The accurate prediction of smooth steering inputs is crucial for autonomous
vehicle applications because control actions with jitter might cause the
vehicle system to become unstable. To address this problem in automobile
lane-keeping control without the use of additional smoothing algorithms, we
developed 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 state
and control barrier functions of the soft-CILQR solver to soften the
constraints in the optimization process so that stabilizing control inputs can
be calculated in a relatively simple manner. Two types of automotive
lane-keeping experiments were conducted with a linear system dynamics model to
test the performance of the proposed soft-CILQR algorithm and to compare its
performance with that of the CILQR algorithm: numerical simulations and
experiments involving challenging vision-based maneuvers. In the numerical
simulations, the soft-CILQR and CILQR solvers managed to drive the system
toward the reference state asymptotically; however, the soft-CILQR solver
obtained smooth steering input trajectories more easily than did the CILQR
solver under conditions involving additive disturbances. In the experiments
with visual inputs, the soft-CILQR controller outperformed the CILQR controller
in terms of tracking accuracy and steering smoothness during the driving of an
ego vehicle on TORCS.

通过软约束迭代线性二次调节器算法结合模型预测控制约束松弛方法，开发了一种在汽车车道保持控制中解决抖动问题的算法，通过软化优化过程中的约束，能够相对简单地计算稳定的控制输入轨迹，提高自动驾驶车辆的操控平滑度和追踪精度。