• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.768-783
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• 2020

Typically, a Dynamic Positioning System (DPS) uses a PID feed-back system, and it often adopts a wind feed-forward system because of its easier implementation than a feed-forward system based on current or wave. But, because a ship's drifting motion is caused by wind, current, and wave drift loads, all three environmental loads should be considered. In this study, a motion predictive control for the PID feedback system of the DPS is proposed, which considers the three environmental loads by utilizing predicted drifted ship positions in the future since it contains information about the three environmental loads from the moment to the future. The prediction accuracy for the future drifted ship position is ensured by adopting deep learning algorithms and a replay buffer. Finally, it is shown that the proposed motion predictive system results in better station-keeping performance than the wind feed-forward system.

• Journal of Navigation and Port Research
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• v.31 no.7
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• pp.597-603
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• 2007

A predictive controller can solve a control problem related to a disturbance-dominant system such as roll stabilization of a ship in waves. In this paper, a predictive controller is developed for a fin stabilizer. Future wave-induced moment is modeled simply using two typical regular wave components for which six parameters are identified by the recursive Fourier transform and the least squares method using the past time series of the roll motion. After predicting the future wave-induced moment, optimal control theory is applied to discover the most effective command fin angle that will stabilize the roll motion. In the results, wave prediction performance is investigated, and the effectiveness of the predictive controller is compared to a conventional PD controller.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.538-541
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• 1995

Joint flexibilities and frictional uncertainties are known to be a major cause of performance degration in motion control systems. This paper investigates the modeling and compensation of these undesired effects. A hybrid controller, which consists of a predictive controller and a neural network controller, is designed to overcome these undesired effects. Also learning scheme for friction uncertainies, which don't interfere with feedback controller dynamics, is discussed. Through simulation works with two inetia-torsional spring system having Coulomb friction, the effectiveness of the proposed hybrid controller was tested. The proposed predictive & neural network hybrid controller shows better performance over one when only predictive controller used.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.359-364
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• 2022

This study presents a nonlinear model predictive control (NMPC)-based obstacle avoidance and whole-body motion planning method for the mobile manipulators. For the whole-body motion control, the mobile manipulator with an omnidirectional mobile base was modeled as a nine degrees-of-freedom (DoFs) serial open chain with the PPR (base) plus 6R (arm) joints, and a swept sphere volume (SSV) was applied to define a convex hull for collision avoidance. The proposed receding horizon control scheme can generate a trajectory to track the end-effector pose while avoiding the self-collision and obstacle in the task space. The proposed method could be calculated using an interior-point (IP) method solver with 100[ms] sampling time and ten samples of horizon size, and the validation of the method was conducted in the environment of Pybullet simulation.

• Journal of the Korea Computer Graphics Society
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• v.27 no.1
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• pp.9-17
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• 2021

In this paper, we present a simple and fast supervised learning framework based on model predictive control so as to learn motion controllers for a physic-based character to track given example motions. The proposed framework is composed of two components: training data generation and offline learning. Given an example motion, the former component stochastically controls the character motion with an optimal controller while repeatedly updating the controller for tracking the example motion through model predictive control over a time window from the current state of the character to a near future state. The repeated update of the optimal controller and the stochastic control make it possible to effectively explore various states that the character may have while mimicking the example motion and collect useful training data for supervised learning. Once all the training data is generated, the latter component normalizes the data to remove the disparity for magnitude and units inherent in the data and trains an artificial neural network with a simple architecture for a controller. The experimental results for walking and running motions demonstrate how effectively and fast the proposed framework produces physics-based motion controllers.

• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.35-42
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• 2022

This paper presents a stochastic model predictive control (SMPC) for stop maneuver of autonomous vehicles considering perception uncertainty of stopped vehicle. The vehicle longitudinal motion should achieve both driving comfortability and safety. The comfortable stop maneuver can be performed by mimicking acceleration profile of human driving pattern. In order to implement human-like stop motion, we propose a reference safe inter-distance and velocity model for the longitudinal control system. The SMPC is used to track the reference model which contains the position uncertainty of preceding vehicle as a chance constraint. We conduct simulation studies of deceleration scenarios against stopped vehicle in urban environment. The test results show that proposed SMPC can execute comfortable stop maneuver and guarantee safety simultaneously.

• Journal of the Korea Computer Graphics Society
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• v.25 no.3
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• pp.133-142
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• 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 Institute of Control, Robotics and Systems
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• v.7 no.12
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• pp.967-972
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• 2001

In this paper, an adaptive robust nonlinear predictive controller is developed for the continuous time nonlinear systems whose control objective is composed of the system output and its desired value. The basic control law is derived from the continuous time prediction model and its feedback dynamcis shows another from if input and output linearization. In order to cope with the parameter uncertainty, robust control is incorporated into the basic control law and the asymptotic convergence of tracking error to a certain bounded region is guaranteed. For stability and performance improvement within the bounded region, an adaptive control is introduced. Simulation tests for the motion control of an underwater wall-ranging robot confirm the performance improvement and the robustness of this controller.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.302-305
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• 1996

This paper presents a visual servoing combined by Neural Network with optimal structure and predictive control for robotic manipulators to tracking or grasping of the moving object. Using the four feature image information from CCD camera attached to end-effector of RV-M2 robot manipulator having 5 dof, we want to predict the updated position of the object. The Kalman filter is used to estimate the motion parameters, namely the state vector of the moving object in successive image frames, and using the multi layer feedforward neural network that permits the connection of other layers, evolutionary programming(EP) that search the structure and weight of the neural network, and evolution strategies(ES) which training the weight of neuron, we optimized the net structure of control scheme. The validity and effectiveness of the proposed control scheme and predictive control of moving object will be verified by computer simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.9
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• pp.957-963
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• 2014

This paper proposes a predictive control for an efficient human following robot using Kinect sensor. Especially, this research is focused on detecting of foot-end-point and foot-vector instead of human body which can be occluded easily by the obstacles. Recognition of the foot-end-point by the Kinect sensor is reliable since the two feet images can be utilized, which increases the detection possibility of the human motion. Depth image features and a decision tree have been utilized to estimate the foot end-point precisely. A tracking point average algorithm is also adopted in this research to estimate the location of foot accurately. Using the continuous locations of foot, the human motion trajectory is estimated to guide the mobile robot along a smooth path to the human. It is verified through the experiments that detecting foot-end-point is more reliable and efficient than detecting the human body. Finally, the tracking performance of the mobile robot is demonstrated with a human motion along an 'L' shape course.