scholarly journals Self-Triggered Model Predictive Control for Linear Systems Based on Transmission of Control Input Sequences

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Koichi Kobayashi
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Linear Systems ◽  
Communication Networks ◽  
Predictive Control ◽  
Control Method ◽  
Input Sequence ◽  
Networked Control System ◽  
Control Input ◽  
Sampled Data

A networked control system (NCS) is a control system where components such as plants and controllers are connected through communication networks. Self-triggered control is well known as one of the control methods in NCSs and is a control method that for sampled-data control systems both the control input and the aperiodic sampling interval (i.e., the transmission interval) are computed simultaneously. In this paper, a self-triggered model predictive control (MPC) method for discrete-time linear systems with disturbances is proposed. In the conventional MPC method, the first one of the control input sequence obtained by solving the finite-time optimal control problem is sent and applied to the plant. In the proposed method, the first some elements of the control input sequence obtained are sent to the plant, and each element is sequentially applied to the plant. The number of elements is decided according to the effect of disturbances. In other words, transmission intervals can be controlled. Finally, the effectiveness of the proposed method is shown by numerical simulations.

scholarly journals Self-Triggered Model Predictive Control Using Optimization with Prediction Horizon One

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Koichi Kobayashi ◽  
Kunihiko Hiraishi
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Solution Method ◽  
Control Method ◽  
Sampling Period ◽  
Iterative Solution ◽  
Control Input ◽  
Sampled Data ◽  
Step Input ◽  
Constrained Problem

Self-triggered control is a control method that the control input and the sampling period are computed simultaneously in sampled-data control systems and is extensively studied in the field of control theory of networked systems and cyber-physical systems. In this paper, a new approach for self-triggered control is proposed from the viewpoint of model predictive control (MPC). First, the difficulty of self-triggered MPC is explained. To overcome this difficulty, two problems, that is, (i) the one-step input-constrained problem and (ii) theN-step input-constrained problem are newly formulated. By repeatedly solving either problem in each sampling period, the control input and the sampling period can be obtained, that is, self-triggered MPC can be realized. Next, an iterative solution method for the latter problem and an approximate solution method for the former problem are proposed. Finally, the effectiveness of the proposed approach is shown by numerical examples.

scholarly journals Research on Tracking Control of A Quadrotor UAV Based on Model Predictive Control and PD Control

2021 ◽  
Vol 2085 (1) ◽  
pp. 012008
Author(s):  
Jimin Yu ◽  
Zhi Yong ◽  
Yousi Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Tracking Control ◽  
Control Algorithm ◽  
Control Method ◽  
Control Input ◽  
Pd Control ◽  
Quadrotor Uav ◽  
Model Predictive Control Algorithm ◽  
Method Model

Abstract In order to solve the problem of path tracking of a quadrotor UAV, this paper proposes a track tracking control method which combines Model Predictive Control algorithm and PD control method. Model Predictive Control algorithm can generate control input for formation flight and track the specified trajectory. PD control can achieve rapid response to attitude and adjust error quickly. The simulation results verify the effectiveness of the proposed control method.

Model Predictive Control for Electrodynamic Tether Geometric Profile in Orbital Maneuvering with Finite Element State Estimator

2021 ◽  
Author(s):  
Gangqiang Li ◽  
Zheng H. Zhu
Keyword(s):  
Finite Element ◽  
Model Predictive Control ◽  
Electric Current ◽  
Predictive Control ◽  
Control Method ◽  
High Fidelity ◽  
Reference Trajectory ◽  
Control Input ◽  
State Estimator ◽  
Electrodynamic Tether

Abstract This paper studies the control of geometric profile of a librating electrodynamic tether by model predictive control using the induced electric current in tether only. First, a high-fidelity multiphysics model of an electrodynamic tether system is built based on the nodal position finite element method and the orbital-motion-limited theory. Second, a state estimator is proposed to estimate the geometric profile of a librating electrodynamic tether, where only the positions and velocities at the tether ends are measurable. The non-measurable geometric profile of tether between two ends is estimated by the high-fidelity multiphysics model with the input of the measurement at tether ends in the spatial domain. To avoid the singularity or ambiguity in the estimation, the geometric profile of tether is then propagated in the time domain by the extended Kalman filter. Third, the problem of controlling the geometric profile of a librating electrodynamic tether is converted into a trajectory tracking problem of the underactuated electrodynamic tether system, where the induced electric current in the tether is the only control input. The control input is optimized by the model predictive control method subject to the output and input control constraints. The numerical simulation results show that the proposed approach is capable of effectively controlling the shape of the liberating electrodynamic tether to the reference trajectory.

Sign in / Sign up

Export Citation Format

Share Document