scholarly journals Hierarchical Model Predictive Control for Autonomous Collision Avoidance of Distributed Electric Drive Vehicle with Lateral Stability Analysis in Extreme Scenarios

2021 ◽  
Vol 12 (4) ◽  
pp. 192
Author(s):  
Bowen Wang ◽  
Cheng Lin ◽  
Sheng Liang ◽  
Xinle Gong ◽  
Zhenyi Tao
Keyword(s):  
Model Predictive Control ◽  
Collision Avoidance ◽  
Predictive Control ◽  
Hierarchical Model ◽  
Electric Drive ◽  
Lateral Displacement ◽  
Mixed Integer ◽  
Lateral Stability ◽  
Integer Quadratic Programming ◽  
Saturation Region

This paper proposes an active collision avoidance controller based on a hierarchical model predictive control framework for distributed electric drive vehicles (4IDEV) considering extreme conditions. In this framework, a two-layer strategy is developed. The upper layer is the path replanning controller based on nonlinear MPC (nMPC), from which a collision-free path including the optimal lateral displacement and yaw angle can be obtained in real-time while encountering the obstacles. The lower layer is the path tracking controller based on hybrid MPC (hMPC), and the coordinated control inputs (yaw moment and the front wheel steering angle) are solved by a Mixed-Integer Quadratic Programming (MIQP) with the piecewise affine (PWA) tire model considering tire saturation region. Moreover, to improve the lateral stability when tracking, the stable zone of lateral stability in the high-risk condition is analyzed based on the phase portrait method, by which the constraints of vehicle states and inputs are derived. The verification is carried out on the MATLAB and CarSim co-simulation platform, and the simulation results show that the proposed active collision avoidance controller can track the reference path accurately and prevent vehicle instability in extreme scenarios.

Regenerative Vibration Control of Tall Buildings Using Model Predictive Control

2013 ◽  
Author(s):  
Yilun Liu ◽  
Lei Zuo ◽  
Xiudong Tang
Keyword(s):  
Model Predictive Control ◽  
Vibration Control ◽  
Predictive Control ◽  
Tall Buildings ◽  
Mixed Integer ◽  
Optimal Controller ◽  
Control Force ◽  
Online Computation ◽  
Integer Quadratic Programming ◽  
Mixed Integer Quadratic Programming

The regenerative Tuned Mass Damper (TMD) can convert the vibration energy of the tall building into the electricity, by replacing the damping element with electromagnetic harvester. The energy harvesting circuit therein which can regulate the electricity and control the vibration will introduce some constraints when designing vibration controller. This paper designed the vibration controller based on Model Predictive Control (MPC). The control force constraints were taken into consideration before designing the controller. The building model with semi-active constraints due to the regenerative properties of the TMD is converted into a Mixed Logical Dynamical (MLD) system first. Then the optimal controller is designed by solving the Mixed Integer Quadratic Programming (MIQP) problem. The results were evaluated and compared to the ones using “clipped-optimal” controller with the same constraints. It is found that the MPC controller can provide the same or better vibration control Results depending on the predicted horizon. Besides, an explicit MPC is obtained to reduce the online computation effort.

scholarly journals Phase Balancing Home Energy Management System Using Model Predictive Control

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3323 ◽  
Author(s):  
Bharath Rao ◽  
Friederich Kupzog ◽  
Martin Kozek
Keyword(s):  
Model Predictive Control ◽  
Energy Management ◽  
Predictive Control ◽  
Management System ◽  
Mixed Integer ◽  
Energy Management System ◽  
Integer Quadratic Programming ◽  
Three Phase ◽  
Home Energy Management ◽  
Home Energy Management System

Most typical distribution networks are unbalanced due to unequal loading on each of the three phases and untransposed lines. In this paper, models and methods which can handle three-phase unbalanced scenarios are developed. The authors present a novel three-phase home energy management system to control both active and reactive power to provide per-phase optimization. Simplified single-phase algorithms are not sufficient to capture all the complexities a three-phase unbalance system poses. Distributed generators such as photo-voltaic systems, wind generators, and loads such as household electric and thermal demand connected to these networks directly depend on external factors such as weather, ambient temperature, and irradiation. They are also time dependent, containing daily, weekly, and seasonal cycles. Economic and phase-balanced operation of such generators and loads is very important to improve energy efficiency and maximize benefit while respecting consumer needs. Since homes and buildings are expected to consume a large share of electrical energy of a country, they are the ideal candidate to help solve these issues. The method developed will include typical distributed generation, loads, and various smart home models which were constructed using realistic models representing typical homes in Austria. A control scheme is provided which uses model predictive control with multi-objective mixed-integer quadratic programming to maximize self-consumption, user comfort and grid support.

scholarly journals Cooperative path-planning and tracking controller evaluation using vehicle models of varying complexities

2020 ◽  
pp. 095440622094546
Author(s):  
Husain Kanchwala ◽  
Icaro Bezerra Viana ◽  
Nabil Aouf
Keyword(s):  
Path Planning ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Mixed Integer ◽  
Distributed Model ◽  
Programming Approach ◽  
High Fidelity ◽  
Vehicle Model ◽  
Integer Quadratic Programming ◽  
Tracking Controller

This paper discusses cooperative path-planning and tracking controller for autonomous vehicles using a distributed model predictive control approach. Mixed-integer quadratic programming approach is used for optimal trajectory generation using a linear model predictive control for path-tracking. Cooperative behaviour is introduced by broadcasting the planned trajectories of two connected automated vehicles. The controller generates steering and torque inputs. The steering and drive motor actuator constraints are incorporated in the control law. Computational simulations are performed to evaluate the controller for vehicle models of varying complexities. A 12-degrees-of-freedom vehicle model is developed and is subsequently linearised to be used as the plant model for the linearised model predictive control-based tracking controller. The model behaviour is compared against the kinematic, bicycle and the sophisticated high-fidelity multi-body dynamics CarSim model of the vehicle. Vehicle trajectories used for tracking are longitudinal and lateral positions, velocities and yaw rate. A cooperative obstacle avoidance manoeuvre is performed at different speeds using a co-simulation between the controller model in Simulink and the high-fidelity vehicle model in CarSim. The simulation results demonstrate the effectiveness of the proposed method.

Model predictive control for integrated longitudinal and lateral stability of electric vehicles with in-wheel motors

2020 ◽  
Vol 14 (18) ◽  
pp. 2741-2751
Author(s):  
Lin Zhang ◽  
Hong Chen ◽  
Yanjun Huang ◽  
Hongyan Guo ◽  
Haobo Sun ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Electric Vehicles ◽  
Lateral Stability

Contract-based Hierarchical Model Predictive Control and Planning for Autonomous Vehicle

2020 ◽  
Vol 53 (2) ◽  
pp. 15758-15764
Author(s):  
Mohamed Ibrahim ◽  
Markus Kögel ◽  
Christian Kallies ◽  
Rolf Findeisen
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Hierarchical Model ◽  
Autonomous Vehicle
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