Model Predictive Control-based Engine Idle Speed Regulation with Various Coordinated Controls Using an Instantaneous Engine Model

2021 ◽  
Vol 14 (4) ◽  
Author(s):  
Shubham Janbandhu ◽  
Somnath Sengupta ◽  
Siddhartha Mukhopadhyay ◽  
Prasanta Sarkar
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Idle Speed ◽  
Engine Model ◽  
Speed Regulation

Nonlinear model predictive control of a discrete-cycle gasoline-controlled auto ignition engine model: Simulative analysis

2019 ◽  
Vol 20 (10) ◽  
pp. 1025-1036 ◽  
Author(s):  
Eugen Nuss ◽  
Maximilian Wick ◽  
Jakob Andert ◽  
Jochem De Schutter ◽  
Moritz Diehl ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Measurement Data ◽  
Nonlinear Model Predictive Control ◽  
Test Bed ◽  
Efficient Operation ◽  
Stable Combustion ◽  
Engine Model ◽  
Auto Ignition

Gasoline-controlled auto ignition is a promising technology capable of reducing both fuel consumption and emissions at the same time. There are, however, challenges to overcome in order to make practical use of it. One area of research addresses methods that guarantee stable combustion as gasoline-controlled auto ignition is very sensitive to disturbances. This article investigates the capability of nonlinear model predictive control to ensure stable combustion while maintaining efficient operation. For this purpose, a suitable gasoline-controlled auto ignition model is selected and identified using measurement data of a single-cylinder test bed. Building upon this model, a controller based on nonlinear model predictive control is derived and analyzed by means of simulation. The investigation shows that the control manages to follow prescribed set points, also for late combustion, and indicates promising results with respect to real-time computation constraints.

A novel approach to disturbance rejection in idle speed control towards reduced idle fuel consumption

2003 ◽  
Vol 217 (8) ◽  
pp. 677-690 ◽  
Author(s):  
C Manzie ◽  
H C Watson
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Multiple Input Multiple Output ◽  
Speed Control ◽  
Idle Speed ◽  
Novel Approach ◽  
Idle Speed Control ◽  
Bypass Valve ◽  
Input Multiple Output ◽  
Air Valve

Idle speed control remains one of the most challenging problems in the automotive control field owing to its multiple-input, multiple-output structure and the step nature of the disturbances applied. In this paper a simulation model is described for a 4.0 l production engine at idle which includes the standard bypass air valve and spark advance dynamics, as well as the e ects of operating point on cycle-by-cycle combustion-generated torque variations. A model predictive control scheme is then developed for the idle bypass valve and spark advance. The idle speed control algorithm is based on rejecting the torque disturbance using model predictive control for the bypass valve duty cycle while minimizing the transient e ects of the disturbance by adjusting the spark advance. Simulation results are presented to demonstrate the effects of different elements of the controller such as levels of spark offset from minimum spark advance for best torque at idle and feedforward load previews. Compensation of the effects of cyclic variation in combustion torque is also implemented in the controller and its benefits are discussed.

A Model Predictive Control Strategy for Lateral and Longitudinal Dynamics in Autonomous Driving

2020 ◽  
Author(s):  
Irfan Khan ◽  
Stefano Feraco ◽  
Angelo Bonfitto ◽  
Nicola Amati
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Autonomous Driving ◽  
Maximum Speed ◽  
Reference Trajectory ◽  
Vehicle Speed ◽  
The Road ◽  
Speed Regulation ◽  
Road Geometry

Abstract This paper presents a controller dedicated to the lateral and longitudinal vehicle dynamics control for autonomous driving. The proposed strategy exploits a Model Predictive Control strategy to perform lateral guidance and speed regulation. To this end, the algorithm controls the steering angle and the throttle and brake pedals for minimizing the vehicle’s lateral deviation and relative yaw angle with respect to the reference trajectory, while the vehicle speed is controlled to drive at the maximum acceptable longitudinal speed considering the adherence and legal speed limits. The technique exploits data computed by a simulated camera mounted on the top of the vehicle while moving in different driving scenarios. The longitudinal control strategy is based on a reference speed generator, which computes the maximum speed considering the road geometry and lateral motion of the vehicle at the same time. The proposed controller is tested in highway, interurban and urban driving scenarios to check the performance of the proposed method in different driving environments.

Engine idle speed control using nonlinear multiparametric model predictive control

2020 ◽  
Vol 41 (3) ◽  
pp. 960-979
Author(s):  
Amir‐Mohammad Shamekhi ◽  
Amir Taghavipour ◽  
Amir H. Shamekhi
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Speed Control ◽  
Idle Speed ◽  
Idle Speed Control

Comparison of Adaptive Control Techniques Applied to Diesel Engine Idle Speed Regulation

2002 ◽  
Vol 124 (4) ◽  
pp. 682-688 ◽  
Author(s):  
Douglas W. Memering ◽  
Peter H. Meckl
Keyword(s):  
Diesel Engine ◽  
Adaptive Controller ◽  
Minimum Variance ◽  
Recursive Least Squares ◽  
Pole Placement ◽  
Good Convergence ◽  
Idle Speed ◽  
Engine Model ◽  
Speed Regulation ◽  
Self Tuning

Two self-tuning adaptive algorithms are developed for a heavy-duty diesel engine in order to tune the idle governor to the specific parameters of a given engine. Engine parameters typically vary across engines and over time, thus causing potentially detrimental effects on engine idle speed performance. Self-tuning controllers determine the specific parameters of a given engine, and then adjust the controller algorithm accordingly. Recursive least squares is used to do the parameter identification, whose samples are synchronized with the discrete injection events of the diesel engine for good convergence. Both Minimum Variance and Pole Placement Self-Tuning Regulators are developed and simulated on the nonlinear diesel engine model. The results show successful tuning of each adaptive controller to the specific parameters of a given engine model, with parameter convergence occurring within 30 seconds.

Sign in / Sign up

Export Citation Format

Share Document