双侧独立电驱动履带车辆反馈线性化解耦与预测行驶控制

针对双侧独立电驱动履带车辆行驶动力学耦合和跟踪优化的控制问题，提出一种解耦和预测控制方法。在车辆仿射非线性模型基础上，通过反馈线性化消除系统动力学耦合因素，实现纵向车速和横摆角速度的解耦控制；对解耦后的速度和横摆角速度子系统分别设计自适应广义预测控制算法，采用递推最小二乘法辨识受控自回归积分滑动平均模型的参数以修正控制律，并结合电机输出能力对系统控制量和输出量进行约束，实现目标速度和横摆角速度的跟踪优化；依托某型电驱动履带样车开展实车试验。结果表明，所设计的算法能够快速准确跟踪目标速度和横摆角速度，控制量输出平滑，抗扰性能强，可实现车辆多种工况下的稳定行驶。

Feedback Linearization Decoupling and Predictive Driving Control for Dual Independent Electric Drive Tracked Vehicle

A decoupling and predictive control method is proposed for the control problem of driving dynamic coupling and tracking optimization of dual independent electric drive tracked vehicle. Based on the vehicle affine nonlinear model, the decoupling control of longitudinal speed and yaw rate is realized by feedback linearization to eliminate the coupling factors of the system dynamics. On this basis, the adaptive generalized predictive control (GPC) algorithms are designed for the longitudinal speed subsystem and yaw rate subsystem after decoupling, respectively. In the GPC algorithm, the recursive least squares method is used to identify the parameters of the controlled autoregressive integrated moving average model to correct the control law, and constrain the system control and output to achieve the tracking optimization of target speed and yaw rate based on the motor output capability. A series of experiments were conducted for an electric drive tracked vehicle prototype. The experimental results show that the designed algorithm can be used to track the target speed and yaw rate quickly and accurately, the output of the control variable is smooth, and the anti-interference performance is strong, which realizes stable driving of vehicle under various working conditions.