scholarly journals Tracking Model Predictive Control for Docking Maneuvers of a CubeSat with a Big Spacecraft

Aerospace ◽  
2021 ◽  
Vol 8 (8) ◽  
pp. 197
Author(s):  
Fabrizio Stesina
Keyword(s):  
Predictive Control ◽  
High Performance ◽  
Complex Model ◽  
Model Predictive Controller ◽  
Environmental Disturbances ◽  
Small Satellites ◽  
Safety Constraints ◽  
Predictive Controller ◽  
Tracking Model ◽  
External Inspection

The release and retrieval of a CubeSat from a big spacecraft is useful for the external inspection and monitoring of the big spacecraft. However, docking maneuvers during the retrieval are challenging since safety constraints and high performance must be achieved, considering the small dimensions and the actual small satellites technology. The trajectory control is crucial to have a soft, accurate, quick, and propellant saving docking. The present paper deals with the design of a tracking model predictive controller (TMPC) tuned to achieve the stringent docking requirements for the retrieval of a CubeSat within the cargo bay of a large cooperative vehicle. The performance of the TMPC is verified using a complex model that includes non-linearities, uncertainties of the CubeSat parameters, and environmental disturbances. Moreover, 300 Monte Carlo runs demonstrate the robustness of the TMPC solution.

scholarly journals Trajectory Tracking and Stabilization of a Quadrotor Using Model Predictive Control of Laguerre Functions

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Mapopa Chipofya ◽  
Deok Jin Lee ◽  
Kil To Chong
Keyword(s):  
State Space ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Linear Quadratic Regulator ◽  
Laguerre Functions ◽  
Linear Quadratic ◽  
Model Predictive Controller ◽  
Predictive Controller ◽  
Linear State ◽  
Quadrotor System

This paper presents a solution to stability and trajectory tracking of a quadrotor system using a model predictive controller designed using a type of orthonormal functions called Laguerre functions. A linear model of the quadrotor is derived and used. To check the performance of the controller we compare it with a linear quadratic regulator and a more traditional linear state space MPC. Simulations for trajectory tracking and stability are performed in MATLAB and results provided in this paper.

scholarly journals A Novel Torque Coordination Control Strategy of a Single-Shaft Parallel Hybrid Electric Vehicle Based on Model Predictive Control

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Jing Sun ◽  
Guojing Xing ◽  
Xudong Liu ◽  
Xiaoling Fu ◽  
Chenghui Zhang
Keyword(s):  
Electric Vehicle ◽  
Predictive Control ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Coordination Control ◽  
Model Predictive Controller ◽  
Parallel Hybrid Electric Vehicle ◽  
Parallel Hybrid ◽  
Predictive Controller ◽  
Hybrid Electric

The torque coordination control during mode transition is a very important task for hybrid electric vehicle (HEV) with a clutch serving as the key enabling actuator element. Poor coordination will deteriorate the drivability of the driver and lead to excessive wearing to the clutch friction plates. In this paper, a novel torque coordination control strategy for a single-shaft parallel hybrid electric vehicle is presented to coordinate the motor torque, engine torque, and clutch torque so that the seamless mode switching can be achieved. Different to the existing model predictive control (MPC) methods, only one model predictive controller is needed and the clutch torque is taken as an optimized variable rather than a known parameter. Furthermore, the successful idea of model reference control (MRC) is also used for reference to generate the set-point signal required by MPC. The parameter sensitivity is studied for better performance of the proposed model predictive controller. The simulation results validate that the proposed novel torque coordination control strategy has less vehicle jerk, less torque interruption, and smaller clutch frictional losses, compared with the baseline method. In addition, the sensitivity and adaptiveness of the proposed novel torque coordination control strategy are evaluated.

scholarly journals Model Predictive Control of NCS with Data Quantization and Bounded Arbitrary Time Delays

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Jimin Yu ◽  
Yanan Xie ◽  
Xiaoming Tang
Keyword(s):  
Time Delay ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Infinite Horizon ◽  
Stability Result ◽  
Loop Model ◽  
Unified Framework ◽  
Model Predictive Controller ◽  
Predictive Controller

The model predictive control for constrained discrete time linear system under network environment is considered. The bounded time delay and data quantization are assumed to coexist in the data transmission link from the sensor to the controller. A novel NCS model is specially established for the model predictive control method, which casts the time delay and data quantization into a unified framework. A stability result of the obtained closed-loop model is presented by applying the Lyapunov method, which plays a key role in synthesizing the model predictive controller. The model predictive controller, which parameterizes the infinite horizon control moves into a single state feedback law, is provided which explicitly considers the satisfaction of input and state constraints. Two numerical examples are given to illustrate the effectiveness of the derived method.

