, it operate a variable control by velocity. Some proposed algorithm in the past just depended on PID controller for the control of position of WMR but for more efficient control we design a variable controller that operate control by PD controller using neural network if it is satisfied with any given condition. it adjust gain of PD controller for real time control using a fast feedforward algorithm which is different with Form of the standard backpropagation algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.787-792
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• 1991

This paper proposes a steering control algorithm for non-holonomic mobile robots. The steering control algorithm is essential to navigate autonomous vehicles which employ comination of the dead reckoning and absolute sensor system such as a machine vison for detecting landmarks in order to estimate the current location of the mobile robot. The proposed algorithm is based on the minimum time BANG-BANG controller and curvature-continuity curve design method. In the BANG-BANG control scheme we introduce velocity/acceleration limiter to avoid any slippage of driving wheels. The proposed scheme is robot-independent and hence can be applied to various kinds of mobile robot or vehicles. To show the effectness of the proposed control algorithm, a series of computer simulations were conducted for two-wheel driven mobile robot.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.4
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• pp.51-56
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• 2006

This paper proposes a control algorithm, which is verified experimentally, for aspherical surface grinding and polishing. The algorithm provides simultaneous control of the position and interpolation of an aspheric curve. The nonlinear formula for the tool position was derived from the aspheric equation and the shape of the tool. The function was partitioned at specific intervals and the control parameters were calculated at each control section. The position, acceleration, and velocity at each interval were updated during the process. A position error feedback was introduced using a rotary encoder. The feedback algorithm corrected the position error by increasing or decreasing the feed speed. In the experimental verification, a two-axis machine was controlled to track an aspherical surface using the proposed algorithm. The effects of the control and process parameters were monitored. The results demonstrated that the maximum tracking error with tuned parameters was at the submicron level for concave and convex surfaces.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.273-277
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• 1988

This paper is to show design of robot manipulator which has 3-link using DC Motor and realization of control algorithm with IBM - XT Micro-computer connected. Gentral algorithm is applicated by position and pass control using point-to-point method. At first, this paper computes required angles on each joint in order to search desired position or path, and uses a voltage control with feedback from output of encoder and tachometer in real time. The application of control algorithm on position, velocity and force for each joint of manipulator by using self-tuning control is left for next study.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.113-117
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• 2002

PID controllers, which have been widely used in industry, have a simple structure and robustness to modeling error. But it is difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose a nonlinear adaptive PR controller based on an Immune feedback mechanism and a gradient descent teaming. This algorithm has a simple structure and robustness to system parameters variation. To verify performances of the proposed nonlinear adaptive PID controller, the speed control of nonlinear DC motor Is peformed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation

• Journal of Korean Association for Spatial Structures
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• v.21 no.2
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• pp.41-48
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• 2021

A smart tuned mass damper (TMD) is widely studied for seismic response reduction of various structures. Control algorithm is the most important factor for control performance of a smart TMD. This study used a Deep Deterministic Policy Gradient (DDPG) among reinforcement learning techniques to develop a control algorithm for a smart TMD. A magnetorheological (MR) damper was used to make the smart TMD. A single mass model with the smart TMD was employed to make a reinforcement learning environment. Time history analysis simulations of the example structure subject to artificial seismic load were performed in the reinforcement learning process. Critic of policy network and actor of value network for DDPG agent were constructed. The action of DDPG agent was selected as the command voltage sent to the MR damper. Reward for the DDPG action was calculated by using displacement and velocity responses of the main mass. Groundhook control algorithm was used as a comparative control algorithm. After 10,000 episode training of the DDPG agent model with proper hyper-parameters, the semi-active control algorithm for control of seismic responses of the example structure with the smart TMD was developed. The simulation results presented that the developed DDPG model can provide effective control algorithms for smart TMD for reduction of seismic responses.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1635-1638
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• 1997

A measurement of conduction veloctiy of the action potentials along the muscle fibres has been applied to various diagnosis. When we measure muscle fiber conduction velocity, it occurs that not only change of conduction velocity but alos inclusion of mipulse component by physiological and experimental reason. So, robuster time delay estimation algorithm than general methods[1] is needed to find correct time delay form these signals. In this paper we, propose new time delay estimation algorithms, robust in impulsive noise, by using characteristic of .alpha.-stable distribution whcih defines impulsive noise well. Then we apply proposed algorthms to measure muscle fiber conduction velocity and compare them with other studies.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.6
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• pp.526-532
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• 2007

This paper considers a fault tolerant control problem for a spacecraft using reaction wheels. Faults are assumed to be inherent to only actuators(reaction wheels) and a control algorithm to accommodate actuators' faults is proposed. An attitude control loop includes an angular velocity control loop. The time delay control method is used to make a spacecraft follow the command angular velocity and to accommodate actuators' faults. A stability condition for the proposed algorithm is derived and the performance is demonstrated by computer simulations.

• Journal of Power Electronics
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• v.13 no.4
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• pp.536-545
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• 2013

In this study, an integrated motor control algorithm for an in-wheel electric vehicle is suggested. It consists of slip control that controls the in-wheel motor torque using the road friction coefficient and slip ratio; yaw rate control that controls the in-wheel motor torque according to the road friction coefficient and the yaw rate error; and velocity control that controls the vehicle velocity by a weight factor based on the road friction coefficient and the yaw rate error. A co-simulator was developed, which combined the vehicle performance simulator based on MATLAB/Simulink and the vehicle model of CarSim. Based on the co-simulator, a human-in-the-loop simulation environment was constructed, in which a driver can directly control the steering wheel, the accelerator pedal, and the brake pedal in real time. The performance of the integrated motor control algorithm for the in-wheel electric vehicle was evaluated through human-in-the-loop simulations.