scholarly journals Model Predictive Control for Cooperative Transportation with Feasibility-Aware Policy

Robotics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 84
Author(s):  
Badr Elaamery ◽  
Massimo Pesavento ◽  
Teresa Aldovini ◽  
Nicola Lissandrini ◽  
Giulia Michieletto ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Optimal Choice ◽  
Reference Trajectory ◽  
Cluttered Environments ◽  
Cooperative Strategies ◽  
Main Challenge ◽  
Robot Systems ◽  
Conservative Policy ◽  
Task Accomplishment

The transportation of large payloads can be made possible with Multi-Robot Systems (MRS) implementing cooperative strategies. In this work, we focus on the coordinated MRS trajectory planning task exploiting a Model Predictive Control (MPC) framework addressing both the acting robots and the transported load. In this context, the main challenge is the possible occurrence of a temporary mismatch among agents’ actions with consequent formation errors that can cause severe damage to the carried load. To mitigate this risk, the coordination scheme may leverage a leader–follower approach, in which a hierarchical strategy is in place to trade-off between the task accomplishment and the dynamics and environment constraints. Nonetheless, particularly in narrow spaces or cluttered environments, the leader’s optimal choice may lead to trajectories that are infeasible for the follower and the load. To this aim, we propose a feasibility-aware leader–follower strategy, where the leader computes a reference trajectory, and the follower accounts for its own and the load constraints; moreover, the follower is able to communicate the trajectory infeasibility to the leader, which reacts by temporarily switching to a conservative policy. The consistent MRS co-design is allowed by the MPC formulation, for both the leader and the follower: here, the prediction capability of MPC is key to guarantee a correct and efficient execution of the leader–follower coordinated action. The approach is formally stated and discussed, and a numerical campaign is conducted to validate and assess the proposed scheme, with respect to different scenarios with growing complexity.

scholarly journals Lactate-Based Model Predictive Control Strategy of Cell Growth for Cell Therapy Applications

2020 ◽  
Vol 7 (3) ◽  
pp. 78
Author(s):  
Kathleen Van Beylen ◽  
Ali Youssef ◽  
Alberto Peña Fernández ◽  
Toon Lambrechts ◽  
Ioannis Papantoniou ◽  
...  
Keyword(s):  
Cell Growth ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Feeding Strategy ◽  
A Priori ◽  
Process Models ◽  
Feeding Strategies ◽  
Cell Culture Process ◽  
Reference Trajectory ◽  
Set Up

Implementing a personalised feeding strategy for each individual batch of a bioprocess could significantly reduce the unnecessary costs of overfeeding the cells. This paper uses lactate measurements during the cell culture process as an indication of cell growth to adapt the feeding strategy accordingly. For this purpose, a model predictive control is used to follow this a priori determined reference trajectory of cumulative lactate. Human progenitor cells from three different donors, which were cultivated in 12-well plates for five days using six different feeding strategies, are used as references. Each experimental set-up is performed in triplicate and for each run an individualised model-based predictive control (MPC) controller is developed. All process models exhibit an accuracy of 99.80% ± 0.02%, and all simulations to reproduce each experimental run, using the data as a reference trajectory, reached their target with a 98.64% ± 0.10% accuracy on average. This work represents a promising framework to control the cell growth through adapting the feeding strategy based on lactate measurements.

A Model Predictive Control Strategy for Lateral and Longitudinal Dynamics in Autonomous Driving

2020 ◽  
Author(s):  
Irfan Khan ◽  
Stefano Feraco ◽  
Angelo Bonfitto ◽  
Nicola Amati
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Autonomous Driving ◽  
Maximum Speed ◽  
Reference Trajectory ◽  
Vehicle Speed ◽  
The Road ◽  
Speed Regulation ◽  
Road Geometry

Abstract This paper presents a controller dedicated to the lateral and longitudinal vehicle dynamics control for autonomous driving. The proposed strategy exploits a Model Predictive Control strategy to perform lateral guidance and speed regulation. To this end, the algorithm controls the steering angle and the throttle and brake pedals for minimizing the vehicle’s lateral deviation and relative yaw angle with respect to the reference trajectory, while the vehicle speed is controlled to drive at the maximum acceptable longitudinal speed considering the adherence and legal speed limits. The technique exploits data computed by a simulated camera mounted on the top of the vehicle while moving in different driving scenarios. The longitudinal control strategy is based on a reference speed generator, which computes the maximum speed considering the road geometry and lateral motion of the vehicle at the same time. The proposed controller is tested in highway, interurban and urban driving scenarios to check the performance of the proposed method in different driving environments.

