Experimental Validation of L1 Adaptive Control: Rohrs' Counterexample in Flight

The paper presents new results on the verification and in-flight validation of an L1 adaptive flight control system, and proposes a general methodology for verification and validation of adaptive flight control algorithms. The proposed framework is based on Rohrs counterexample, a benchmark problem presented in the early 80s to show the limitations of adaptive controllers developed at that time. In this paper, the framework is used to evaluate the performance and robustness characteristics of an L1 adaptive control augmentation loop implemented onboard a small unmanned aerial vehicle. Hardware-in-the-loop simulations and flight test results confirm the ability of the L1 adaptive controller to maintain stability and predictable performance of the closed loop adaptive system in the presence of general (artificially injected) unmodeled dynamics. The results demonstrate the advantages of L1 adaptive control as a verifiable robust adaptive control architecture with the potential of reducing flight control design costs and facilitating the transition of adaptive control into advanced flight control systems

Sensor-based motion planning for autonomous vehicle teams

This paper employs a computational optimal control framework to develop a mission planning tool for a team of heterogeneous unmanned vehicles conducting a nominal mine countermeasures (MCM) mission. We first describe our motivation for developing vehicle-specific sensor models for unmanned surface and underwater vehicles working collaboratively to detect mines. Next, we describe the sonar detection models used to evaluate the performance of a long-range, forward-looking detection sonar and a high-resolution sidescan sonar deployed from these unmanned vehicles. Results from multiple computer simulations which highlight the flexibility and utility of this solution framework are presented.Consortium for Robotics and Unmanned Systems Education and Research (CRUSER

Observability Options Against an Adversarial Swarm - a Quantitative Analysis

CRUSER TechCon 2018 Research at NPS. Tuesday 1: SwarmingIn this presentation we address the problem of detecting internal cooperating strategies of an adversarial swarm by estimating a set of parameters that define a particular swarm cooperating strategy. This is a nonstandard estimation problem the estimation problem we address in this paper is not to estimate, for example, position and velocity of each member of the swarm; rather we are interested in understanding how individual agents cooperate to achieve the swarm behavior that is observed by outsiders. For a non-cooperative, adversarial swarm, this estimation problem produces unique challenges. The dynamic, evolving configuration of the swarm over time can lead to time periods where observation is effective for estimation, or it can lead to times such as when a swarm has stabilized into an equilibrium configuration when some internal strategies may be unobservable. The interactive nature of a swarm also makes relevant the impact of the observer on the swarm itself. For a swarm which reacts to obstacles or other agents, the observer may impact the movements of the swarm. This provides the opportunity for a dynamic observer to act not just as a passive data collector, but as a possible agent provocateur, provoking the swarm into more revealing behaviors. In this presentation, we explore tools for this problem using a model-based approach. We adopt a swarm model developed by Leonard et al. where authors propose an algorithm for controlling a swarm based on a potential function and virtual leaders. The potential function and virtual leaders employed can be characterized by a set of parameters. The estimation problem is then examined for the estimation of these parameters. Prior to actually designing an estimator a natural question is whether these parameters are in fact observable. The answer to this question is surprisingly nontrivial. Using the well-established notion of unobservability index we show that these parameters are indeed observable. However, in order to achieve observability the adversarial swarm must be disrupted by an intruder. In the presence of the intruder the unobservability index is shown to be good, i.e., the parameters in question are observable. Another non-trivial aspect of the problem is estimation of the parameters. Many swarm models involve parameters that represent the range limit of communication and/or range of influence among agents. These parameters introduce discontinuity into the swarm dynamics making the design of a convergent estimation algorithm very challenging. When applying standard filtering techniques such as unscented Kalman filter (UKF) to actually estimate these parameters we have discovered that the estimates often fail to converge although the parameters have shown to be observable

Observability Options Against an Adversarial Swarm - a Quantitative Analysis [video]

CRUSER TechCon 2018 Research at NPSIn this presentation we address the problem of detecting internal cooperating strategies of an adversarial swarm by estimating a set of parameters that define a particular swarm cooperating strategy. This is a nonstandard estimation problem the estimation problem we address in this paper is not to estimate, for example, position and velocity of each member of the swarm; rather we are interested in understanding how individual agents cooperate to achieve the swarm behavior that is observed by outsiders. For a non-cooperative, adversarial swarm, this estimation problem produces unique challenges. The dynamic, evolving configuration of the swarm over time can lead to time periods where observation is effective for estimation, or it can lead to times such as when a swarm has stabilized into an equilibrium configuration when some internal strategies may be unobservable. The interactive nature of a swarm also makes relevant the impact of the observer on the swarm itself. For a swarm which reacts to obstacles or other agents, the observer may impact the movements of the swarm. This provides the opportunity for a dynamic observer to act not just as a passive data collector, but as a possible agent provocateur, provoking the swarm into more revealing behaviors. In this presentation, we explore tools for this problem using a model-based approach. We adopt a swarm model developed by Leonard et al. where authors propose an algorithm for controlling a swarm based on a potential function and virtual leaders. The potential function and virtual leaders employed can be characterized by a set of parameters. The estimation problem is then examined for the estimation of these parameters. Prior to actually designing an estimator a natural question is whether these parameters are in fact observable. The answer to this question is surprisingly nontrivial. Using the well-established notion of unobservability index we show that these parameters are indeed observable. However, in order to achieve observability the adversarial swarm must be disrupted by an intruder. In the presence of the intruder the unobservability index is shown to be good, i.e., the parameters in question are observable. Another non-trivial aspect of the problem is estimation of the parameters. Many swarm models involve parameters that represent the range limit of communication and/or range of influence among agents. These parameters introduce discontinuity into the swarm dynamics making the design of a convergent estimation algorithm very challenging. When applying standard filtering techniques such as unscented Kalman filter (UKF) to actually estimate these parameters we have discovered that the estimates often fail to converge although the parameters have shown to be observable