An Effective Modeling and Control Strategy for Supply Chain Design and Planning

2011 ◽  
Vol 467-469 ◽  
pp. 1132-1135
Author(s):  
Hai Dong ◽  
Wei Ling Zhao ◽  
Yan Ping Li
Keyword(s):  
Supply Chain ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Planning Process ◽  
Supply Chain Design ◽  
Supply Chain Network ◽  
Modeling And Control ◽  
Work Model ◽  
And Control

The dynamics and uncertainty of the business and the market makes difficult to coordinate the activities of a supply chain. Therefore, it is important to review systematically and to take into account the variability in the planning formulation in order to manage a supply chain network efficiently. A novel stochastic multi-period design and planning MILP model of a multiechelon supply chain network is used as a predictive model in this work. Model predictive control is presented as a way to manage supply chain in the presence of uncertainty by incorporating unforeseen events into the planning process. Illustrative example shows control strategy based on model predictive control framework is effective in the supply chain network design and planning.

scholarly journals A Centralized Model Predictive Control Strategy for Dynamic Supply Chain Management

2013 ◽  
Vol 46 (9) ◽  
pp. 1608-1613 ◽  
Author(s):  
Dongfei Fu ◽  
Clara Mihaela Ionescu ◽  
El-Houssaine Aghezzaf ◽  
Robin De Keyser
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Chain Management ◽  
Centralized Model

An integrated control strategy of path following and lateral motion stabilization for autonomous distributed drive electric vehicles

2019 ◽  
pp. 095440701988416
Author(s):  
Yuan Zou ◽  
Ningyuan Guo ◽  
Xudong Zhang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Integrated Control ◽  
Path Following ◽  
Control Technique ◽  
Programming Algorithm ◽  
Linear Quadratic ◽  
And Control

This article proposes an integrated control strategy of autonomous distributed drive electric vehicles. First, to handle the multi-constraints and integrated problem of path following and the yaw motion control, a model predictive control technique is applied to determine optimal front wheels’ steering angle and external yaw moment synthetically and synchronously. For ensuring the desired path-tracking performance and vehicle lateral stability, a series of imperative state constraints and control references are transferred in the form of a matrix and imposed into the rolling optimization mechanism of model predictive control, where the detailed derivation is also illustrated and analyzed. Then, the quadratic programming algorithm is employed to optimize and distribute each in-wheel motor’s torque output. Finally, numerical simulation validations are carried out and analyzed in depth by comparing with a linear quadratic regulator–based strategy, proving the effectiveness and control efficacy of the proposed strategy.

Modeling and control through leadership of a refined flocking system

2014 ◽  
Vol 25 (02) ◽  
pp. 255-282 ◽  
Author(s):  
Alfio Borzì ◽  
Suttida Wongkaew
Keyword(s):  
Predictive Control ◽  
Control Strategy ◽  
Social Systems ◽  
Open Loop ◽  
Modeling And Control ◽  
Control Scheme ◽  
Nonlinear Conjugate Gradient ◽  
Optimality Systems ◽  
And Control ◽  
Conjugate Gradient Solver

A new refined flocking model that includes self-propelling, friction, attraction and repulsion, and alignment features is presented. This model takes into account various behavioral phenomena observed in biological and social systems. In addition, the presence of a leader is included in the system in order to develop a control strategy for the flocking model to accomplish desired objectives. Specifically, a model predictive control scheme is proposed that requires the solution of a sequence of open-loop optimality systems. An accurate Runge–Kutta scheme to discretize the optimality systems and a nonlinear conjugate gradient solver are implemented and discussed. Numerical experiments are performed that investigate the properties of the refined flocking model and demonstrate the ability of the control strategy to drive the flocking system to attain a desired target configuration and to follow a given trajectory.

scholarly journals Modeling and Control of Crane Overload Protection During Marine Lifting Operation Based on Model Predictive Control

2017 ◽  
Author(s):  
Zhengru Ren ◽  
Roger Skjetne ◽  
Zhen Gao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Degrees Of Freedom ◽  
Multiple Shooting ◽  
Control Input ◽  
Lumped Mass ◽  
Modeling And Control ◽  
Overload Protection ◽  
And Control

This paper deals with a nonlinear model predictive control (NMPC) scheme for a winch servo motor to overcome the sudden peak tension in the lifting wire caused by a lumped-mass payload at the beginning of a lifting off or a lowering operation. The crane-wire-payload system is modeled in 3 degrees of freedom with the Newton-Euler approach. Direct multiple shooting and real-time iteration (RTI) scheme are employed to provide feedback control input to the winch servo. Simulations are implemented with MATLAB and CaSADi toolkit. By well tuning the weighting matrices, the NMPC controller can reduce the snatch loads in the lifting wire and the winch loads simultaneously. A comparative study with a PID controller is conducted to verify its performance.

scholarly journals Decentralized Mesh-Based Model Predictive Control for Swarms of UAVs

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4324
Author(s):  
Salvatore Rosario Bassolillo ◽  
Egidio D’Amato ◽  
Immacolata Notaro ◽  
Luciano Blasi ◽  
Massimiliano Mattei
Keyword(s):  
Model Predictive Control ◽  
Unmanned Aerial Vehicles ◽  
Predictive Control ◽  
Control Strategy ◽  
Target Area ◽  
Guidance And Control ◽  
Aerial Vehicles ◽  
Control Scheme ◽  
And Control ◽  
Numerical Simulator

This paper deals with the design of a decentralized guidance and control strategy for a swarm of unmanned aerial vehicles (UAVs), with the objective of maintaining a given connection topology with assigned mutual distances while flying to a target area. In the absence of obstacles, the assigned topology, based on an extended Delaunay triangulation concept, implements regular and connected formation shapes. In the presence of obstacles, this technique is combined with a model predictive control (MPC) that allows forming independent sub-swarms optimizing the formation spreading to avoid obstacles and collisions between neighboring vehicles. A custom numerical simulator was developed in a Matlab/Simulink environment to prove the effectiveness of the proposed guidance and control scheme in several 2D operational scenarios with obstacles of different sizes and increasing number of aircraft.

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