Adaptive Nonlinear Model Predictive Cruise Controller: Trailer Tow Use Case

Šantin, Ondřej; Beran, Jaroslav; Pekař, Jaroslav; Michelini, John; Jing, Junbo; Szwabowski, Steve; Filev, Dimitar

doi:10.4271/2017-01-0090

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…Liu et al 22 studied the reduction in fuel consumption in cruise control systems. Santin et al 23 designed the cruise control system for the trailer tow and aimed to reduce fuel consumption. In papers [1][2][3][4]6,[10][11][12][14][15][16][17][21][22][23] comfort objective was not considered and the objective function coefficients were assumed to be constant.…”

Section: Introductionmentioning

confidence: 99%

“…Santin et al 23 designed the cruise control system for the trailer tow and aimed to reduce fuel consumption. In papers [1][2][3][4]6,[10][11][12][14][15][16][17][21][22][23] comfort objective was not considered and the objective function coefficients were assumed to be constant. Also in papers 5,[7][8][9]13,19,20 the objective function coefficients were assumed to be constant.…”

Section: Introductionmentioning

confidence: 99%

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

Samani

Shamekhi

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Samani

Shamekhi

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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show abstract

“…Additionally, implementing DP or PMP for an online ecodriving strategy is challenging because of the computational burden of DP or the co-state sensitive characteristics of PMP. The more practical method of MPC was used in [10], and [11]. Here, adaptive nonlinear MPC was utilized, and it was implemented in a vehicle with a standard production powertrain control module.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Reinforcement Learning for Eco-Driving Control of Electric Vehicles

2020

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“…During a real-world test on a sport utility vehicle (SUV), ANLMPC improved fuel economy up to 2.4% on average compared to a production cruise controller with the same time of arrival. The works of References [14,15] incorporated quadratic programming to handle nonlinearity in improving fuel economy while cruising. Since these nonlinear MPC (NMPC) is difficult to design and can take a lot of CPU power, an approach to train a deep neural network (DNN) controller architecture on data from well-designed NMPC was proposed [16].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Vehicle Fuel Economy Optimization with Deep Reinforcement Learning

et al. 2020

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The ever-increasing number of vehicles on the road puts pressure on car manufacturers to make their car fuel-efficient. With autonomous vehicles, we can find new strategies to optimize fuels. We propose a reinforcement learning algorithm that trains deep neural networks to generate a fuel-efficient velocity profile for autonomous vehicles given road altitude information for the planned trip. Using a highly accurate industry-accepted fuel economy simulation program, we train our deep neural network model. We developed a technique for adapting the heterogeneous simulation program on top of an open-source deep learning framework, and reduced dimension of the problem output with suitable parameterization to train the neural network much faster. The learned model combined with reinforcement learning-based strategy generation effectively generated the velocity profile so that autonomous vehicles can follow to control itself in a fuel efficient way. We evaluate our algorithm’s performance using the fuel economy simulation program for various altitude profiles. We also demonstrate that our method can teach neural networks to generate useful strategies to increase fuel economy even on unseen roads. Our method improved fuel economy by 8% compared to a simple grid search approach.

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Adaptive Nonlinear Model Predictive Cruise Controller: Trailer Tow Use Case

Cited by 9 publications

References 13 publications

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

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

Model-Based Reinforcement Learning for Eco-Driving Control of Electric Vehicles

Autonomous Vehicle Fuel Economy Optimization with Deep Reinforcement Learning

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