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.

NEURAL MODELING AND CONTROL OF DYNAMIC SYSTEMS WITH HYSTERESIS

2007 ◽  
Vol 31 (1) ◽  
pp. 127-141
Author(s):  
Yonghong Tan ◽  
Xinlong Zhao
Keyword(s):  
Dynamic Systems ◽  
Control Strategy ◽  
Neural Modeling ◽  
Modeling And Control ◽  
Control Scheme ◽  
The Neural Networks ◽  
Adaptive Control Strategy ◽  
Set Up ◽  
Modeling Strategy ◽  
And Control

A hysteretic operator is proposed to set up an expanded input space so as to transform the multi-valued mapping of hysteresis to a one-to-one mapping so that the neural networks can be applied to model of the behavior of hysteresis. Based on the proposed neural modeling strategy for hysteresis, a pseudo control scheme is developed to handle the control of nonlinear dynamic systems with hysteresis. A neural estimator is constructed to predict the system residual so that it avoids constructing the inverse model of hysteresis. Thus, the control strategy can be used for the case where the output of hysteresis is unmeasurable directly. Then, the corresponding adaptive control strategy is presented. The application of the novel modeling approach to hysteresis in a piezoelectric actuator is illustrated. Then a numerical example of using the proposed control strategy for a nonlinear system with hysteresis is presented.

Modeling and Control of a Rotary Crane

1985 ◽  
Vol 107 (3) ◽  
pp. 200-206 ◽  
Author(s):  
Y. Sakawa ◽  
A. Nakazumi
Keyword(s):  
Feedback Control ◽  
Equilibrium State ◽  
Open Loop ◽  
The State ◽  
Control Input ◽  
Loop Control ◽  
Modeling And Control ◽  
Control Scheme ◽  
Rotary Crane ◽  
And Control

In this paper we first derive a dynamical model for the control of a rotary crane, which makes three kinds of motion (rotation, load hoisting, and boom hoisting) simultaneously. The goal is to transfer a load to a desired place in such a way that at the end of transfer the swing of the load decays as quickly as possible. We first apply an open-loop control input to the system such that the state of the system can be transferred to a neighborhood of the equilibrium state. Then we apply a feedback control signal so that the state of the system approaches the equilibrium state as quickly as possible. The results of computer simulation prove that the open-loop plus feedback control scheme works well.

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.

Research of Modeling and Control Method for Electromagnetic Levitation System

2014 ◽  
Vol 651-653 ◽  
pp. 812-817 ◽  
Author(s):  
Jian Guo Zheng ◽  
Zhi Gang Zou ◽  
Hui Zeng ◽  
Tian Peng He
Keyword(s):  
Control Method ◽  
State Space Model ◽  
Electromagnetic Levitation ◽  
Open Loop ◽  
Modeling And Control ◽  
Control Approach ◽  
Control Scheme ◽  
Levitation System ◽  
And Control ◽  
Assignment Scheme

There has been wide interest in the control scheme of the electromagnetic levitation system due to its disadvantages of nonlinearity and open-loop uncertainty. A typical coil-ball levitation system is used in research. The forces of the ball are analyzed and a dynamic model of the whole electromagnetic levitation system is established. Based on the nonlinear state-space model, the coil-ball system is linearized and then a LQR control approach is proposed. Simulation results show that, compared with conventional pole assignment scheme, the electromagnetic levitation system under the proposed control approach gets a better performance, including smaller overshot and faster response.

MLD Systems: Modeling and Control — Experience With a Pilot Process

2001 ◽  
Author(s):  
W. Colmenares ◽  
S. Cristea ◽  
C. de Prada ◽  
O. Perez ◽  
A. Alonso ◽  
...  
Keyword(s):  
Predictive Control ◽  
Control Strategy ◽  
Closed Loop ◽  
Closed Loop System ◽  
Modeling And Control ◽  
Integer Variables ◽  
Under Construction ◽  
And Control ◽  
Mixed Logical Dynamical System ◽  
Mixed Logical Dynamical

Abstract In this report, we present results of the modeling and control of a hydraulic pilot process, currently under construction at the Laboratory of Automatic of the ISA department of Universidad de Valladolid. The system is described by linear inequalities involving both, real and integer variables and the dynamical and logical decisions are heavily inter dependent. Hence the characterization as a Mixed Logical Dynamical system. Two MLD models are featured and both are suited to apply a Model Based Predictive Control strategy to command the system. Results of a simulation of the closed loop system are feature.

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 Switching System oriented Modeling and Control Strategy for Idle Speed Control of a Hybrid Powertrain

2020 ◽  
Vol 53 (2) ◽  
pp. 14028-14033
Author(s):  
Micha S. Obergfell ◽  
Steven X. Ding ◽  
Frank Wobbe ◽  
Christoph-Marian Goletz ◽  
Michael Folkers ◽  
...  
Keyword(s):  
Control Strategy ◽  
Speed Control ◽  
Switching System ◽  
Hybrid Powertrain ◽  
Modeling And Control ◽  
Idle Speed ◽  
Idle Speed Control ◽  
And Control

Biostability in distribution systems in one city in southern China: Characteristics, modeling and control strategy

2014 ◽  
Vol 26 (2) ◽  
pp. 323-331 ◽  
Author(s):  
Pinpin Lu ◽  
Xiaojian Zhang ◽  
Chiqian Zhang ◽  
Zhangbin Niu ◽  
Shuguang Xie ◽  
...  
Keyword(s):  
Control Strategy ◽  
Distribution Systems ◽  
Southern China ◽  
Modeling And Control ◽  
And Control

The Research of Air Precooling System Based on Generalize Predictive Control Strategy

2013 ◽  
Vol 433-435 ◽  
pp. 1091-1098
Author(s):  
Wei Bo Yu ◽  
Cui Yuan Feng ◽  
Ting Ting Yang ◽  
Hong Jun Li
Keyword(s):  
Predictive Control ◽  
Control Strategy ◽  
Control Algorithm ◽  
Exchange Process ◽  
Generalized Predictive Control ◽  
Matlab Simulation ◽  
Control Effect ◽  
Heat Exchange Process ◽  
Complex Control System ◽  
And Control

The air precooling system heat exchange process is a complex control system with features such as: nonlinear, lag and random interference. So choose Generalized Predictive Control Algorithm that has low model dependence, good robustness and control effect, as well as easy to implement. But due to the large amount of calculation of traditional generalized predictive control and can't juggle quickness and overshoot problem, an improved generalized predictive control algorithm is proposed, then carry out the MATLAB simulation, the experimental results show that the algorithm can not only greatly reduce the amount of computation, but also can restrain the overshoot and its rapidity.

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