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Implicit dual control based on particle filtering and forward dynamic programming
International Journal of Adaptive Control and Signal Processing ( IF 3.1 ) Pub Date : 2008-01-01 , DOI: 10.1002/acs.1094
David S Bayard 1 , Alan Schumitzky
Affiliation  

This paper develops a sampling-based approach to implicit dual control. Implicit dual control methods synthesize stochastic control policies by systematically approximating the stochastic dynamic programming equations of Bellman, in contrast to explicit dual control methods that artificially induce probing into the control law by modifying the cost function to include a term that rewards learning. The proposed implicit dual control approach is novel in that it combines a particle filter with a policy-iteration method for forward dynamic programming. The integration of the two methods provides a complete sampling-based approach to the problem. Implementation of the approach is simplified by making use of a specific architecture denoted as an H-block. Practical suggestions are given for reducing computational loads within the H-block for real-time applications. As an example, the method is applied to the control of a stochastic pendulum model having unknown mass, length, initial position and velocity, and unknown sign of its dc gain. Simulation results indicate that active controllers based on the described method can systematically improve closed-loop performance with respect to other more common stochastic control approaches.

中文翻译:

基于粒子滤波和前向动态规划的隐式对偶控制

本文开发了一种基于采样的隐式双重控制方法。隐式双控制方法通过系统地逼近 Bellman 的随机动态规划方程来合成随机控制策略,而显式双控制方法通过修改成本函数以包括奖励学习的项来人为地诱导对控制律的探索。所提出的隐式双重控制方法的新颖之处在于它将粒子滤波器与前向动态规划的策略迭代方法相结合。这两种方法的集成提供了一个完整的基于抽样的方法来解决这个问题。通过使用表示为 H 块的特定架构,该方法的实现得以简化。为减少实时应用程序的 H 块内的计算负载给出了实用建议。例如,该方法应用于控制质量、长度、初始位置和速度以及直流增益符号未知的随机摆模型。仿真结果表明,相对于其他更常见的随机控制方法,基于所描述方法的主动控制器可以系统地提高闭环性能。
更新日期:2008-01-01
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