This paper considers the problem of real-time control and learning in dynamic systems subjected to parametric uncertainties. A combination of Adaptive Control (AC) in the inner loop and a Reinforcement Learning (RL) based policy in the outer loop is proposed such that in real-time the inner-loop AC contracts the closed-loop dynamics towards a reference system, and as the contraction takes hold, the RL in the outerloop directs the overall system towards optimal performance. Two classes of nonlinear dynamic systems are considered, both of which are control-affine. The first class of dynamic systems utilizes equilibrium points with expansion forms around these points and employs a Lyapunov approach while second class of nonlinear systems uses contraction theory. AC-RL controllers are proposed for both classes of systems and shown to lead to online policies that guarantee stability using a high-order tuner and accommodate parametric uncertainties and magnitude limits on the input. In addition to establishing a stability guarantee with real-time control, the AC-RL controller is also shown to lead to parameter learning with persistent excitation for the first class of systems. Numerical validations of all algorithms are carried out using a quadrotor landing task on a moving platform. These results point out the clear advantage of the proposed integrative AC-RL approach.
翻译:本文探讨了在具有参数不确定性的动态系统中实时控制和学习的问题。提出了内环适应控制(AC)和外环强化学习(RL)政策相结合的建议,以便在外环适应控制(AC)和外环强化学习(RL)政策相结合的情况下,内环AC将闭环动态合同到参照系统,随着收缩的进行,外环系统RL将整个系统引导到最佳性能,两种非线性动态系统类别都属于控制装置。第一类动态系统利用这些点周围扩展表的平衡点,并采用Lyapunov方法,而第二类非线性系统则使用收缩理论。提议在两个系统类别中采用AC-RL控制器,并显示其导致在线政策,保证使用高序调控器实现稳定性,并满足投入的准不确定性和规模限制。除了建立实时控制的稳定保证之外,A-R-R控制器还显示,这两类动态系统利用这些扩展表的平衡点使用L级系统持续解析点,而使用L级系统第一级系统持续解析法,这些A-R-R-R-R-Simplical Ex所有Aslaling 的清晰定位工具利用了所有Axlal的移动结果。