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The accurate prediction of smooth steering inputs is crucial for autonomousvehicle applications because control actions with jitter might cause thevehicle system to become unstable. To address this problem in automobilelane-keeping control without the use of additional smoothing algorithms, wedeveloped a soft-constrained iterative linear-quadratic regulator (soft-CILQR)algorithm by integrating CILQR algorithm and a model predictive control (MPC)constraint relaxation method. We incorporated slack variables into the stateand control barrier functions of the soft-CILQR solver to soften theconstraints in the optimization process so that stabilizing control inputs canbe calculated in a relatively simple manner. Two types of automotivelane-keeping experiments were conducted with a linear system dynamics model totest the performance of the proposed soft-CILQR algorithm and to compare itsperformance with that of the CILQR algorithm: numerical simulations andexperiments involving challenging vision-based maneuvers. In the numericalsimulations, the soft-CILQR and CILQR solvers managed to drive the systemtoward the reference state asymptotically; however, the soft-CILQR solverobtained smooth steering input trajectories more easily than did the CILQRsolver under conditions involving additive disturbances. In the experimentswith visual inputs, the soft-CILQR controller outperformed the CILQR controllerin terms of tracking accuracy and steering smoothness during the driving of anego vehicle on TORCS.

Lane-Keeping Control of Autonomous Vehicles Through a Soft-Constrained Iterative LQR