scholarly journals Method for Switching between Traction and Brake Control for Speed Profile Optimization in Mountainous Situations

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3042 ◽  
Author(s):  
Nan Lin ◽  
Changfu Zong ◽  
Shuming Shi
Keyword(s):  
Objective Function ◽  
Theoretical Foundation ◽  
Fuel Efficiency ◽  
Switching Control ◽  
Intelligent Vehicles ◽  
Speed Profile ◽  
Cruise Control ◽  
Control Laws ◽  
Switch Time ◽  
Profile Optimization

Making full use of front road grade information to achieve the best fuel efficiency is important for intelligent vehicles. Normal theoretical studies pay too much attention to engine continuous feedback control. The theoretical foundation of switching between traction and brake control has been ignored. In mountainous terrain, both the engine and road slopes are energy sources. Switching between traction and brake control is the key point. This research focuses on broadening the normal control range. The comprehensive objective function that contains traction and brake control is built, and then the analytical switching control law is derived based on Pontryagin’s maximum principle (PMP). Analytical switching control laws express the mechanism of switching between traction and brake control for economic cruise control (ECC). Simulation results show that the model can solve the switch time and the entire speed profile precisely. Brake control is very important in downhill situations. The parameters in the objective function influence not only the switch time but also the switch process. This research offers a theoretical foundation for ECC with road slopes and can make onboard control more precise and efficient.

scholarly journals Fuel-Efficient Driving Strategies for Heavy-Duty Vehicles: A Platooning Approach Based on Speed Profile Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Sina Torabi ◽  
Mattias Wahde
Keyword(s):  
Speed Profile ◽  
Fuel Reduction ◽  
Heavy Duty ◽  
Cruise Control ◽  
Fuel Savings ◽  
Percentage Points ◽  
Profile Optimization ◽  
Layer Control ◽  
Lead Vehicle ◽  
Heavy Duty Vehicles

A method for reducing the fuel consumption of a platoon of heavy-duty vehicles (HDVs) is described and evaluated in simulations for homogeneous and heterogeneous platoons. The method, which is based on speed profile optimization and is referred to as P-SPO, was applied to a set of road profiles of 10 km length, resulting in fuel reduction of 15.8% for a homogeneous platoon and between 16.8% and 17.4% for heterogeneous platoons of different mass configurations, relative to the combination of standard cruise control (for the lead vehicle) and adaptive cruise control (for the follower vehicle). In a direct comparison with MPC-based approaches, it was found that P-SPO outperforms the fuel savings of such methods by around 3 percentage points for the entire platoon, in similar settings. In P-SPO, unlike most common platooning approaches, each vehicle within the platoon receives its own optimized speed profile, thus eliminating the intervehicle distance control problem. Moreover, the P-SPO approach requires only a simple vehicle controller, rather than the two-layer control architecture used in MPC-based approaches.

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.

scholarly journals Design and Lyapunov Stability Analysis of a Fuzzy Logic Controller for Autonomous Road Following

2010 ◽  
Vol 2010 ◽  
pp. 1-20 ◽  
Author(s):  
Yi Fu ◽  
Howard Li ◽  
Mary Kaye
Keyword(s):  
Fuzzy Logic ◽  
Stability Analysis ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Rule Base ◽  
Control Laws ◽  
Robotic Vehicle ◽  
Road Following

Autonomous road following is one of the major goals in intelligent vehicle applications. The development of an autonomous road following embedded system for intelligent vehicles is the focus of this paper. A fuzzy logic controller (FLC) is designed for vision-based autonomous road following. The stability analysis of this control system is addressed. Lyapunov's direct method is utilized to formulate a class of control laws that guarantee the convergence of the steering error. Certain requirements for the control laws are presented for designers to choose a suitable rule base for the fuzzy controller in order to make the system stable. Stability of the proposed fuzzy controller is guaranteed theoretically and also demonstrated by simulation studies and experiments. Simulations using the model of the four degree of freedom nonholonomic robotic vehicle are conducted to investigate the performance of the fuzzy controller. The proposed fuzzy controller can achieve the desired steering angle and make the robotic vehicle follow the road successfully. Experiments show that the developed intelligent vehicle is able to follow a mocked road autonomously.

scholarly journals Feedforward Tactical Optimization for Energy-Efficient Operation of Freight Trains: The Swiss Case

