Fast Online Computation of a Model Predictive Controller and Its Application to Fuel Economy–Oriented Adaptive Cruise Control

2015 ◽  
Vol 16 (3) ◽  
pp. 1199-1209 ◽  
Author(s):  
Shengbo Eben Li ◽  
Zhenzhong Jia ◽  
Keqiang Li ◽  
Bo Cheng
Keyword(s):  
Fuel Economy ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Model Predictive Controller ◽  
Online Computation ◽  
Predictive Controller

Multi-objective adaptive cruise controller design using nonlinear predictive controller with the objective function with variable weights determined by fuzzy logic controller

2021 ◽  
pp. 095440702110147
Author(s):  
Behzad Samani ◽  
Amir H. Shamekhi
Keyword(s):  
Objective Function ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Hierarchical Control ◽  
Control Input ◽  
Cruise Control ◽  
Model Predictive Controller ◽  
Control Step ◽  
Predictive Controller

In this paper, an adaptive cruise control system with a hierarchical control structure is designed. The upper-level controller is a model predictive controller (MPC) that by minimizing an objective function in the presence of the constraints, calculates the desired acceleration as control input and sends it to the lower-level controller. So the lower-level controller, which is a fuzzy controller, determines the amount of throttle valve opening or brake pressure to get the car to this desired acceleration. The model predictive controller performs optimization at each control step to minimize the objective function and achieve the reference values. Usually, the objective function has predetermined and constant weights to meet objectives such as maintain the driver’s desired speed and increase safety and in some cases increase comfort and reduce fuel consumption. In this paper, it is suggested that instead of using constant weights in the objective function, these weights should be determined by a fuzzy controller, depending on the different conditions in which the car is placed. The simulation results show that the variability of the weights of the objective function achieves control objectives much better than the optimization of the objective function with constant weights.

Online Hybrid Model Predictive Controller Design for Cruise Control of Automobiles

2017 ◽  
Author(s):  
Kaveh Merat ◽  
Jafar Abbaszadeh Chekan ◽  
Hassan Salarieh ◽  
Aria Alasty
Keyword(s):  
Hybrid Model ◽  
Optimization Problem ◽  
Controller Design ◽  
Research Work ◽  
Mixed Integer ◽  
Control Procedure ◽  
Cruise Control ◽  
Model Predictive Controller ◽  
Gear Shift ◽  
Predictive Controller

In the proposed study, a Hybrid Model Predictive Controller is introduced for cruise control of an automobile model. The presented model consists of the engine, the gearbox, and the transmission dynamics, where the aerodynamics force and elastic friction between the tires and road are taken into account. Through Piecewise Linearization of nonlinearities in the system; (torque)-(throttle)-(angular velocity) of engine and (aerodynamic drag force)-(automobile velocity), a comprehensive piecewise linear model for the system is obtained. Then combined with the switch and shift between engaged gears in gearbox, the Piecewise Affine (PWA) model for the vehicle dynamics is acquired. As far as the control design is concerned, the cruise control problem for tracking a desired speed fashion is addressed by a MPC-based controller design. The proposed control approach is based on the online model predictive control, applied on the obtained PWA dynamics. The highlighted novelties of the presented research work are summarized as: first a more complete model is examined due to the consideration of a realistic model for engine. This improvement makes the polyhedron regions of the PWA system dependent to both state variable (i.e., velocity) and input signals (i.e., throttle and engaged gear) which brings the complexity to the design of control procedure. Second, due to the switch in the dynamics and dependence of our PWA model to discrete input (gear shift), the desperate need to solve the optimization problem through mixed integer programming, which needs high computation effort specially for our system, seems inevitable. We triumph over this challenge through introducing “possible gear shift scenario” sets. Hence, by constraining the optimization problem to the introduced logical sets, the problem still remains convex optimization type and the computation volume is reduced. In addition, we hired branch and bound method which allowed us to have large problems to be solved in a tractable amount of time and computation resources. At last, some simulations are presented to exhibit the performance of the proposed method.

Mobility and Energy Consumption Impacts of Cooperative Adaptive Cruise Control Vehicle Strings on Freeway Corridors

2020 ◽  
Vol 2674 (9) ◽  
pp. 111-123
Author(s):  
Hao Liu ◽  
Xiao-Yun Lu ◽  
Steven E. Shladover
Keyword(s):  
Energy Consumption ◽  
Fuel Economy ◽  
Adaptive Cruise Control ◽  
Baseline Scenario ◽  
Vehicle Speed ◽  
Market Penetration ◽  
Cruise Control ◽  
Cooperative Adaptive Cruise Control ◽  
Freeway Corridors ◽  
The Impact

Cooperative adaptive cruise control (CACC) vehicle string operations have the potential to improve significantly the mobility and energy consumption performance of congested freeway corridors. This study examines the impact of CACC string operations on vehicle speed and fuel economy on the 13-mi SR-99 corridor, near Sacramento, CA. It extends the existing body of knowledge by performing a multi-scenario simulation analysis of the freeway corridor. A simulation study evaluated the performance of the corridor under various CACC market penetration scenarios and traffic demand inputs. The CACC string operation was also analyzed when vehicle awareness device (VAD) and CACC managed lane (ML) strategies were implemented. The case study revealed that the average vehicle speed increased by 70% when the CACC market penetration increased from 0% to 100%. The highest average fuel economy, expressed in miles per gallon (mpg), was achieved under the 50% CACC scenario where mpg was 27. This was 10% higher than the baseline scenario. However, when the CACC market penetration was 50% or higher, the vehicle fuel efficiency only had minor increases. When CACC market penetration reached 100%, the corridor allowed 30% more traffic to enter the network without experiencing reduced average speed. Results also indicate that the VAD strategy increased the speed by 8% when the CACC market penetration was 20% or 40%, while there was a minor decrease in mpg. The ML strategy decreased the corridor performance when implemented alone.

