Model Predictive Control of an Electro-Hydraulic Powertrain With Energy Storage

2011 ◽  
Author(s):  
Timothy O. Deppen ◽  
Andrew G. Alleyne ◽  
Kim A. Stelson ◽  
Jonathan J. Meyer
Keyword(s):  
Energy Storage ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Reference Trajectory ◽  
Management Problem ◽  
Hybrid Powertrain ◽  
Engine Operation ◽  
Control Approach ◽  
Hydraulic Hybrid ◽  
Battery Technology

This paper presents a model predictive control approach to solving the energy management problem within a series hydraulic hybrid powertrain. The hydraulic hybrid utilizes a high pressure accumulator for energy storage which has superior power density than conventional battery technology. This makes fluid power attractive for urban driving applications in which there are frequent starts and stops and large startup power demands. Model predictive control was chosen for control design because this technique requires no information about the future drive cycle, which can be highly uncertain in urban settings. The proposed control strategy was validated experimentally using an electro-hydraulic powertrain testbed which includes energy storage. The experimental study demonstrates the controller’s ability to track a reference trajectory while achieving efficient engine operation.

scholarly journals Optimal Load and Energy Management of Aircraft Microgrids Using Multi-Objective Model Predictive Control

2021 ◽  
Vol 13 (24) ◽  
pp. 13907
Author(s):  
Xin Wang ◽  
Jason Atkin ◽  
Najmeh Bazmohammadi ◽  
Serhiy Bozhko ◽  
Josep M. Guerrero
Keyword(s):  
Energy Storage ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Evaluation Framework ◽  
Operation Management ◽  
System Level ◽  
Mixed Integer ◽  
Battery Life ◽  
Management Problem ◽  
Objective Functions

Safety issues related to the electrification of more electric aircraft (MEA) need to be addressed because of the increasing complexity of aircraft electrical power systems and the growing number of safety-critical sub-systems that need to be powered. Managing the energy storage systems and the flexibility in the load-side plays an important role in preserving the system’s safety when facing an energy shortage. This paper presents a system-level centralized operation management strategy based on model predictive control (MPC) for MEA to schedule battery systems and exploit flexibility in the demand-side while satisfying time-varying operational requirements. The proposed online control strategy aims to maintain energy storage (ES) and prolong the battery life cycle, while minimizing load shedding, with fewer switching activities to improve devices lifetime and to avoid unnecessary transients. Using a mixed-integer linear programming (MILP) formulation, different objective functions are proposed to realize the control targets, with soft constraints improving the feasibility of the model. In addition, an evaluation framework is proposed to analyze the effects of various objective functions and the prediction horizon on system performance, which provides the designers and users of MEA and other complex systems with new insights into operation management problem formulation.

Tracking control of multiple unmanned aerial vehicles incorporating disturbance observer and model predictive approach

2019 ◽  
Vol 42 (5) ◽  
pp. 951-964 ◽  
Author(s):  
Boyang Zhang ◽  
Xiuxia Sun ◽  
Shuguang Liu ◽  
Xiongfeng Deng
Keyword(s):  
Model Predictive Control ◽  
Unmanned Aerial Vehicle ◽  
Predictive Control ◽  
Disturbance Observer ◽  
Control Method ◽  
Differential Evolution Algorithm ◽  
Reference Trajectory ◽  
Control Approach ◽  
Unknown Disturbances ◽  
Aerial Vehicle

This paper studies the disturbance observer-based model predictive control approach to deal with the unmanned aerial vehicle formation flight with unknown disturbances. The distributed control problem for a class of multiple unmanned aerial vehicle systems with reference trajectory tracking and disturbance rejection is formulated. Firstly, a local distributed controller is designed by using the model predictive control method to achieve stable tracking, where the local optimization problem is solved by an adaptive differential evolution algorithm. Then, a feedforward compensation controller is introduced by using a disturbance observer to estimate and compensate disturbances, and improve the ability of anti-interference. Besides, the stability of the proposed composite controller is analyzed as well. Finally, the simulation examples are provided to illustrate the validity of proposed control structure.

scholarly journals Real-Time Model Predictive Control of Human Bodyweight Based on Energy Intake

2019 ◽  
Vol 9 (13) ◽  
pp. 2609 ◽  
Author(s):  
Peña Fernández ◽  
Youssef ◽  
Heeren ◽  
Matthys ◽  
Aerts
Keyword(s):  
Body Weight ◽  
Model Predictive Control ◽  
Real Time ◽  
Energy Intake ◽  
Predictive Control ◽  
Reference Trajectory ◽  
Time Model ◽  
Control Approach ◽  
Overweight People ◽  
Mean Square Errors

The number of overweight people reached 1.9 billion in 2016. Lifespan decrease and many diseases have been linked to obesity. Efficient ways to monitor and control body weight are needed. The objective of this work is to explore the use of a model predictive control approach to manage bodyweight in response to energy intake. The analysis is performed based on data obtained during the Minnesota starvation experiment, with weekly measurements on body weight and energy intake for 32 male participants over the course of 27 weeks. A first order dynamic auto-regression with exogenous variables model exhibits the best prediction, with an average mean relative prediction error value of 1.01 ± 0.02% for 1 week-ahead predictions. Then, the performance of a model predictive control algorithm, following a predefined bodyweight trajectory, is tested. Root mean square errors of 0.30 ± 0.06 kg and 9 ± 3 kcal day-1 are found between the desired target and simulated bodyweights, and between the measured energy intake and advised by the controller energy intake, respectively. The model predictive control approach for bodyweight allows calculating the needed energy intake in order to follow a predefined target bodyweight reference trajectory. This study shows a first possible step towards real-time active control of human bodyweight.

scholarly journals Optimal Load and Energy Management of Aircraft Microgrids Using Multi-Objective Model Predictive Control

2021 ◽  
Author(s):  
Xin Wang ◽  
Jason Atkin ◽  
Najmeh Bazmohammadi ◽  
Serhiy Bozhko ◽  
and Josep M. Guerrero
Keyword(s):  
Energy Storage ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Evaluation Framework ◽  
Operation Management ◽  
System Level ◽  
Mixed Integer ◽  
Battery Life ◽  
Management Problem ◽  
Objective Functions

Abstract: Safety issues related to the electrification of more electric aircraft (MEA) need to be addressed because of the increasing complexity of aircraft electrical power systems and the growing number of safety-critical sub-systems that need to be powered. Managing the energy storage systems and the flexibility in the load-side plays an important role in preserving the system’s safety when facing an energy shortage. This paper presents a system-level centralized operation management strategy based on model predictive control (MPC) for MEA to schedule battery systems and exploit flexibility in the demand-side while satisfying time-varying operational requirements. The proposed online control strategy aims to maintain energy storage (ES) and prolong the battery life cycle, while minimizing load shedding, with fewer switching activities to improve devices lifetime and to avoid unnecessary transients. Using a mixed-integer linear programming (MILP) formulation, different objective functions are proposed to realize the control targets, with soft constraints improving the robustness of the model. Besides, an evaluation framework is proposed to analyze the effects of various objective functions and the prediction horizon on system performance, which provides the designers and users of MEA and other complex systems with new insights into operation management problem formulation.

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