论文标题
使用未知系统模型堆叠自适应动态编程
Stacked adaptive dynamic programming with unknown system model
论文作者
论文摘要
自适应动态编程是用于多种无限马最佳控制方法的集体术语。所有方法共有的是基于动态编程理念的无限 - 水平成本函数的近似。通常，它们还需要了解系统动力学模型。在当前的工作中，解决了控制器未知的动力学模型的系统。为了实现控制算法，使用卡尔曼滤波器估算了系统动力学的模型。建议提高控制器性能的堆叠控制方案。在模拟中验证了新方法的功能，并将其与以梯度下降为代表的运行成本相比。
Adaptive dynamic programming is a collective term for a variety of approaches to infinite-horizon optimal control. Common to all approaches is approximation of the infinite-horizon cost function based on dynamic programming philosophy. Typically, they also require knowledge of a dynamical model of the system. In the current work, application of adaptive dynamic programming to a system whose dynamical model is unknown to the controller is addressed. In order to realize the control algorithm, a model of the system dynamics is estimated with a Kalman filter. A stacked control scheme to boost the controller performance is suggested. The functioning of the new approach was verified in simulation and compared to the baseline represented by gradient descent on the running cost.