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.

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 Model predictive driver model considering the steering characteristics of the skilled drivers

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401982933 ◽  
Author(s):  
Haobin Jiang ◽  
Huan Tian ◽  
Yiding Hua
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Single Point ◽  
Driver Model ◽  
General Regression Neural Network ◽  
Reference Trajectory ◽  
Model Based ◽  
Vehicle Trajectories ◽  
Steering Characteristics ◽  
General Regression

First, the experienced drivers with good driving skills are used as objects of learning and road steering test data of skilled drivers are collected in this article. To better simulate human drivers, skilled drivers’ steering characteristics are analyzed under different steering conditions. Vehicle trajectories of skilled drivers are fitted by general regression neural network, and the ideal path trajectory is obtained. Second, the model predictive control algorithm is used to build the driver model. According to the requirements of quickly and steadily tracking the track of skilled drivers, vehicle kinematics model is established. The objective function and the corresponding constraint conditions of the driver model based on model predictive control were determined. Finally, numerical simulations results demonstrate that the driver model based on model predictive control can accurately track the reference trajectory of skilled drivers under the four typical steering conditions, and the tracking effect is better than the traditional single-point preview driver model and path tracking method based on a β-spline curve.

Integration of Optimization and Model Predictive Control of an Intensified Continuous Three-Phase Catalytic Reactor

2015 ◽  
Vol 13 (1) ◽  
pp. 51-62 ◽  
Author(s):  
Li Shi
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
State Space Model ◽  
Reference Trajectory ◽  
Catalytic Reactor ◽  
Catalytic Reactors ◽  
Three Phase ◽  
Highly Nonlinear ◽  
Steady State Model ◽  
Control Layer

Abstract Intensified continuous three-phase catalytic reactors working in high-pressure and -temperature conditions are particularly effective at coping with mass transfer limitations during three-phase catalytic reactions. They are highly nonlinear, multivariable systems and behave differently from conventional batch, fed-batch or continuous non-intensified reactors. This paper deals with an integration of real-time optimization and model predictive control (RTO–MPC) of an intensified continuous three-phase catalytic reactor. A steady-state model developed by regression method is used in optimization layer and gives the reference trajectory for control layer. At control layer, a linear MPC is proposed based on identified state space model. The performance of RTO–MPC is illustrated by simulation

scholarly journals Model Predictive Control of Grid Forming Converters with Enhanced Power Quality

2020 ◽  
Vol 10 (18) ◽  
pp. 6390
Author(s):  
Mohammed Alhasheem ◽  
Ahmed Abdelhakim ◽  
Frede Blaabjerg ◽  
Paolo Mattavelli ◽  
Pooya Davari
Keyword(s):  
Model Predictive Control ◽  
Power Quality ◽  
Predictive Control ◽  
Single Step ◽  
Voltage Source ◽  
Voltage Source Converter ◽  
Reference Trajectory ◽  
Computation Efficiency ◽  
Switching Behaviour ◽  
Control Schemes

This paper proposes an enhanced finite control set model predictive control (FCS-MPC) strategy for voltage source converter (VSC) with a LC output filter. The proposed control scheme is based on tracking the voltage reference trajectory by using only a single-step prediction within the controller horizon. Besides, the suitability of different frequency control schemes with the proposed scheme to prevent from inherent variable switching behaviour of conventional FCS-MPC is investigated. Based on that, the proposed method targets two major factors influencing power quality in grid forming applications by enhancing the output voltage harmonic distortion and also preventing variable switching behaviour of FCS-MPC. Although compared to multi-step prediction approaches, only a single-step multi-objective cost function to improve computation efficiency is utilized, the introduced control schemes are able to deliver higher power quality compared to its counterpart methods as well. Furthermore, the effect of different applied cost functions on the transient response of the system is studied and investigated for the future use of the VSC in microgrids (MGs). The effectiveness of the proposed scheme was assessed by simulation using MATLAB/SIMULINK and experiment using a 5.5 kVA VSC module and the results were in good agreement.

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