Passive Fault-Tolerant Augmented Neural Lyapunov Control: A method to synthesise control functions for marine vehicles affected by actuators faults

Abstract

Closed-loop stability of control systems can be undermined by actuator faults. Redundant actuator sets and Fault-Tolerant Control (FTC) strategies can be exploited to enhance system resiliency to loss of actuator efficiency, complete failures or jamming. Passive FTC methods entail designing a fixed-gain control law that can preserve the stability of the closed-loop system when faults occur, by compromising on the performance of the faultless system. The use of Passive FTC methods is of particular interest in the case of underwater autonomous platforms, where the use of extensive sensoring to monitor the status of the actuator is limited by strict space and energy constraints. In this work, a machine learning-based method is formulated to systematically synthesise control laws for systems affected by actuator faults, encompassing partial and total loss of actuator efficiency and control surfaces jamming. Differently from other methods in this category, the closed-loop stability is formally certified. The learning architecture encompasses two Artificial Neural Networks, one representing the control law, and the other resembling a Control Lyapunov Function (CLF). Periodically, a Satisfiability Modulo Theory solver is employed to verify that the synthesised CLF formally satisfies the theoretical Lyapunov conditions associated to both the nominal and faulty dynamics. The method is applied to three marine test cases: first, an Autonomous Underwater Vehicle performing planar motion and subjected to full loss of actuator efficiency is investigated. Next, a study is conducted on a hybrid Underwater Glider with a pair of independent twin stern planes jamming at a fixed position. Finally, partial loss of effectiveness is considered. In all three scenarios, the system is able to synthesise stabilising control laws with performance degradation prescribed by the user. Unlike other machine-learning based techniques, this method offers formal stability certificates and relies on limited computational resources rendering it possible to be run on unassuming office laptops. An open-source software tool is developed and released at: https://github.com/grande-dev/pFT-ANLC.