Distributed Nonlinear Estimation: A Recursive Optimization Approach

Author(s):  
Yidi Teng ◽  
Bo Chen ◽  
Shouzhao Sheng
Author(s):  
Yiqun Dong ◽  
Zhixiang Liu ◽  
Bin Yu ◽  
Youmin Zhang

This paper discusses a position and height limitation control for a quadrotor UAV (Unmanned Aerial Vehicle) using Model Predictive Control (MPC) approach. Nonlinear dynamics of the quadrotor is discussed first, and decoupled linearized dynamics is obtained. For the implementation of MPC, extended state vector of vehicle is generated, and augmented linear dynamics is constructed. The MPC in this paper utilizes a set of Laguerre function as basis to approximate the future movement of modeled vehicle. Position/height constraints and vehicle actuator characteristics enter the dynamics as linearized inequalities, which could be solved on-line via a recursive optimization approach. While validations based on experimental tests will be conducted in future, currently simulations have been completed. Based on the simulation results, when state of the vehicle is laid within the permissible bound, it retains the same dynamics of original vehicle. However, if predicted response exceeds the limits, however, MPC will take effect and restrict associate vehicle states. The discussed MPC framework in this paper is considered to be applicable.


2020 ◽  
Vol 54 (6) ◽  
pp. 1703-1722 ◽  
Author(s):  
Narges Soltani ◽  
Sebastián Lozano

In this paper, a new interactive multiobjective target setting approach based on lexicographic directional distance function (DDF) method is proposed. Lexicographic DDF computes efficient targets along a specified directional vector. The interactive multiobjective optimization approach consists in several iteration cycles in each of which the Decision Making Unit (DMU) is presented a fixed number of efficient targets computed corresponding to different directional vectors. If the DMU finds one of them promising, the directional vectors tried in the next iteration are generated close to the promising one, thus focusing the exploration of the efficient frontier on the promising area. In any iteration the DMU may choose to finish the exploration of the current region and restart the process to probe a new region. The interactive process ends when the DMU finds its most preferred solution (MPS).


2016 ◽  
Vol 18 (1) ◽  
pp. 114
Author(s):  
She Wei ◽  
Huang Huang ◽  
Guan Chunyun ◽  
Chen Fu ◽  
Chen Guanghui

Author(s):  
Obaid Ur Rehman ◽  
Ian R. Petersen ◽  
Hongbin Song ◽  
Elanor H. Huntington

Sign in / Sign up

Export Citation Format

Share Document