Dynamic state estimation in distributed aircraft electric control systems via adaptive submodularity

Abstract

Abstract-We consider the problem of estimating the discrete state of an aircraft electric system under a distributed control architecture through active sensing. The main idea is to use a set of controllable switches to reconfigure the system in order to gather more information about the unknown state. By adaptively making a sequence of reconfiguration decisions with uncertain outcome, then correlating measurements and prior information to make the next decision, we aim to reduce the uncertainty. A greedy strategy is developed that maximizes the one-step expected uncertainty reduction. By exploiting recent results on adaptive submodularity, we give theoretical guarantees on the worst-case performance of the greedy strategy. We apply the proposed method in a fault detection scenario where the discrete state captures possible faults in various circuit components. In addition, simple abstraction rules are proposed to alleviate state space explosion and to scale up the strategy. Finally, the efficiency of the proposed method is demonstrated empirically on different circuits.

Weak Adaptive Submodularity and Group-Based Active Diagnosis with Applications to State Estimation with Persistent Sensor Faults

“…PERSISTENT SENSOR FAULTS We are motivated by the discrete state estimation problem of an aircraft electrical system via active sensing, first studied in [10] (see Figure 1 for an example of a simple circuit and the readers are referred to [10] for a detailed description of the electrical circuits). As more systems become more dependent on electric power systems, this problem is crucial to the safety of these systems.…”

“…Despite persistent faults, our goal is similar to that in [10], i.e., to design a policy that can adaptively estimate the discrete state of the circuit by taking "actions" (i.e., opening or closing controllable contactors) and observing the sensor measurements. Note that we are only interested in estimating the unknown discrete state of the system and not the true failure mode of the sensors.…”

“…This problem of adaptive decision-making appears in various applications, both in the context of stochastic control (e.g., in robot navigation [1] and reinforcement learning [2]) and stochastic state estimation (e.g., in information gathering in robotics [3], [4], fault diagnosis in nuclear plants [5] and sensor placement [6], [7]). In particular, the research area of active diagnosis and active learning, i.e., the problem of discrete object or state identification through a minimal number of sequential actions, has many real-world applications in medical diagnosis and emergency response [8], [9], electrical systems diagnosis [10], scheduling [11], etc. Hence, progress in this direction can have a significant impact on a broad range of problems in robotics, cyber-physical systems and artificial intelligence.…”

Approximation Algorithms for Optimization of Combinatorial Dynamical Systems

“…As summarized in Section I, several algorithms have been proposed for minimizing or maximizing a submodular function. Its application includes sensor placement [39], [40], actuator placement (based on the controllability Grammian) [41], network inference [42], dynamic state estimation [43], and leader selection under link noise [44].…”

Time-annotated game graphs for synthesis from abstracted systems

“…For aircraft electric power systems, switching contactors can be used to route power as needs change or faults occur. There are numerous questions that can be formulated in this setting, such as switching to identify faults when there are limited sensors available [8].…”