scholarly journals A New Hybrid Model Predictive Controller Design for Adaptive Cruise of Autonomous Electric Vehicles

2021 ◽  
Vol 2021 ◽  
pp. 1-25
Author(s):  
Yuanhang Chen ◽  
Guodong Feng ◽  
Shaofang Wu ◽  
Xiaojun Tan
Keyword(s):  
Hybrid Model ◽  
Controller Design ◽  
Autonomous Driving ◽  
Cruise Control ◽  
Model Predictive Controller ◽  
Energy Feedback ◽  
Lateral Dynamics ◽  
Computation Efficiency ◽  
Predictive Controller ◽  
Autonomous Electric Vehicles

Autonomous driving is an appealing research topic for integrating advanced intelligent algorithms to transform automotive industries and human commuting. This paper focuses on a hybrid model predictive controller (MPC) design for an adaptive cruise. The driving modes are divided into following and cruising, and the MPC algorithm based on simplified dual neural network (SDNN) and proportional-integral-derivative (PID) based on single neuron (SN) are applied to the following mode and the cruising mode, respectively. SDNN is used to accelerate the solution of the quadratic programming (QP) problem of the proposed MPC algorithm to improve the computation efficiency, while PID based on SN performs well in the nonlinear and time-varying conditions in the ACC system. Moreover, lateral dynamics control is integrated into the designed system to fulfill cruise control in the curved road conditions. Furthermore, to improve the energy efficiency of the electric vehicle, an energy feedback strategy is proposed. The simulation results show that the proposed ACC system is effective on both straight roads and curved roads.

Evaluation and Testing of Driver-Assistive Truck Platooning: Phase 2 Final Results

2017 ◽  
Vol 2615 (1) ◽  
pp. 11-18 ◽  
Author(s):  
Richard Bishop ◽  
David Bevly ◽  
Luke Humphreys ◽  
Stephen Boyd ◽  
Daniel Murray
Keyword(s):  
Fuel Economy ◽  
Short Range ◽  
Adaptive Cruise Control ◽  
Phase 1 ◽  
Phase 2 ◽  
Cruise Control ◽  
Dedicated Short Range Communications ◽  
Cooperative Adaptive Cruise Control ◽  
Transportation Research ◽  
The Impact

Phase 2 final results are described for the FHWA Exploratory Advanced Research project titled Heavy Truck Cooperative Adaptive Cruise Control: Evaluation, Testing, and Stakeholder Engagement for Near Term Deployment, which evaluates the commercial feasibility of driver-assistive truck platooning (DATP). The project was led by Auburn University, in partnership with Peloton Technology, Peterbilt Trucks, Meritor WABCO, and the American Transportation Research Institute. DATP is a form of cooperative adaptive cruise control for heavy trucks (two-truck platoons). It takes advantage of the increasing maturity of vehicle-to-vehicle (V2V) communications (and the expected widespread deployment of V2V connectivity based on dedicated short-range communications over the next decade) to improve freight efficiency, fleet efficiency, safety, and highway mobility as well as reduce emissions. Notably, truck fleets can implement DATP regardless of the regulatory timeline for dedicated short-range communications. The Phase 2 analysis built on Phase 1 and included a testing program of a DATP prototype (with detailed SAE Type 2 fuel economy testing), wireless communications optimization, traffic modeling to understand the impact on roadways at various levels of market penetration, and additional analysis of methods to find DATP partners as well as aerodynamic simulations to understand drag on the vehicles. Detailed analysis of the fuel economy testing data is provided.

scholarly journals PERPENDICULAR TURNING SOLAR TRACKER ANTI-COLLISION CAR

2021 ◽  
Vol 5 (9) ◽  
Author(s):  
Sujash Dhole ◽  
Satyam Mehrotra
Keyword(s):  
Social Networking ◽  
Fuel Economy ◽  
Adaptive Cruise Control ◽  
Azimuth Angle ◽  
Cruise Control ◽  
Hard Part ◽  
Driving Skills ◽  
Collision Mitigation ◽  
Solar Tracker ◽  
Turning Radius

"We could definitely make a flying car - but that's not the hard part. The hard part is, how do you make a flying car that is super safe and quiet?” - ELON MUSK While safety, reliability, fuel economy, and low running costs put them at the top of the list of what people consider to be the 'most important' in a car, more than a third (36%) of those tested online rank with the latest driving skills in the same fields. Driving technology includes steering or parking assist, adaptive cruise control, and wireless entry or ignition. More than a quarter (28%) of people online also account for having the latest passenger technology, which includes audio or video streaming and social networking, as 'most important' to them. Our team has developed a vehicle archetype which is incorporated of Azimuth Angle, trimming off the turning radius and anti-collision mitigation system like concepts with a single touch button. The underlying concept of the smart car is to free the driver from many of the mundane tasks associated with driving, making the act of driving more pleasant.

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