整车控制器(vehicle control unit,VCU)作为无人驾驶方程式赛车的重要组成部分,直接影响赛车的稳定性、灵敏性和控制性。为满足中国大学生无人驾驶方程式大赛(Formula Student Autonomous China,FSAC)规则要求,分析了赛车状态机需求,以M...整车控制器(vehicle control unit,VCU)作为无人驾驶方程式赛车的重要组成部分,直接影响赛车的稳定性、灵敏性和控制性。为满足中国大学生无人驾驶方程式大赛(Formula Student Autonomous China,FSAC)规则要求,分析了赛车状态机需求,以MC9S12XET256单片机为主控芯片,基于控制器局域网络(controller area network,CAN)通信技术,设计了整车控制器软件系统,针对模数转换(analog to digital converter,A/D)采样技术、轮速测量技术及通信技术3个功能模块进行详细阐述,并进行实验。实验测试证明,所设计的控制器能够较好地完成无人驾驶方程式赛车的控制任务。展开更多
Double cost function linear quadratic regulator (DLQR) is developed from LQR theory to solve an optimal control problem with a general nonlinear cost function. In addition to the traditional LQ cost function, anothe...Double cost function linear quadratic regulator (DLQR) is developed from LQR theory to solve an optimal control problem with a general nonlinear cost function. In addition to the traditional LQ cost function, another free form cost function was introduced to express the physical need plainly and optimize weights of LQ cost function using the search algorithms. As an instance, DLQR was applied in determining the control input in the front steering angle compensation control (FSAC) model for heavy duty vehicles. The brief simulations show that DLQR is powerful enough to specify the engineering requirements correctly and balance many factors effectively. The concept and applicable field of LQR are expanded by DLQR to optimize the system with a free form cost function.展开更多
文摘整车控制器(vehicle control unit,VCU)作为无人驾驶方程式赛车的重要组成部分,直接影响赛车的稳定性、灵敏性和控制性。为满足中国大学生无人驾驶方程式大赛(Formula Student Autonomous China,FSAC)规则要求,分析了赛车状态机需求,以MC9S12XET256单片机为主控芯片,基于控制器局域网络(controller area network,CAN)通信技术,设计了整车控制器软件系统,针对模数转换(analog to digital converter,A/D)采样技术、轮速测量技术及通信技术3个功能模块进行详细阐述,并进行实验。实验测试证明,所设计的控制器能够较好地完成无人驾驶方程式赛车的控制任务。
文摘Double cost function linear quadratic regulator (DLQR) is developed from LQR theory to solve an optimal control problem with a general nonlinear cost function. In addition to the traditional LQ cost function, another free form cost function was introduced to express the physical need plainly and optimize weights of LQ cost function using the search algorithms. As an instance, DLQR was applied in determining the control input in the front steering angle compensation control (FSAC) model for heavy duty vehicles. The brief simulations show that DLQR is powerful enough to specify the engineering requirements correctly and balance many factors effectively. The concept and applicable field of LQR are expanded by DLQR to optimize the system with a free form cost function.
