• International Journal of Aeronautical and Space Sciences
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• v.12 no.1
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• pp.16-23
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• 2011

In this paper, nonlinear model predictive control (NMPC) is addressed to develop formation guidance for multiple unmanned aerial vehicles. An NMPC algorithm predicts the behavior of a system over a receding time horizon, and the NMPC generates the optimal control commands for the horizon. The first input command is, then, applied to the system and this procedure repeats at each time step. The input constraint and state constraint for formation flight and inter-collision avoidance are considered in the proposed NMPC framework. The performance of NMPC for formation guidance critically degrades when there exists a communication failure. In order to address this problem, the modified optimal guidance law using only line-of-sight, relative distance, and own motion information is presented. If this information can be measured or estimated, the proposed formation guidance is sustainable with the communication failure. The performance of this approach is validated by numerical simulations.

• 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.

• The Plant Pathology Journal
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• v.37 no.6
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• pp.533-542
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• 2021

Chitosan has been considered an environmental-friendly polymer. However, its use in agriculture has not been extended yet due to its relatively low solubility in water. N-Methylene phosphonic chitosan (NMPC) is a water-soluble derivative prepared by adding a phosphonic group to chitosan. This study demonstrates that NMPC has a fungicidal effect on the phytopathogenic fungus Fusarium solani f. sp. eumartii (F. eumartii) judged by the inhibition of F. eumartti mycelial growth and spore germination. NMPC affected fungal membrane permeability, reactive oxygen species production, and cell death. Also, this chitosan-derivative exerted antifungal effects against two other phytopathogens, Botrytis cinerea, and Phytophthora infestans. NMPC did not affect tomato cell viability at the same doses applied to these phytopathogens to exert fungicide action. In addition to water solubility, the selective biological cytotoxicity of NMPC adds value in its application as an antimicrobial agent in agriculture.

• Nuclear Engineering and Technology
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• v.56 no.10
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• pp.4437-4445
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• 2024

During the process of bilateral teleoperation, operators not only need to perform complex maintenance tasks but also have to constantly monitor the safety of the operation, leading to reduced operational efficiency. Therefore, in this paper, we introduce a shared autonomous scheme that intervenes in the operator's command input when necessary, autonomously ensuring the safe operation of the manipulator by employing a rolling horizon planning controller based on Nonlinear Model Predictive Control (NMPC). This controller considers the motion boundaries and collision avoidance constraints of the manipulator, accompanied by the design of corresponding objective functions. To validate the effectiveness of the proposed method, we conduct tests on collision-free trajectory tracking and comprehensive performance with collision constraints, confirming the feasibility and excellent performance of the approach.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.15-22
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• 2004

Some recent advances in stability and optimality for the nonlinear receding horizon control (NRHC) or the nonlinear model predictive control (NMPC) are assessed. The NRHCs with terminal conditions are surveyed in terms of a terminal state equality constraint, a terminal cost, and a terminal constraint set. Other NRHCs without terminal conditions are surveyed in terms of a control Lyapunov function (CLF) and cost monotonicity. Additional approaches such as output feedback, fuzzy, and neural network are introduced. This paper excludes the results for linear receding horizon controls and concentrates only on the analytical results of NRHCs, not including applications of NRHCs. Stability and optimality are focused on rather than robustness.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.3
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• pp.155-164
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• 2023

About 75% of vessel collision accidents are caused by human error, which causes enormous economic loss, environmental pollution, and human casualties, thus research on automatic collision avoidance of vessels is being actively conducted. In addition, vessels must comply with the COLREGs rules stipulated by IMO when performing collision avoidance with other vessels in motion. In this study, the collision risk was calculated by estimating the position and velocity of other vessels through the Probabilistic Data Association Filter (PDAF) algorithm based on RADAR sensor data. When a collision risk is detected, we propose an event-triggered Nonlinear Model Predict Control (NMPC) algorithm that geometrically creates waypoints that satisfy COLREGs and follows them. To verify the proposed algorithm, simulations through MATLAB are performed.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.490-495
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• 1992

A neural model predictive control strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrained nonlinear optimization approach using successive quadratic programming cooperates with neural identification network is used to generate the optimum control law for the complicate continuous/batch chemical reactor systems that have inherent nonlinear dynamics. Based on our approach, we developed a neural model predictive controller(NMPC) which shows excellent performances on nonlinear, model-plant mismatch cases of chemical reactor systems.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.73-79
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• 2009

For the shake of control for movement object, control theory like neural network, nonlinear model predictive control(NMPC) is realized on digital high speed computer. Predictor of flight control system(FCS) based nonlinear model predictive control has to be satisfied with response for hard real-time to perform applications on each module in the FCS. Simultaneously, It gives a serious consideration accuracy to give full play to FCS's performance. Error of mathematical aspect affects realization of whole algorithm. But factors of bring mathematical error is not considered to calculate final accuracy on parameter of predictor. In this paper, Predictor was made using load control model on the digital computer for design FCS at hard real-time and is shown response time on realization algorithm. And is shown realization algorithm of high effective predictor over the accuracy. The predictor was realized on the load control model using Euler method, Heun method, Runge-Kutta and Taylor method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1443-1448
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• 2003

Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impounded by linear models due to the lack of a similarly accepted nonlinear modelling or data based technique. The authors have recently developed a new method for obtaining Volterra kernels of up to third order by use of pseudorandom M-sequence. By use of this method, nonparametric NMPC is derived in discrete-time using multi-dimensional convolution between plant data and Volterra kernel measurements. This approach is applied to an industrial polymerisation process using Volterra kernels of up to the third order. Results show that the nonparametric approach is very efficient and effective and considerably outperforms existing methods, while retaining the original data-based spirit and characteristics of linear MPC.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.2
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• pp.55-59
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• 2010

Recently it is tried to use load control model for maneuver moving object. MIN design method proposed to solve control problem of nonlinear system using load concept. The Min design method shows direct method for finding control value on the load control model. In this paper, is shown realization free falling model using nonlinear load control model and analysis of load values acting falling object according to disturbance. And made a trajectory according to acting load values due to disturbance. This paper's result is able to be applied to design algorithm for improvement accuracy of MLRS, GPS air-to-surface missile(ASM) and returning spacecraft with nonlinear model predictive control.