Open AccessMotion control of self-driving vehicles on rough roads
Author(s) -
Igor Shapovalov,
Salimzhan Gafurov,
Alexandr Klimchik
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/707/1/012008
Subject(s) - control theory (sociology) , pid controller , controller (irrigation) , position (finance) , motion (physics) , trajectory , control (management) , engineering , model predictive control , path (computing) , motion control , computer science , control engineering , robot , artificial intelligence , economics , astronomy , biology , programming language , temperature control , agronomy , physics , finance
Control of self-driving vehicles is one of the-up-to-date tasks. Vehicles are non-linear dynamic systems with a large number of parameters. When it is supposed to move on a rough road with a lot of abrupt turns, significant unpredictable and unmeasurable disturbances emerge and the control quality degrades significantly. We implemented model predictive path integral (MPPI) control to deal with significant unmeasured disturbances. To approve efficiency of the proposed controller, we conducted simulation of complex car motion on a plane under the control of PID, and MPPI controllers. Performance of controllers is comparatively equal in stable modes, when reference position and velocity do not change. MPPI controllers outperform PID ones in unstable modes due to their ability to predict a vehicle behavior. Also, MPPI is the best choice for the rejection of significant external disturbances.