scholarly journals Vehicle Dynamics Control Using Model Predictive Control Allocation Combined with an Adaptive Parameter Estimator

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Karan Chatrath ◽  
Yanggu Zheng ◽  
Barys Shyrokau
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Vehicle Dynamics ◽  
Control Allocation ◽  
Parameter Estimator ◽  
Adaptive Parameter ◽  
Vehicle Dynamics Control

Direct Longitudinal Force Feedback for High-Performance Vehicle Dynamics Control Systems

2021 ◽  
Author(s):  
Giorgio Riva ◽  
Luca Mozzarelli ◽  
Matteo Corno ◽  
Simone Formentin ◽  
Sergio M. Savaresi
Keyword(s):  
Model Predictive Control ◽  
Control Systems ◽  
Predictive Control ◽  
Vehicle Dynamics ◽  
High Performance ◽  
Force Feedback ◽  
Longitudinal Force ◽  
Spiral Path ◽  
Comparable Performance ◽  
Vehicle Dynamics Control

Abstract State of the art vehicle dynamics control systems do not exploit tire road forces information, even though the vehicle behaviour is ultimately determined by the tire road interaction. Recent technological improvements allow to accurately measure and estimate these variables, making it possible to introduce such knowledge inside a control system. In this paper, a vehicle dynamics control architecture based on a direct longitudinal tire force feedback is proposed. The scheme is made by a nested architecture composed by an outer Model Predictive Control algorithm, written in spatial coordinates, and an inner longitudinal force feedback controller. The latter is composed by four classical Proportional-Integral controllers in anti-windup configuration, endowed with a suitably designed gain switching logic to cope with possible unfeasible references provided by the outer loop, avoiding instability. The proposed scheme is tested in simulation in a challenging scenario where the tracking of a spiral path on a slippery surface and the timing performance are handled simultaneously by the controller. The performance is compared with that of an inner slip-based controller, sharing the same outer Model Predictive Control loop. The results show comparable performance in presence of unfeasible force references, while higher robustness is achieved with respect to friction curve uncertainties.

scholarly journals Model Predictive Control With Learned Vehicle Dynamics for Autonomous Vehicle Path Tracking

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 128233-128249
Author(s):  
Mohammad Rokonuzzaman ◽  
Navid Mohajer ◽  
Saeid Nahavandi ◽  
Shady Mohamed
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Vehicle Dynamics ◽  
Autonomous Vehicle ◽  
Path Tracking ◽  
Vehicle Path

SET POINT TRACKING CONTROL OF A REMOTELY OPERATED VEHICLE USING MODEL PREDICTIVE CONTROL

2021 ◽  
Vol 162 (A1) ◽  
Author(s):  
M P R Prasad
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Vehicle Dynamics ◽  
Tracking Control ◽  
Sliding Mode ◽  
Pole Placement ◽  
Control Technique ◽  
Remotely Operated Vehicle ◽  
Point Tracking ◽  
Placement Technique

This paper considers kinematics and dynamics of Remotely Operated Underwater Vehicle (ROV) to control position, orientation and velocity of the vehicle. Cascade control technique has been applied in this paper. The pole placement technique is used in inner loop of kinematics to stabilize the vehicle motions. Model Predictive control is proposed and applied in outer loop of vehicle dynamics to maintain position and velocity trajectories of ROV. Simulation results carried out on ROV shows the good performance and stability are achieved by using MPC algorithm, whereas sliding mode control loses its stability when ocean currents are high. Implementation of proposed MPC algorithm and stabilization of vehicle motions is the main contribution in this paper.

scholarly journals Analysis of the Influence of Suspension Actuator Limitations on Ride Comfort in Passenger Cars Using Model Predictive Control

Actuators ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 77 ◽  
Author(s):  
Erik Enders ◽  
Georg Burkhard ◽  
Nathan Munzinger
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Ride Comfort ◽  
Rate Of Change ◽  
High Rate ◽  
Passenger Cars ◽  
Wheel Load ◽  
Country Road ◽  
Necessary Constraints ◽  
Vehicle Dynamics Control

Active suspension systems help to deliver superior ride comfort and can be used to resolve the objective conflict between ride comfort and road-holding. Currently, there exists no method for analyzing the influence of actuator limitations, such as maximum force and maximum rate of change, on the achievable ride comfort. This research paper presents a method that is capable of doing this. It uses model predictive control to eliminate the influence of feedback controller performance and to integrate both actuator limitations and necessary constraints on dynamic wheel-load variation and suspension travel. Various scenarios are simulated, such as driving over a speed bump and inner city driving, as well as driving on a country road and motorway driving, using a state-of-the-art quarter-car model, parameterized for a luxury class vehicle. It is analyzed how comfort, or in one scenario road-holding, can be improved with consideration for the actuator limitations. The results indicate that actuator rate limitation has a strong influence on vertical vehicle dynamics control system performance, and that relatively small maximum forces of around 1000 to 2000 N are sufficient to successfully reject disturbances from road irregularities, provided the actuator is capable of supplying the forces at a sufficiently high rate of change.

The Role of Model Fidelity in Model Predictive Control Based Hazard Avoidance in Unmanned Ground Vehicles Using LIDAR Sensors

2013 ◽  
Author(s):  
Jiechao Liu ◽  
Paramsothy Jayakumar ◽  
James L. Overholt ◽  
Jeffrey L. Stein ◽  
Tulga Ersal
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Vehicle Dynamics ◽  
High Fidelity ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Model Fidelity ◽  
Hazard Avoidance ◽  
The Impact

Unmanned ground vehicles (UGVs) are gaining importance and finding increased utility in both military and commercial applications. Although earlier UGV platforms were typically exclusively small ground robots, recent efforts started targeting passenger vehicle and larger size platforms. Due to their size and speed, these platforms have significantly different dynamics than small robots, and therefore the existing hazard avoidance algorithms, which were developed for small robots, may not deliver the desired performance. The goal of this paper is to present the first steps towards a model predictive control (MPC) based hazard avoidance algorithm for large UGVs that accounts for the vehicle dynamics through high fidelity models and uses only local information about the environment as provided by the onboard sensors. Specifically, the paper presents the MPC formulation for hazard avoidance using a light detection and ranging (LIDAR) sensor and applies it to a case study to investigate the impact of model fidelity on the performance of the algorithm, where performance is measured mainly by the time to reach the target point. Towards this end, the case study compares a 2 degrees-of-freedom (DoF) vehicle dynamics representation to a 14 DoF representation as the model used in MPC. The results show that the 2 DoF model can perform comparable to the 14 DoF model if the safe steering range is established using the 14 DoF model rather than the 2 DoF model itself. The conclusion is that high fidelity models are needed to push autonomous vehicles to their limits to increase their performance, but simulating the high fidelity models online within the MPC may not be as critical as using them to establish the safe control input limits.

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