2018 ◽  
Vol 2672 (10) ◽  
pp. 278-288 ◽  
Author(s):  
Valerio De Martinis ◽  
Ambra Toletti ◽  
Francesco Corman ◽  
Ulrich A. Weidmann ◽  
Andrew Nash
Keyword(s):  
Energy Efficiency ◽  
Energy Efficient ◽  
Real Data ◽  
Efficient Solutions ◽  
Future Market ◽  
Speed Profile ◽  
Efficient Operation ◽  
The North ◽  
Simulation Based ◽  
Profile Optimization

The optimization of rail operation for improving energy efficiency plays an important role for the current and future market of rail freight services and helps rail compete with other transport modes. This paper presents a feedforward simulation-based model that performs speed profile optimization together with minor rescheduling actions. The model’s purpose is to provide railway operators and infrastructure managers with energy-efficient solutions that are tailored especially for freight trains. This work starts from the assumption that freight train characteristics are completely defined only a few hours before actual departure; therefore, small specific feedforward adjustments that do not affect the surrounding operation can still be considered. The model was tested in a numerical example. The example clearly shows how the optimized solutions can be evaluated with reference to energy saved and robustness within the rail traffic. The evaluation is based on real data from the North–South corridor crossing Switzerland from Germany to Italy.

A Receding Horizon Switching Control Resilient to Communication Failures for Connected Vehicles

2017 ◽  
Author(s):  
Gianluca Savaia ◽  
Zoleikha Abdollahi Biron ◽  
Pierluigi Pisu
Keyword(s):  
Networked Control Systems ◽  
Switching Control ◽  
Connected Vehicles ◽  
Cruise Control ◽  
Control Logic ◽  
Receding Horizon ◽  
Transmission Delays ◽  
Cooperative Adaptive Cruise Control ◽  
Finite Set ◽  
Optimal Control Action

This paper focuses on networked control systems subject to network-induced constraints, namely transmission delays and packet dropping. The proposed framework is based on a switching control logic which selects the optimal control action in a finite set of strategies tailored to a specific scenario. The switching logic relies on a receding horizon optimization — which resembles model predictive control — and does not require any prior knowledge on the condition of the network. This strategy is tested on a platoon of connected vehicles engaged in cooperative adaptive cruise control which communicate over an imperfect DSRC network. The main objective consists in avoiding unsafe scenarios where the network is subject to the aforementioned failures; results show the proposed approach achieves the objective whereas a nominal controller would lead the platoon to crash.

Smooth-Switching Control-Based Cooperative Adaptive Cruise Control by Considering Dynamic Information Flow Topology

2020 ◽  
Vol 2674 (4) ◽  
pp. 444-458
Author(s):  
Anye Zhou ◽  
Siyuan Gong ◽  
Chaojie Wang ◽  
Srinivas Peeta
Keyword(s):  
Information Flow ◽  
Adaptive Cruise Control ◽  
Switching Control ◽  
Cruise Control ◽  
Flow Topology ◽  
Dynamic Information ◽  
String Stability ◽  
Kalman Predictor ◽  
Cooperative Adaptive Cruise Control ◽  
Smooth Switching

Vehicle-to-vehicle communications can be unreliable because of interference and information congestion, which leads to the dynamic information flow topology (IFT) in a platoon of connected and autonomous vehicles. Some existing studies adaptively switch the controller of cooperative adaptive cruise control (CACC) to optimize string stability when IFT varies. However, the difference of transient response between controllers can induce uncomfortable jerks at switching instances, significantly affecting riding comfort and jeopardizing vehicle powertrain. To improve riding comfort while maintaining string stability, the authors introduce a smooth-switching control-based CACC scheme with IFT optimization (CACC-SOIFT) by implementing a bi-layer optimization model and a Kalman predictor. The first optimization layer balances the probability of communication failure and control performance optimally, generating a robust IFT to reduce controller switching. The second optimization layer adjusts the controller parameters to minimize tracking error and the undesired jerk. Further, a Kalman predictor is applied to predict vehicle acceleration if communication failures occur. It is also used to estimate the states of preceding vehicles to suppress the measurement noise and the acceleration disturbance. The effectiveness of the proposed CACC-SOIFT is validated through numerical experiments based on NGSIM field data. Results indicate that the CACC-SOIFT framework can guarantee string stability and riding comfort in the environment of dynamic IFT.

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