Adaptive Generalized Predictive Controller and Cartesian Force Control for Robot Arm Using Dynamics and Geometric Identification

2018 ◽  
Vol 30 (6) ◽  
pp. 927-942 ◽  
Author(s):  
Shohei Hagane ◽  
Liz Katherine Rincon Ardila ◽  
Takuma Katsumata ◽  
Vincent Bonnet ◽  
Philippe Fraisse ◽  
...  
Keyword(s):  
Predictive Control ◽  
High Performance ◽  
Wall Painting ◽  
Robot Arm ◽  
Inertial Parameters ◽  
Control Scheme ◽  
Predictive Controller ◽  
Adaptive Predictive Control ◽  
Predictive Controllers ◽  
Contact Tasks

In realistic situations such as human-robot interactions or contact tasks, robots must have the capacity to adapt accordingly to their environment, other processes and systems. Adaptive model based controllers, that requires accurate dynamic and geometric robot’s information, can be used. Accurate estimations of the inertial and geometric parameters of the robot and end-effector are essential for the controller to demonstrate a high performance. However, the identification of these parameters can be time-consuming and complex. Thus, in this paper, a framework based on an adaptive predictive control scheme and a fast dynamic and geometric identification process is proposed. This approach was demonstrated using a KUKA lightweight robot (LWR) in the performance of a force-controlled wall-painting task. In this study, the performances of a generalized predictive control (GPC), adaptive proportional derivative gravity compensation, and adaptive GPC (AGPC) were compared. The results revealed that predictive controllers are more suitable than adaptive PD controllers with gravitational compensation, owing to the use of well-identified geometric and inertial parameters.

scholarly journals Hybrid Grey Wolf Optimization Nonlinear Model Predictive Control for Aircraft Engines Based on an Elastic BP Neural Network

2019 ◽  
Vol 9 (6) ◽  
pp. 1254 ◽  
Author(s):  
Lingfei Xiao ◽  
Min Xu ◽  
Yuhan Chen ◽  
Yusheng Chen
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Bp Neural Network ◽  
Aircraft Engine ◽  
Aircraft Engines ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Model Predictive Controller ◽  
Predictive Controller

In order to deal with control constraints and the performance optimization requirements in aircraft engines, a new nonlinear model predictive control method based on an elastic BP neural network with a hybrid grey wolf optimizer is proposed in this paper. Based on the acquired aircraft engines data, the elastic BP neural network is used to train the prediction model, and the grey wolf optimization algorithm is applied to improve the selection of initial parameters in the elastic BP neural network. The accuracy of network modeling is increased as a result. By introducing the logistics chaotic sequence, the individual optimal search mechanism, and the cross operation, the novel hybrid grey wolf optimization algorithm is proposed and then used in receding horizon optimization to ensure real-time operation. Subsequently, a nonlinear model predictive controller for aircraft engine is obtained. Simulation results show that, with constraints in the control signal, the proposed nonlinear model predictive controller can guarantee that the aircraft engine has a satisfactory performance.

A high performance model predictive controller:

2001 ◽  
Vol 9 (8) ◽  
pp. 829-835 ◽  
Author(s):  
Wim Van Brempt ◽  
Ton Backx ◽  
Jobert Ludlage ◽  
Peter Van Overschee ◽  
Bart De Moor ◽  
...  
Keyword(s):  
High Performance ◽  
Performance Model ◽  
Model Predictive Controller ◽  
Predictive Controller

Data-Based Research on Predictive Control of Multiple Mismatched Model Parameters

2013 ◽  
Vol 397-400 ◽  
pp. 1331-1336
Author(s):  
Da Kuo He ◽  
Jun Qi Xin ◽  
Wen Long Yuan ◽  
Qing Yun Yuan
Keyword(s):  
Control System ◽  
Predictive Control ◽  
Settling Time ◽  
Fuzzy Rules ◽  
Model Parameters ◽  
Step Size ◽  
Model Predictive Controller ◽  
Model Mismatch ◽  
Predictive Controller ◽  
Rising Time

The basis of the paper is that there are already some methods to accurately evaluate, test and diagnose the performance of the model predictive controller. And the result shows the reason of a bad performance of control system is because of model mismatch. There are much more complexity and variety in the problem of multiple mismatched parameters than single mismatched parameter, so we need consider more factors about it on the basis of the solution of single mismatched parameter. We propose a way of adjusting model parameters based on fuzzy rules when there are more than one mismatched parameters. The method is to adjust the step-size of parameters and get the adjustment rules on the basis of the changes of maximum overshoot, rising time and settling time. The last, verifying the method is effective by experiments.

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