Trajectory Tracking Control Optimization with Neural Network for Autonomous Vehicles
Trajectory Tracking Control Optimization with Neural Network for Autonomous Vehicles
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DOI：
10.25046/aj040121
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
S. Bamgbose;Lijun Li
中科院分区：
文献类型：
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作者：
S. Bamgbose;Lijun Li
对于任务关键型和时间敏感型的自主车辆导航，控制器设计必须在收敛到参考指令的速度和稳态精度方面表现出优异的跟踪性能。在这篇文章中，提出了一种新的设计集成的神经网络与传统的控制系统，自适应地获得优化的控制器参数，从而提高瞬态和稳态性能的运动和位置控制的自主车辆。通过优化基于李雅普诺夫函数的非线性控制器，将所提出的智能控制方案应用于移动的机器人的八字形轨迹导航。最后，以二自由度直升机的俯仰角和偏航角为对象，基于所提出的控制策略优化了基于线性二次型调节器的控制器。仿真结果表明，该方案在收敛到期望轨迹的速度和总体误差最小化方面优于传统控制器。
For mission-critical and time-sensitive navigation of autonomous vehicles, controller design must exhibit excellent tracking performance with respect to the speed of convergence to reference command and steady-state accuracy. In this article, a novel design integration of the neural network with the traditional control system is proposed to adaptively obtain optimized controller parameters resulting in improved transient and steady-state performance of motion and position control of autonomous vehicles. Application of the proposed intelligent control scheme to mobile robot navigation was presented for an eight-shaped trajectory by optimizing a Lyapunov-based nonlinear controller. Furthermore, a Linear Quadratic Regulator-based controller was optimized based on the proposed strategy to control the pitch and yaw angles of a 2-Degree-of –Freedom helicopter. The simulation results showed that the proposed scheme outperforms the traditional controllers in terms of the speed of convergence to the desired trajectory and overall error minimization.