Neuro-Optimal Control for Discrete Stochastic Processes via a Novel Policy Iteration Algorithm

Liang, Mingming; Wang, Ding; Liu, Derong

doi:10.1109/tsmc.2019.2907991

Cited by 14 publications

(1 citation statement)

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“…The existence of a stabilizing optimal controller is guaranteed under some mild assumptions on the system dynamics and the performance index [25], [26]. The optimal control found by optimizing (3), however, does not guarantee robust stability of the system due to the disturbance.…”

Section: A Optimal Control Design Frameworkmentioning

confidence: 99%

A Satisficing Control Design Framework with Safety and Performance Guarantees for Constrained Systems under Disturbances

Han,

Modares

2020

Preprint

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This paper presents a safe robust policy iteration (SR-PI) algorithm to design controllers with satisficing (good enough) performance and safety guarantee. This is in contrast to standard PI-based control design methods with no safety certification. It also moves away from existing safe control design approaches that perform pointwise optimization and are thus myopic. Safety assurance requires satisfying a control barrier function (CBF), which might be in conflict with the performancedriven Lyapunov solution to the Bellman equation arising in each iteration of the PI. Therefore, a new development is required to robustly certify the safety of an improved policy at each iteration of the PI. The proposed SR-PI algorithm unifies performance guarantee (provided by a Bellman inequality) with safety guarantee (provided by a robust CBF) at each iteration. The Bellman inequality resembles the satisficing decision making framework and parameterizes the sacrifice on the performance with an aspiration level when there is a conflict with safety. This aspiration level is optimized at each iteration to minimize the sacrifice on the performance. It is shown that the presented satisficing control policies obtained at each iteration of the SR-PI guarantees robust safety and performance. Robust stability is also guaranteed when there is no conflict with safety. Sum of squares (SOS) program is employed to implement the proposed SR-PI algorithm iteratively. Finally, numerical simulations are carried out to illustrate the proposed satisficing control framework.

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Section: A Optimal Control Design Frameworkmentioning

confidence: 99%