scholarly journals How Imitation Learning and Human Factors Can Be Combined in a Model Predictive Control Algorithm for Adaptive Motion Planning and Control

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4012
Author(s):  
Milad Karimshoushtari ◽  
Carlo Novara ◽  
Fabio Tango
Keyword(s):  
Model Predictive Control ◽  
Motion Planning ◽  
Predictive Control ◽  
Urban Areas ◽  
Control Algorithm ◽  
Transportation Systems ◽  
Imitation Learning ◽  
Planning And Control ◽  
Computation Efficiency ◽  
And Control

Interest in autonomous vehicles (AVs) has significantly increased in recent years, but despite the huge research efforts carried out in the field of intelligent transportation systems (ITSs), several technological challenges must still be addressed before AVs can be extensively deployed in any environment. In this context, one of the key technological enablers is represented by the motion-planning and control system, with the aim of guaranteeing the occupants comfort and safety. In this paper, a trajectory-planning and control algorithm is developed based on a Model Predictive Control (MPC) approach that is able to work in different road scenarios (such as urban areas and motorways). This MPC is designed considering imitation-learning from a specific dataset (from real-world overtaking maneuver data), with the aim of getting human-like behavior. The algorithm is used to generate optimal trajectories and control the vehicle dynamics. Simulations and Hardware-In-the-Loop tests are carried out to demonstrate the effectiveness and computation efficiency of the proposed approach.

scholarly journals Plant and Controller Optimization for Power and Energy Systems With Model Predictive Control

2021 ◽  
Vol 143 (8) ◽  
Author(s):  
Donald J. Docimo ◽  
Ziliang Kang ◽  
Kai A. James ◽  
Andrew G. Alleyne
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Hybrid Electric Vehicle ◽  
Performance Metrics ◽  
Control Strategies ◽  
Transportation Systems ◽  
Thermal Dynamics ◽  
Plant Optimization ◽  
Power And Energy Systems ◽  
And Control

Abstract This article explores the optimization of plant characteristics and controller parameters for electrified mobility. Electrification of mobile transportation systems, such as automobiles and aircraft, presents the ability to improve key performance metrics such as efficiency and cost. However, the strong bidirectional coupling between electrical and thermal dynamics within new components creates integration challenges, increasing component degradation, and reducing performance. Diminishing these issues requires novel plant designs and control strategies. The electrified mobility literature provides prior studies on plant and controller optimization, known as control co-design (CCD). A void within these studies is the lack of model predictive control (MPC), recognized to manage multi-domain dynamics for electrified systems, within CCD frameworks. This article addresses this through three contributions. First, a thermo-electromechanical hybrid electric vehicle (HEV) powertrain model is developed that is suitable for both plant optimization and MPC. Second, simultaneous plant and controller optimization is performed for this multi-domain system. Third, MPC is integrated within a CCD framework using the candidate HEV powertrain model. Results indicate that optimizing both the plant and MPC parameters simultaneously can reduce physical component sizes by over 60% and key performance metric errors by over 50%.

scholarly journals Aplicación de Muestreo basado en Modelos de Control Predictivo a un Vehículo Autónomo Subacuático

2010 ◽  
Vol 4 (7) ◽  
pp. 55
Author(s):  
Charmane V. Caldwell ◽  
Damion D. Dunlap ◽  
Emmanuel G. Collins
Keyword(s):  
Model Predictive Control ◽  
Motion Planning ◽  
Predictive Control ◽  
Planning Process ◽  
Autonomous Underwater Vehicle ◽  
Kinematic Model ◽  
Local Minima ◽  
Cluttered Environments ◽  
Unmanned Underwater Vehicles ◽  
Planning And Control

Unmanned Underwater Vehicles (UUVs) can be utilized to perform difficult tasks in cluttered environments such as harbor and port protection. However, since UUVs have nonlinear and highly coupled dynamics, motion planning and control can be difficult when completing complex tasks. Introducing models into the motion planning process can produce paths the vehicle can feasibly traverse. As a result, Sampling-Based Model Predictive Control (SBMPC) is proposed to simultaneously generate control inputs and system trajectories for an autonomous underwater vehicle (AUV). The algorithm combines the benefits of sampling-based motion planning with model predictive control (MPC) while avoiding some of the major pitfalls facing both traditional sampling-based planning algorithms and traditional MPC. The method is based on sampling (i.e., discretizing) the input space at each sample period and implementing a goal-directed optimization (e.g., A*) in place of standard numerical optimization. This formulation of MPC readily applies to nonlinear systems and avoids the local minima which can cause a vehicle to become immobilized behind obstacles. The SBMPC algorithm is applied to an AUV in a 2D cluttered environment and an AUV in a common local minima problem. The algorithm is then used on a full kinematic model to demonstrate the benefits.

scholarly journals Simultaneous Realization of Decision, Planning and Control for Lane-Changing Behavior Using Nonlinear Model Predictive Control

2020 ◽  
Vol E103.D (12) ◽  
pp. 2632-2642
Author(s):  
Hiroyuki OKUDA ◽  
Nobuto SUGIE ◽  
Tatsuya SUZUKI ◽  
Kentaro HARAGUCHI ◽  
Zibo KANG
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Nonlinear Model Predictive Control ◽  
Lane Changing ◽  
Planning And Control ◽  
And Control

Model predictive control—Based distributed control algorithm for bidirectional interlinking converter in hybrid microgrids

2021 ◽  
Author(s):  
Syed Umaid Ali ◽  
Muhammad Aamir ◽  
Atif Raza Jafri ◽  
Umashankar Subramaniam ◽  
Faheem Haroon ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Distributed Control ◽  
Predictive Control ◽  
Control Algorithm ◽  
Distributed Control Algorithm

Model predictive control-based control algorithm for a target-chaser maneuvering situation

2021 ◽  
pp. 1-12
Author(s):  
A. S. M. Gilimalage ◽  
S. Kimura
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Algorithm
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