A Nonlinear H-infinity Control Approach for Autonomous Truck and Trailer Systems

G. Rigatos, P. Siano, P. Wira, K. Busawon, R. Binns
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引用次数: 7

Abstract

A nonlinear optimal control method is developed for autonomous truck and trailer systems. Actually, two cases are distinguished: (a) a truck and trailer system that is steered by the front wheels of its truck, (b) an autonomous fire-truck robot that is steered by both the front wheels of its truck and by the rear wheels of its trailer. The kinematic model of the autonomous vehicles undergoes linearization through Taylor series expansion. The linearization is computed at a temporary operating point that is defined at each time instant by the present value of the state vector and the last value of the control inputs vector. The linearization is based on the computation of Jacobian matrices. The modeling error due to approximate linearization is considered to be a perturbation that is compensated by the robustness of the control scheme. For the approximately linearized model of the autonomous vehicles an H-infinity feedback controller is designed. This requires the solution of an algebraic Riccati equation at each iteration of the control algorithm. The stability of the control loop is confirmed through Lyapunov analysis. It is shown that the control loop exhibits the H-infinity tracking performance which implies elevated robustness against modeling errors and external disturbances. Moreover, under moderate conditions the global asymptotic stability of the control loop is proven. Finally, to implement state estimation-based control for the autonomous vehicles, through the processing of a small number of sensor measurements, the H-infinity Kalman Filter is proposed.
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自动驾驶卡车和挂车系统的非线性h∞控制方法
提出了一种用于自动驾驶卡车和挂车系统的非线性最优控制方法。实际上,有两种情况是不同的:(a)卡车和拖车系统由卡车的前轮驱动,(b)自动消防车机器人由卡车的前轮和拖车的后轮驱动。通过泰勒级数展开对自动驾驶汽车的运动模型进行线性化处理。线性化是在一个临时工作点上计算的,这个临时工作点是在每个时刻由状态向量的现值和控制输入向量的最后值定义的。线性化是基于雅可比矩阵的计算。由于近似线性化引起的建模误差被认为是由控制方案的鲁棒性补偿的扰动。针对自动驾驶汽车的近似线性化模型,设计了h∞反馈控制器。这需要在每次迭代控制算法时解一个代数Riccati方程。通过李亚普诺夫分析证实了控制回路的稳定性。结果表明,控制回路具有h∞跟踪性能,这意味着对建模误差和外部干扰的鲁棒性提高。此外,在中等条件下,证明了控制回路的全局渐近稳定性。最后,为了实现基于状态估计的自动驾驶汽车控制,通过对少量传感器测量数据的处理,提出了h -∞卡尔曼滤波器。
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